In this paper, the observer-based quasi-consensus control problem is investigated for a class of discrete-time multi-agent systems subject to time-varying delay. To improve communication quality and extend transmission distance, a stochastic communication protocol and an amplify-and-forward relay mechanism are incorporated. During the process of signal amplification and transmission in the AaF relay, stochastic packet loss is considered, which introduces additional complexity into the system analysis. The main objective is to design a distributed observer-based control strategy capable of handling time-varying delays, stochastic scheduling governed by a Markov chain, and random packet dropouts. Sufficient conditions are derived to guarantee the achievement of quasi-consensus in probability among the agents. These conditions are formulated in terms of matrix inequalities through which the required gain matrices are computed. A simulation example is provided to validate the effectiveness and robustness of the proposed control approach under realistic communication constraints.

The Internet of Things (IoT) increasingly relies on large-scale networked agents that cooperate over bandwidth-limited and unreliable wireless links. This paper studies the finite-horizonH∞consensus control problem for a class of discrete-time IoT-enabled multi-agent systems subject to random parameters, stochastic communication scheduling, and relay-assisted transmissions. To reflect practical IoT communication constraints, a novel transmission framework is considered that integrates a stochastic communication protocol with a decode-and-forward relay mechanism under random packet losses. Within this framework, only a subset of sensing information is scheduled for transmission, encoded, decoded, and forwarded by relay nodes to improve communication reliability and coverage. Based on the resulting networked system model, a distributed consensus controller is designed to attenuate the effects of disturbances and communication uncertainties over a finite time horizon. Sufficient conditions guaranteeing finite-horizonH∞consensus are derived in terms of recursive linear matrix inequalities, and a corresponding controller synthesis algorithm is developed. Simulation results illustrate the effectiveness of the proposed approach in achieving robust consensus for IoT-enabled multi-agent systems operating over stochastic relay-assisted communication networks.

This paper addresses the robust model predictive control problem for a class of networked control systems with polytopic uncertainties and hard constraints, where the controller design is complicated by the joint presence of an energy harvesting sensor in the forward channel and the round-robin protocol in the backward channel. In such a setting, stochastic transmission behavior caused by random energy availability, together with fixed communication scheduling and immeasurable states, makes it difficult to guarantee recursive feasibility of the online optimization and mean-square stability of the closed-loop system. To capture these features, the mathematical expectation of a quadratic function depending on both the sensor energy level and the transmission order over an infinite horizon is constructed to formulate the optimization problem. In order to cope with the terminal constraint set and the immeasurability of system states, an auxiliary optimization problem with guaranteed solvability is developed by employing inequality analysis and slack-matrix techniques, through which a sub-optimal solution is obtained. Furthermore, sufficient conditions are derived to ensure the recursive feasibility of the proposed algorithm and the mean-square stability of the resulting closed-loop system with and without hard constraints. Finally, a simulation example is provided to demonstrate the effectiveness of the proposed method.

Large-scale Internet of Things (IoT) systems are characterized by massive numbers of interconnected devices, heterogeneous dynamics, and complex interaction structures, which can be effectively modeled using complex networks. In many IoT applications, secure cluster synchronization is essential for coordinated and reliable operation, yet it is highly vulnerable to cyber-attacks, particularly replay attacks that maliciously reuse previously transmitted but valid data. This paper investigates the secure cluster synchronization problem for discrete-time nonlinear complex networks representing IoT systems under stochastic replay attacks. A probabilistic replay attack model with bounded consecutive attack duration is introduced to capture the random and intermittent characteristics of realistic attack behaviors. To mitigate the adverse impact of replayed information, a PID-based cluster synchronization control strategy is developed, where proportional, integral, and derivative actions are jointly exploited to enhance robustness against outdated and compromised signals. By constructing an appropriate Lyapunov functional and employing stochastic analysis techniques, sufficient conditions are derived to guarantee asymptotic mean-square cluster synchronization. A systematic controller synthesis procedure is further provided. Numerical simulations demonstrate the effectiveness and improved resilience of the proposed approach compared with conventional proportional control schemes.

This paper addresses the regional control problem for networked systems under simultaneous actuator amplitude and rate constraints. Communication protocols are employed to manage signal transmission over the constrained network. Under the round-robin and try-once-discard protocols, sufficient conditions are derived, expressed as nonlinear matrix inequalities, to ensure the boundedness, H∞performance, and asymptotic stability of the closed-loop systems. Some algorithms are proposed to optimize the performance indices under linear matrix inequality constraints. Two numerical examples illustrate the validity of the obtained results. Unlike earlier studies that focus mainly on communication protocols or amplitude constraints alone, this paper explicitly incorporates actuator rate constraints and systematically designs static feedback gains rather than assuming them known. As a result, the proposed method yields a less conservative estimate of the domain of attraction even under amplitude-only constraints, while maintaining simpler controller implementation compared to dynamic output-feedback strategies.

In the context of Internet of Things (IoT)-enabled wireless multi-sensor networks, efficient and reliable remote state estimation is often challenged by resource constraints and unreliable communication links. This paper investigates a coding-decoding-based state estimation problem for discrete-time dynamical systems subject to external disturbances and packet losses. Specifically, the influence of packet length, which is a critical design parameter constrained by practical IoT protocols, is studied in terms of its dual impact on quantization error and transmission reliability over lossy wireless channels. An ultimately bounded filtering approach is adopted to develop a packet-length-aware estimator, and a comprehensive analysis is conducted to quantify how packet length affects estimation accuracy under joint quantization and dropout effects. To optimize estimation performance, a co-design framework is proposed for selecting both the estimator gains and packet lengths, formulated as a mixed-integer nonlinear programming problem. This optimization is solved via a hybrid method combining particle swarm optimization and linear matrix inequality techniques. Simulation results validate the effectiveness and practicality of the proposed approach for real-world IoT monitoring and control scenarios.

Pedestrian detection plays a critical role in intelligent perception systems in autonomous vehicles, which directly influences the reliability and safety of the overall system. Advanced in-vehicle sensor technology has enabled the continuous evolution of pedestrian detection systems by leveraging heterogeneous multimodal inputs such as RGB, infrared, depth, Light Detection And Ranging, and event data. Nevertheless, establishing a robust pedestrian detection system that is capable of integrating and processing such heterogeneous multimodal data effectively remains a significant challenge. At the same time, growing concerns about data privacy among automobile manufacturers have hindered further advances in detection model performance by restricting the sharing of private data within the industry. In this paper, a novel personalised federated learning framework, Kolmogorov-Arnold network-based Dual Expert Transformer Heterogeneous Personalized Federated Learning (KDET-HPFL), is proposed for multimodal pedestrian detection. To be specific, the KDET pedestrian detector is developed based on an expert feature selection module (which is designed to adaptively choose essential features from multimodal data) and a Group-Rational Kolmogorov-Arnold Network module, which enhances the feature extraction capabilities and improves the detection performance effectively. The HPFL framework is proposed for data privacy protection on heterogeneous multimodal data, where a cross-client aggregation (CCA) method is put forward by integrating different aggregation methods for certain layers in the KDET detector. With CCA, the HPFL framework achieves personalised feature retention of multimodal data pairs on multiple clients and improved model aggregation effect for each client. Experimental findings reveal that the proposed KDET-HPFL framework outperforms some existing personalised federated learning frameworks for pedestrian detection on four public datasets (i.e., LLVIP, STCrowd, InOutDoor, and EventPed) with mAP scores of 73.74%, 75.39%, 66.14%, and 79.57%, respectively.

This paper investigates the problem ofl2-l∞proportional-integral state estimation for stochastic nonlinear systems over decode-and-forward relay networks subject to packet losses. The system model is formulated to capture the effects of stochastic nonlinearities and state saturations, thereby reflecting practical engineering conditions. To alleviate transmission distance limitations, a decode-and-forward relay is introduced into the wireless channel to support communication from the sensor to the remote estimator, which enhances the reliability of long-distance data transmission. The objective is to design a proportional-integral state estimator that guarantees a prescribed finite-horizonl2-l∞performance level for the estimation error dynamics, despite the presence of stochastic nonlinearities, state saturations, packet losses, and decoding errors. A sufficient condition for the existence of such an estimator is established, and the corresponding estimator gains are obtained by solving a set of matrix equalities. Finally, a simulation example is provided to demonstrate the effectiveness of the proposed state estimation scheme.

This paper addresses the distributed recursive state estimation problem for wireless sensor networks operating under stringent energy, bandwidth, and computational constraints. A compress-and-forward relay architecture integrated with compressed sensing is proposed to reduce the transmission burden while preserving the information required for reliable estimation. Within the proposed framework, sensor measurements are compressed at the relay and reconstructed at the remote estimator from low-dimensional observations. To account for the uncertainty induced by wireless transmission, the effects of channel fading and channel noise on the compressed-sensing reconstruction process are analysed, and a corresponding reconstruction error model is established by exploiting channel statistical characteristics. On this basis, a distributed recursive state estimator is developed to mitigate channel-induced distortion. An upper bound on the estimation error covariance is then derived in the presence of reconstruction error, and the estimator gains are determined by minimizing this bound. Simulation studies based on an unmanned surface vehicle mooring-assisted dynamic positioning system demonstrate that the proposed method can maintain satisfactory estimation accuracy and robustness while significantly reducing communication overhead.

This paper is concerned with the asynchronous sampled-data state estimation problems for a class of continuous-time nonlinear complex networks. A novel asynchronous sampled-data estimator is constructed with matrix exponential gains to estimate the states of network nodes, which allows each node to independently sample and transmit the measured signals at its own designated time instants. It is demonstrated that the utilization of matrix exponential gains is capable of enlarging the maximum-allowable bound of the sampling intervals. Moreover, a modified Halanay-type inequality is derived to facilitate the analysis of estimation errors. Accordingly, by leveraging the Lyapunov stability theory, some sufficient conditions are obtained to guarantee the global exponential stability of the estimation error dynamics. In addition, the maximum-allowable bound of the sampling intervals is explicitly characterized by resorting to an algebraic inequality, and a convex optimization method is adopted with the aim of maximizing such an allowable bound. Finally, some numerical simulations are conducted to validate the feasibility and usefulness of the established theoretical results.

In this article, theH∞fuzzy control problem is investigated for a class of nonlinear systems subject to replay attacks with frequency-duration constraints. Owing to the vulnerability of the open shared communication network, the information transmitted from the sensor to the controller may be exposed to replay attackers. A novel yet comprehensive replay attack model is constructed to characterize the repeated replay behavior of the adversary. On the basis of the constructed model, a fuzzy controller is designed to guarantee asymptotic stability and the desiredH∞performance. By employing Lyapunov stability theory and the orthogonal decomposition technique, sufficient conditions are derived to ensure the existence of the desired controller parameter. Finally, simulation results are presented to verify the effectiveness and correctness of the proposed fuzzy controller for T-S fuzzy systems under replay attacks.

Networked multi-sensor systems operating under the FlexRay protocol (FRP) are widely used in automotive industry, where reliable state estimation under bounded uncertainties is of fundamental importance. In such systems, the measurement information from multiple sensors is transmitted to the estimator through a network governed by the FRP, which induces scheduling constraints and switching behaviors in the estimation process. These characteristics make it challenging to guarantee accuracy and boundedness of the state estimates using conventional methods. This paper investigates the zonotopic set-membership fusion estimation (SMFE) problem for multi-sensor systems under the FRP. The research objective is to design a parallel fusion estimation algorithm for the transformed switched system, to establish a sufficient condition guaranteeing the ultimate boundedness of the radii of the resulting zonotopes, and to improve the transient estimation performance. An SMFE algorithm is proposed to recursively calculate the zonotopes that constrain the system state by exploiting the properties of zonotopes. A sufficient condition is derived to ensure the ultimate boundedness of the output zonotopes’ radii, which explicitly takes into account both the scheduling of the FRP and the adverse effect of zonotope order reduction on estimation performance. Furthermore, a matrix-inequality-based method is developed to construct an additional enclosing zonotope, based on which a tighter zonotope is obtained at each time instant to enhance the transient performance. The efficacy of the proposed SMFE method is demonstrated through two simulation experiments.

Semi-supervised learning can leverage both labeled and unlabeled samples simultaneously to improve performance. However, existing methods often present the following issues: (1) The emphasis of learning is put on either the similarity structures or the regression losses of data, neglecting the interaction between them. (2) The similarity structures among boundary samples might be unreliable, which misleads label propagation and impairs the performance of models on out-of-sample data. (3) They often involve the inverses of high-order matrices, making them inefficient in computation. To overcome these issues, we propose a scalable semi-supervised learning framework with Discriminative Label Propagation and Correction (DLPC), which collaboratively exploits the regression losses and similarity structures of data. Particularly, each sample is projected onto the independent class labels associated with nonnegative adjustment vectors rather than the propagated labels, such that the distances between samples from different classes are naturally enlarged, making regression losses more effective for boundary samples. Benefiting from this, the regression losses can guide the propagation of labels in boundary areas. Thus, the label information is first propagated through dynamically optimized graph structures and then corrected by the regression losses, effectively improving the quality of labels and facilitating feature projection learning. Furthermore, an accelerated solution has been developed to reduce the computational costs of DLPC on sample scales, thereby making it scalable to relatively large-scale problems. Moreover, the proposed DLPC can not only be applied to single-view scenarios but also extended to multi-view tasks. Additionally, an optimization strategy with fast convergence has been presented for DLPC, and extensive experiments demonstrate the effectiveness and superiority of DLPC over state-of-the-art competitors.

With increasing wind power penetration, the inherent uncertainty of wind power poses significant challenges to dispatch decisions in power systems. To address this issue, this paper proposes a two-stage distributionally robust chance-constrained (TDRC) model for the unit commitment problem with wind power uncertainty. In this model, an ambiguity confidence set is developed to characterise wind power uncertainty with unknown probability distributions, and wind power curtailment and load shedding levels are modelled as chance constraints to balance wind power uncertainty and system security of dispatch decisions. A hybrid parallel solution (HPS) is proposed for efficient computation by integrating Benders decomposition (BD) and column-and-constraint generation (C&CG) methods. Case studies on the IEEE 24- and 118-bus systems demonstrate the rationality of the proposed approach, while experiments on a practical 126-bus system using the cyber-physical power system (CPPS) dispatch platform further validate the effectiveness and practical applicability of the proposed TDRC model.

This paper is concerned with the adaptive dynamic event-triggered (DET)H∞control for a class of networked nonlinear systems with non-uniform sampling under the Takagi-Sugeno (T-S) fuzzy model. To address the problems of high energy consumption in sensors and the high occupancy rate of limited bandwidth, an improved adaptive DET communication mechanism with non-uniform sampling is proposed, in which the interval dynamic variable is constructed with an adaptive law. Unlike existing works, an online iterative asynchronous premise reconstruction (APR) technique is devised to tackle the challenges caused by the mismatch of premise variables. Based on the adaptive DET mechanism and the online iterative APR method, switched-like fuzzy controllers are designed, which can be switched at different triggering instances. Furthermore, to ensure stability and achieve the desired control performance, new triggering instants-dependent Lyapunov functions are construct- ed in accordance with the idea of event interval partitioning. Fi- nally, the practicability of the proposed strategy is demonstrated using two practical examples.

Pedestrian trajectory prediction is an important research area with significant applications in autonomous driving and intelligent surveillance. However, existing studies on pedestrian trajectory prediction often suffer from a noticeable discrepancy between predicted and actual trajectories, due to incomplete extraction of pedestrian trajectory features and the randomness of the pedestrian walking process. The key objective of this article is to address this issue by proposing a method that can reasonably simulate the randomness of pedestrian walking and comprehensively extract pedestrian trajectory features. To achieve this, a novel social informer model built upon the informer model is proposed in this article. The social informer utilizes a transformer encoder-based interaction module to comprehensively extract pedestrian trajectory features, which are input into the informer model for further processing. Additionally, an adaptive variance mechanism is proposed to determine the optimal variance and accurately simulate the random nature of pedestrian walking. Finally, the proposed model is evaluated in a comparative experiment on ETH and UCY datasets, with results demonstrating that the proposed model outperforms other models, exhibiting improved accuracy and performance.

In this article, the encoding-decoding-based state estimation problem is investigated for a class of continuous-time nonlinear complex networks (CNs) subject to communication bandwidth constraints. Based on the sampled outputs from a subset of network nodes, a novel dynamic event-driven encoding mechanism is integrated into the design of state estimator, where a time-varying auxiliary parameter is utilized to modulate the triggering condition in a dynamical fashion, enabling the event detector to decide whether the data packet should be released at the periodic sampling instants. Specifically, when the dynamic triggering condition is satisfied, the data are first encoded into a codeword and subsequently transmitted to the estimator through a digital communication channel. The Zeno behavior can be naturally prevented due to the periodic feature of the proposed event detector. By leveraging the Lyapunov theory and the matrix inequality techniques, sufficient conditions are established to ensure the exponential stability of the estimation error system. In addition, a convex optimization approach is employed to design the estimator gain with the goal of maximizing the allowable bound of the sampling intervals. Finally, an illustrative example and a practical example involving a three-area power system are provided to showcase the effectiveness of the proposed state estimation method.

In this article, the multisensor particle filtering problem is investigated for a class of nonlinear complex networks with multirate heterogeneous measurements. The underlying complex networks are subject to non-Gaussian noises and randomly switching couplings, while the multirate heterogeneous measurements (including fast-rate binary measurements and slow-rate integral measurements) are transmitted to remote filters via imperfect wireless communication channels. Both the deterministic and stochastic channel gains, along with possible transmission failures, are taken into account to characterize the properties of wireless communication channels. The purpose of this article is to propose a channel-related filtering scheme in the particle filtering framework to address these engineering-oriented complexities. To achieve this, a mixture distribution is established to reflect the effects of randomly switching couplings and generate new particle candidates. By utilizing the Monte Carlo approximation method, two types of update expressions for importance weights are explicitly derived based on the channel properties and the likelihood functions. Finally, numerical simulations are presented to demonstrate the viability and effectiveness of the proposed particle filtering algorithms.

This article addresses the integration of model-driven and data-driven approaches for robust hybrid-driven state estimation under limited data and model uncertainties. An unsupervised hybrid estimation framework, termed adaptive model-driven and data-driven (AMD), is proposed. AMD employs an adaptive cross-coupled prior mechanism within the Bayesian inference paradigm to integrate prior information. A two-stage fusion strategy is introduced: an initial hard fusion of model pseudomeasurements and data-driven priors, followed by an adaptive soft fusion that adjusts model influence based on reconstruction discrepancies, thereby enhancing robustness to imperfect model priors. To capture complex nonlinear transition dynamics, a dynamic bilinear recurrent module has been developed, tailored to the system's underlying behavior. The AMD framework adopts a nonidentical training-testing strategy and an unsupervised hybrid learning objective inspired by the information bottleneck principle, enabling accurate parameter learning without access to ground-truth states. Extensive experiments on multiple nonlinear chaotic systems have demonstrated that AMD consistently achieves competitive or superior estimation accuracy compared to state-of-the-art model-based and hybrid approaches, particularly under underdetermined estimation, model mismatch, and dynamic disturbances. These results demonstrate AMD's capability to effectively leverage limited information through complementary fusion, thereby enhancing both data representation and model robustness. This adaptability positions AMD as a powerful solution for challenging state estimation problems.

This article is concerned with the recursive neural network (NN)-based state estimation problem for a class of stochastic discrete time-varying systems subjected to both unknown nonlinear dynamics and the token bucket communication protocol. The token bucket protocol is utilized to determine whether the sensor signal is granted access to the network at each transmission instant, wherein the transmission may fail due to an insufficient number of tokens in the bucket. The objective of the addressed problem is to design a recursive NN-based state estimator such that, under the influence of the unknown nonlinear dynamics and the token bucket communication protocol, certain upper bounds of both the state estimation error covariance and the NN-weight (NNW) error covariance are guaranteed, while the explicit expressions of the NN-based estimator gain and the NN tuning parameters are derived. By employing two sets of matrix difference equations, two upper bounds for the state estimation error covariance and the NNW error covariance are established, and these upper bounds are subsequently minimized by parameterizing the NN-based estimator gain in terms of the solutions to the matrix difference equations. Finally, an illustrative example is provided to demonstrate the feasibility and effectiveness of the proposed estimation approach.

This article is concerned with the recursive state estimation issue for a class of nonlinear cyber-physical systems (CPSs) with token bucket protocols (TBPs) subject to sensor failures and false data injection (FDI) attacks. In the system under consideration, measurement signals are transmitted to the remote estimator only when there are sufficient tokens in the bucket to meet the token consumption. During network transmissions, the signals are exposed to FDI attacks, which occur randomly and follow a Bernoulli distribution. The primary objective is to develop a state estimation algorithm that can handle the TBP, sensor failures, and FDI attacks simultaneously. Initially, the upper bound of the estimation error covariance is derived using an intensive stochastic technique and the induction approach. Subsequently, the desired estimator gains are recursively computed to minimize this upper bound. Finally, an example is presented to demonstrate the effectiveness of the proposed estimation scheme.

This article is concerned with the zonotopic set-membership fusion estimation (SMFE) problem for a class of complex networks (CNs). The measurements of the CNs are transmitted to a remote fusion center through a shared communication network. Due to the limited network bandwidth, the transmissions of the measurement information occur intermittently, and the nodes' transmission intervals may exceed their sampling periods. To enhance the utilization of the measurement information, each node of the CN is equipped with a buffer for real-time data storage, so that the fusion center can utilize more measurement information at time instants when the node's transmission interval is larger than its sampling period. The aim of this article is to design SMFE algorithms based on both the parallel fusion scheme and the data-compression fusion scheme, respectively, using the data received at the fusion center. First, by iterating the state equation of the CN, a batch processing method is proposed to process the input data of the fusion center concurrently. Subsequently, by employing the zonotopic set-membership estimation (SME) technique, the desired SMFE algorithms are designed. Moreover, sufficient criteria are established to ensure that the sizes of the output zonotopes of the SMFE algorithms remain uniformly bounded. Finally, two numerical examples are presented to illustrate the effectiveness of the proposed algorithms.

This paper addresses the problem of distributed state estimation for smooth nonlinear systems over sensor networks by means of a generalized fuzzy proportional-integral observer (PIO). A sensor network is employed to collect system measurements, with a pull-type gossip protocol governing the intermittent data exchange among neighboring nodes. Under the gossip protocol, each sensor node randomly selects one neighbor to request data, facilitating distributed information updating. Furthermore, considering challenges such as long-distance communication and complex environmental conditions, signal transmission is subject to amplitude fading. To accommodate the characteristics of the gossip protocol, a generalized fuzzy PIO with a flexible structure is developed. Sufficient conditions are derived to guarantee the$H\_{\infty }$estimation performance of the proposed observer. Based on established conditions, the parameters of both the gossip protocol and the fuzzy PIO are co-designed via a particle-swarm-optimization-based iterative algorithm, with emphasis on enhancing observer robustness. Finally, an engineering-oriented simulation example is presented to illustrate the effectiveness of the proposed methodology.

The key management faces significant challenges in terms of efficiency, scalability, and quantum resistance in smart grid environments. This paper addresses these challenges by proposing a novel mutual authentication and key agreement (AKA) protocol for the Noisy Intermediate-Scale Quantum (NISQ) era. The protocol integrates quantum and classical information streams, enabling secure key agreement without third-party reliance. For the first time, quantum-based mutual identity authentication is applied to smart grid security, with a comprehensive analysis of resilience against various attacks, including man-in-the-middle, quantum computation, relay, impersonation, and DoS. Experimental results show that the scheme achieves a communication cost of 0.9 KB for 2 rounds, outperforming quantum-resistant schemes (2.5 KB) and performing similarly to classical schemes (1.2 KB). The scheme requires 2 quantum preparations and 1 measurement, offering higher efficiency compared to quantum-resistant methods. Compared to QKD and computational hardness-based models, our approach provides superior key management efficiency, practical implementation in non-fault-tolerant quantum systems, and enhanced security protection. Rigorous analysis and validation using IBM’s quantum-enabled environments demonstrate the robustness of the protocol in addressing modern smart grid security challenges.

The reliable operation of industrial systems requires not only the prompt detection of faults but also their accurate classification into the appropriate categories. At present, numerous data-driven industrial fault detection and diagnosis models, which have been developed based on historical fault data, frequently neglect the issue of label noise. When labels are corrupted by noise, a significant degradation in the performance of industrial fault detection models can be observed. In this article, alabel-noise-resistanttime-seriesclassification (LNRTSC) method based on consistency-driven label correction is proposed. First, an attention-based temporal correlation-enhanced encoder is introduced to extract low-dimensional representations of industrial time series. Then, label confidence, which is assessed based on local label consistency, is utilized to correct noisy labels during training. In addition, a two-stage self-supervised enhancement strategy is designed to guarantee the reliability of the corrected labels. Specifically, a reconstruction loss term is introduced to assist feature extraction in the warming-up stage, and a newly designed contrastive loss term is added to the loss function for the LNL training stage, which mitigates the effect of false negatives. Finally, the effectiveness of the LNRTSC method is validated on the Tennessee Eastman process and the SEU-gearbox datasets. When compared to peer methods, the LNRTSC approach demonstrates substantial improvements in fault classification performance on corrupted data.

To address the challenges of multiscale feature coupling, high-frequency noise interference, and complex nonlinear relationship modeling in inverter fault diagnosis, this study proposes an intelligent diagnostic method based on a synchronous cascaded wavelet transform and an improved pyramid vision transformer-Kolmogorov–Arnold Network. First, the synchronous cascaded wavelet transform is utilized to convert the inverter output signal into a 2-D time–frequency image, effectively capturing the multiband harmonic features induced by capacitor parameter degradation. Next, a cross-scale attention mechanism is developed to dynamically weight and fuse high-resolution details with low-resolution contextual features from adjacent stages, thereby enhancing the joint perception of both high-frequency transient faults and low-frequency gradual faults. In addition, an adaptive spatial reduction mechanism is introduced to lower computational costs while preserving fault-relevant frequency-band information. Finally, the Kolmogorov–Arnold network module employs interpretable basis functions constructed from spline-parameterized univariate functions to model the nonlinear mapping between capacitor parameters and harmonic characteristics. Its edge-level learnable activation structure further enhances the capability to decouple concurrent faults. Experimental results demonstrate that the proposed method achieves high diagnostic accuracy, significantly reduces model parameters, and improves computational efficiency.

This article investigates the privacy-preserving fusion filtering problem for vehicle localization subject to unknown inputs. High-accuracy and privacy-preserving localization is essential for the safe and reliable operation of intelligent transportation systems. In practice, vehicle localization is often challenged by measurement bias caused by nonline-of-sight (NLOS) propagation, unknown inputs arising from uncertainties or acceleration/deceleration maneuvers, and risks of signal and location privacy leakage. To address these issues, a privacy-preserving fusion filtering framework is proposed by integrating the reconfigurable intelligent surface (RIS) technique, an unknown-input estimation method, and an output-mask mechanism. A unified measurement model is first developed to represent both line-of-sight (LOS) and NLOS scenarios, and RISs are employed to construct virtual LOS paths to mitigate NLOS effects. An output-mask-based privacy-preserving strategy is then designed to prevent eavesdroppers from inferring vehicle locations or signal characteristics while maintaining the required filtering performance. Based on this model, an unknown-input estimator and a privacy-preserving filter are constructed, and the impacts of NLOS propagation, unknown inputs, and masking on filtering performance are analyzed. The associated gain matrix parameters are obtained by solving the corresponding optimization problems. Furthermore, an RIS-assisted privacy-preserving fusion filtering algorithm is developed to exploit multisource measurements and enhance localization robustness and accuracy. Simulation results demonstrate the effectiveness of the proposed method.

Health monitoring and fault diagnosis of high-speed train traction systems are essential for maintaining reliable operation. To effectively process multisensor signals and avoid overfitting, ensemble learning methods are employed, leveraging multiple base models to integrate data from various sensors and enhance fault diagnosis performance. However, conventional ensemble frameworks are often burdened by excessive model parameters, limiting their applicability on edge computing processors. To address these challenges, this study proposes a novel dual attention-guided ensemble framework. This framework incorporates multiple multiscale feature attention (MFA) modules and a decision fusion attention (DFA) module, designed to capture critical features from multisensor signals, optimize the capacity of prominent feature extraction, and simultaneously reducing trainable parameters. The proposed ensemble framework is validated on the hardware-in-the-loop (HIL) simulation platform for high-speed train traction control systems, with experimental results demonstrating its superior effectiveness over several recently published ensemble learning methods.

This article is concerned with the distributed recursive filtering problem for a class of nonlinear time-varying systems over sensor networks (SNs) under state saturations via a reputation-aware mechanism (RAM). The state saturation is considered to describe the inevitable constraints of physical equipment, while the RAM is introduced to eliminate abnormal data caused by sensor faults or malicious attacks. To address these challenges, a reputation-aware distributed recursive filtering (RADRF) algorithm is developed, where a novel reputation model is constructed to assign credibility scores to neighboring sensors, thereby identifying and rejecting unreliable data. Furthermore, a saturation-dependent recursive filter is designed, through which the upper bound of the covariance of filtering error dynamics (UBCFEDs) is determined by solving a recursive matrix equation. The filter gain is subsequently parameterized by minimizing the trace of the UBCFED so as to guarantee the optimality of estimation performance under nonlinear saturation effects and reputation-based weighting. To validate the feasibility and effectiveness of the developed approach, an illustrative case study concerning the indoor localization of a mobile robot is conducted. Simulation results demonstrate that the proposed RADRF algorithm significantly improves estimation accuracy and robustness compared with schemes that neglect the RAM.

Accurately predicting real estate enterprise risk is crucial for the national economy. Although some initial works have been made on this topic such as Z-score, support vector machines, and logistic regression, there remains a gap in comprehensive models that can effectively capture the dynamic risk fluctuations from real estate-specific data. As such, a novel prediction model called HRAGRU is proposed for real estate enterprises to forecast potential risk through multimodal data including news reports, policy updates, and stock information in this paper. We first extract the semantic information from news text by using a BERT model optimized for high-target semantic density. Then we investigate the relationships among various data types through a graph neural network (GNN) model with randomly masked edges or nodes. Finally, we establish an improved gated recurrent unit (GRU) model to capture the interactions between new and historical data. The effectiveness of the proposed HRAGRU model is validated using data from A-share and Hong Kong-listed real estate companies, demonstrating its superior performance in forecasting corporate risk indices. Our sources are released at https://github.com/maxiaoyan290/HRAGRU

This paper investigates the state estimation problem for power distribution networks subject to dynamic quantization effects and hybrid cyber-attacks, where measurement signals are transmitted from sensors to a remote filter via open digital communication networks. To enhance bandwidth utilization and ensure reliable data transmission, a dynamic quantization mechanism is introduced, which effectively accommodates the dynamic characteristics of power signals. Furthermore, the system is vulnerable to hybrid cyber-attacks that may occur simultaneously in a random manner, including denial-of-service attacks and false data injection attacks, characterized by Bernoulli distributed random variables. The primary objective of this work is to develop a recursive unscented Kalman filter capable of addressing the combined challenges of measurement nonlinearities, dynamic quantization effects, and hybrid cyber-attack scenarios. By solving Riccati-like difference equations, an upper bound on the filtering error covariance is derived, and subsequently minimized through the design of time-varying filter gains. Extensive simulations on the IEEE 69 distribution test system demonstrate the effectiveness of the proposed filtering algorithm.

This paper addresses the issue of robust finite-horizon H∞ filtering for complex networks subject to replay attacks. A replay attack strategy is implemented by the adversary on the communication channel between the network nodes and the filters, with the intention of replacing the current measurement data with previously recorded measurement data. Considering the limited energy of the attacker, a binary variable is adopted to indicate whether the communication channel is under attack. To better characterize the dynamic behavior of replay attacks, two factors dependent on attack frequency and a time-varying parameter are introduced. Subsequently, under the impact of replay attacks, the switched filtering error dynamics is obtained with a time-varying delay. By employing the average dwell-time method, sufficient conditions are derived to guarantee the weighted H∞ performance of the filtering error dynamics. Furthermore, the filter gain parameters are computed through the solution of some recursive matrix inequalities. Finally, numerical simulation results are conducted to verify that the developed filter design algorithm is effective.

This paper addresses the problem of distributed state estimation for nonlinear systems over sensor networks that are subject to replay attacks. Sensor resolution, recognized as a crucial indicator of measurement accuracy and its influence on estimation performance, is taken into account to reflect practical engineering scenarios. An upper-bounding technique is employed to handle the uncertainty introduced by sensor resolution. Replay attacks, executed by adversaries on communication channels between sensor nodes, are characterized by the replacement of current innovations with previously recorded innovations. The dynamic behavior of these replay attacks is described by two factors: one dependent on a stochastic variable and the other on a time-varying parameter. The objective of this study is to design distributed state estimators to ensure that the estimation error dynamics are exponentially ultimately bounded in the mean-square sense while the desired security level is maintained. Furthermore, the gain parameters of the estimators are determined by solving specific matrix inequalities. Finally, the feasibility and effectiveness of the proposed estimation approach are validated through numerical simulations.

This paper addresses the problem of resilient state estimation for complex networks, with a particular focus on scenarios involving state saturations and replay attacks, using a partial-nodes-based approach. State saturation, characterized as a type of nonlinearity, is considered to reflect practical engineering scenarios. Replay attacks, executed by adversaries on communication channels between sensors and estimators, are defined by the replacement of current measurements with previously recorded measurements. The dynamics of these replay attacks are described by two components: one dependent on a stochastic variable and the other on a time-varying parameter. To mitigate the adverse effects of state saturations and replay attacks on estimation performance, a partial-nodes-based resilient estimator is designed. By employing the convex hull method, a sufficient condition is derived to ensure that the augmented error system is exponentially ultimately bounded in the mean-square sense. Furthermore, the gain parameter of the estimator is determined by solving a specific matrix inequality. Finally, the feasibility and effectiveness of the proposed estimation approach are validated through numerical simulations.

This paper addresses the state estimation problem for a class of networked two-dimensional systems subject to autocorrelated multiplicative noises and multi-step observation delays. The autocorrelated multiplicative noises are characterized as a linear system corrupted by additive white noises, and the multi-step observation delays are described by the instantaneous and delayed output equations. The globally coupled correlation terms are first introduced to deal with the autocorrelated multiplicative noises, and then a novel observation reconstruction approach is proposed to tackle the difficulty induced by multi-step observation delays. By utilizing the space equivalence verification, it is shown that the reconstructed delay-free observation sequence retains the same information as the original delayed observation sequence. The purpose of this paper is to design an iterative coupled estimator based on the globally coupled correlation terms and the reconstructed observation sequence. By means of the mathematical induction technique, it is demonstrated that the proposed estimator preserves the property of unbiasedness. Moreover, the estimator gains are obtained by minimizing the estimation error covariance, and some sufficient conditions are derived to guarantee the existence of an upper bound for the estimation error covariance in the mean-square sense. Finally, a numerical example is provided to verify the effectiveness of the proposed estimation scheme.

Accurate prediction of machinery’sremaining useful life (RUL) is essential for preventing catastrophic breakdowns and supporting predictive maintenance. Although RUL prediction has been extensively studied, most literature develops on unimodal data, which providesa limited and often biased perspective. Multimodal monitoring, which collects multiple sensor data, enables a more comprehensive understanding of degradation processes. While promising, significant challenges are encountered in existing methods: 1) point yet deterministic predictions are predominantly produced which, while potentially erroneous, tend to exhibit overconfidence, thereby lacking the dynamic uncertainty informing; 2) the processing of heterogeneous data and the achievement of physically interpretable fusion remain challenging; and 3) anomalies in the operation process are not appropriately identified. To address these issues, a new multimodal uncertainty-aware RUL prediction framework is proposed, grounded in stochastic modeling. Fractional stochastic differential equation-controlled subnets process each modality independently, wherein layer-wise transformations are modeled as state evolution in stochastic dynamical systems, allowing modality-specific uncertainty to be quantified without requiring parameter priors. A Lagrange multiplier-based fusion module is subsequently employed to perform explicit uncertainty-based fusion, enabling an interpretable and synergistic integration. Validation on harmonic drive reducers for robots demonstrates the superiority of the proposed framework, achieving an average improvement of 26.6% in RMSE and a 16.6% reduction in MAPE compared to state-of-the-art benchmarks. Furthermore, the method significantly reduces prediction uncertainty variance by 21.3%, offering more reliable insights into system degradation.

This article investigates the proportional-integral-derivative (PID) containment control problem for a class of linear MAS with multirate measurements under the constraint of sensor resolution. The sensors of agents are classified into two distinct groups, characterized by their relatively fast and slow sampling periods. The concept of sensor resolution is introduced to quantify the ability of sensors to detect the smallest changes in information. A PID controller with an improved structure is proposed to achieve containment control, ensuring that follower agents remain within the convex hull formed by the leader agents. The closed-loop system is reformulated into a simplified representation, incorporating both sampling characteristics and communication topology. Sufficient conditions are then derived to guarantee the exponentially ultimate boundedness of the tracking error. Based on these conditions, an iterative algorithm is developed for computing the required controller gains. Finally, a simulation study, along with comparative analyses, is conducted to validate the effectiveness of the proposed approach.

This paper is concerned with the set-membership filtering problem for active power distribution systems that are influenced by unknown but bounded noises. Both the phenomena of fading channels and limited communication capacity are taken into account. In consideration of the integration of photovoltaic generation systems, an active power distribution system model is formulated which encompasses the conventional power distribution networks and distributed power sources. Network data are transmitted to a remote filter through fading channels, where a component-based dynamic event-triggered mechanism is introduced, by which the transmission frequency is reduced while sustaining the filtering performance, thereby mitigating the transmission load of the communication network. The purpose of this paper is to design a dynamic event-triggered filter such that, when faced with unknown but bounded noises, a set of zonotopes is devised to confine the system states. By minimizing the$F$-radius of these zonotopes, the time-varying filter gain is determined recursively at each time step. Additionally, simulation experiments on the IEEE 34 distribution test system are carried out, through which the efficiency of the proposed filtering methodology is validated.Note to Practitioners— The rapid increase in energy demand, coupled with the fast progression of energy technologies, has resulted in the widespread merging of traditional distribution networks with various other distributed power systems Consequently, these have transformed into active power distribution systems (APDSs), undergoing notable alterations in their distribution network configurations. Challenges like power back-flow and overvoltage, which might seriously compromise the stability, operation, and control of the APDS, can be introduced by this transformation. For addressing these challenges, having precise information about the system state is deemed crucial for the facilitation of real-time monitoring, improved control, and reliable protection of the APDS, and therefore the state estimation problem has been attracting an increasing research interest for ensuring the system’s safe operation and economical dispatch. In this paper, the problem of zonotopic set-membership filtering is examined for the APDS with unknown-but-bounded noises in fading channels. Initially, a component-based dynamic event-triggered mechanism is used in the APDS, which eases the load on communication networks with limited resources, and the fading coefficients are represented as uncertain variables within a specified range. Subsequently, by harnessing mathematical induction and set theory, the state estimation algorithm is introduced, ensuring the real system states are strictly encompassed within zonotopic sets and system states are estimated accurately. In conclusion, simulation experiments on the IEEE 34 distribution test system demonstrate the effectiveness of the introduced filtering algorithm.

In practical applications, the simultaneous optimization of numerous design parameters in time-consuming multi-objective optimization experiments is recognized as a significant bottleneck across various scientific and engineering disciplines. A prominent example is the optimization of machining parameters for achieving efficient and precise robotic belt grinding (RBG). This paper presents a closed-loop machining parameter optimization approach, which comprises two key stages: forward multi-task prediction and backward multi-objective parameter optimization. In the first stage, a physics-informed neural network (PINN) method is introduced, which integrates the multi-gate mixture-of-experts multi-task learning method with an RBG mechanism model to simultaneously predict material removal depth and averaged surface roughness. In the second stage, a powered multi-objective particle swarm optimization (MOPSO) method is developed, which combines a standard MOPSO method with a non-linear Powerball technique, to efficiently optimize the RBG machining parameters with a limited number of training iterations based on the learned PINN model. Two optimal machining parameter solutions are generated and recommended for the RBG machining process. The effectiveness and superiority of the proposed closed-loop parameter optimization method are validated through comparative experiments, which demonstrate its advantages in both coprediction accuracy and optimization efficiency.

In this article, the problem of ultimately bounded state estimation is investigated for discrete-time multirate singularly perturbed complex networks under the bit rate constraints, where the sensor sampling period is allowed to differ from the updating period of the networks. The facilitation of communication between sensors and the remote estimator through wireless networks, which are subject to bit rate constraints, involves the use of a coding-decoding mechanism. For efficient estimation in the presence of periodic measurements, a specialized impulsive estimation method is developed, which aims to carry out impulsive corrections precisely at the instants when the measurement signal is received by the estimator. By employing the iteration analysis method under the impulsive mechanism, a sufficient condition is established that ensures the exponential boundedness of the estimation error dynamics. Furthermore, an optimization algorithm is introduced for addressing the challenges related to bit rate allocation and the design of desired estimator gains. Within the presented theoretical framework, the correlation between estimation performance and bit rate allocation is elucidated. Finally, a simulation example is provided to demonstrate the validity of the proposed estimation approach.

This paper is concerned with the privacy-preserving bidirectional electric power data transmission problem of the smart grid. A privacy-preserving bidirectional data transmission (BDT) protocol is developed over semi-quantum computation with aim to achieve bidirectional sensitive data flow between power suppliers and users. The minimal quantum cost is pursued under practical constraints while ensuring that power sensitive information is not leaked. To achieve the goal, a two-way data transmission protocol is first proposed that combines mutual identity authentication with message authentication for the benefits of enhanced security. Furthermore, for the preservation of privacy, a semi-quantum duplex communication approach is utilized, wherein the quantum state is randomly divided into two parts: teleportation and measurement qubits. The effectiveness of the privacy-preserving scheme against existing attack strategies is also rigorously analyzed. Lastly, simulation studies conducted on the IBM quantum cloud platform validate and underscore the superiority of the developed privacy-preserving BDT protocol.

The concept of fuzzy domain adaptation (FDA) is focused on transferring a model trained in a source domain to a target domain, where intrinsic distribution discrepancies exist in non-stationary and non-deterministic environments. In this paper, a novel drift decoupling-based variational adaptation network (DD-VAN) is proposed for FDA, allowing for the learning of intra-domain evolutionary patterns and inter-domain uncertainties. The DD-VAN algorithm is implemented in three main steps: (1) an intra-domain evolutionary trend modeling module is first employed to capture unknown temporal variations through an autoencoder architecture with variational inference; (2) a prototype-assisted fuzzy clustering module is used to estimate the membership degree of the target data, characterizing the inherent uncertainty and imprecision present in real-world distributions; and (3) a membership-aware domain fuzzy matching module is utilized to learn the gradual transitions between category-related data pairs in the source and target domains by introducing uncertainties. Furthermore, it is theoretically demonstrated that the inferred posterior distributions of latent codes can be optimized to align with the corresponding prior distributions by minimizing the Kullback-Leibler divergence. Extensive experiments are conducted on cross-domain tasks involving both synthetic and realworld datasets, and the experimental results suggest that the DDVAN algorithm outperforms existing state-of-the-art methods.

This paper is concerned with the$H_{\infty }$proportional-integral-derivative (PID) control problem for Takagi-Sugeno fuzzy systems over lossy networks that are characterized by the Gilbert-Eillott model. The communication quality is reflected by the presence of two channel modes (i.e., “bad” mode and “good” mode), which switch randomly according to a Markov process. In the “bad” mode, packet dropouts are governed by a stochastic variable sequence. Considering the inaccessibility of channel modes, a mode detector is utilized to estimate the communication situation. The relationship between the actual channel mode and the estimated mode is depicted in terms of certain conditional probabilities. Moreover, a comprehensive model is constructed to represent the probability uncertainties arising from statistical errors in channel mode switching, packet dropouts, and mode detection processes. Subsequently, a robust asynchronous PID controller, based on the detected channel mode, is proposed. Sufficient conditions are then derived to ensure the mean-square stability of the closed-loop system while maintaining the desired$H_{\infty }$performance. Finally, the efficacy of the proposed design approach is demonstrated through a simulation example.

This article addresses the privacy-preserving energy management problem of battery energy storage systems (BESSs). An autonomous privacy-preserving distributed optimization (APPDO) scheme is developed over time-varying networks with the aim of regulating the power output of local BESS to fulfill the total load demand at the minimum economic cost under battery capacity constraints without privacy leakage. To this end, a linearly convergent distributed algorithm is proposed by combining the gradient descent algorithm with leaderless and leader-following consensus schemes. This algorithm is applicable to both islanded and grid-connected modes of BESSs. Furthermore, a novel privacy-preserving approach is constructed by injecting well-designed perturbation sequences into the data exchanged between neighboring nodes, making it effective against malicious eavesdroppers. Furthermore, a comprehensive analysis framework is established to evaluate the convergence, optimality, and privacy-preserving performance of the APPDO algorithm. Finally, numerical studies are conducted to demonstrate the effectiveness of the developed APPDO scheme.

This artcle investigates the fusion estimation problem for renewable energy microgrids subject to relay transmissions and cyber-attacks. A filter-and-forward (FaF) relay strategy is adopted to ensure the reliable transmission of power signals from distributed sensors to the remote estimator. This strategy jointly accounts for both sensor-relay and relay-estimator channels when determining signal transmission, and relays with embedded filtering capabilities are considered essential for extracting relay signals from corrupted measurements. In addition, four stochastic sequences governed by Bernoulli distributions are employed to characterize denial-of-service and false data injection attacks, which occur randomly throughout the communication process. The principal objective is to improve estimation accuracy by constructing an estimation scheme capable of operating under the presence of FaF relays and hybrid attacks. Specifically, the proposed scheme consists of two main components: first, the design of a recursive filtering algorithm at each FaF relay for generating the relay signal, and second, the development of a sequential fusion estimation algorithm to enable accurate state estimation. Sufficient conditions are derived to guarantee the mean-square boundedness of both the filtering error and the estimation error. The effectiveness of the proposed estimation methodology is demonstrated through simulation experiments conducted on renewable energy microgrids in a wireless multisensor system.

To address the challenges encountered by dictionary learning-based monitoring, this article presents a novel pattern discovery scheme for detection and isolation of incipient faults that involves structured sparse coding and sequential dictionary augmentations. Through learning a basic dictionary for normal pattern and augmenting the low-dimensional sparse dictionaries for analyzing different fault patterns, the process signals can be decomposed into fault-free and fault-related components. To guarantee the in-statistical-control status of the fault-free part and improve detection sensitivity, a$\ell _{2}$-penalty is imposed on the sum of coefficient vectors to ensure that the monitoring statistic related to the fault-free part will not exceed the control limit. In addition, two Frobenius norm penalties are imposed on the zero centered coefficient matrix and atom matrix to improve the robustness of signal decomposition. Instead of imposing$\ell _{1}$-sparsity constraint on the atoms, a hard sparsity constraint is used to correctly select fault-related feature variables, so that fault patterns can be better revealed. The informative dictionaries are then incorporated into the moving window-based monitoring strategy, yielding a fault detection and isolation scheme suitable for incipient faults. The superior performance of our proposed approach is validated by application studies involving a numerical example and two practical industrial processes.

Accurate prediction of material removal depth and averaged surface roughness is crucial for evaluating the performance of robotic belt grinding (RBG). Nevertheless, the machining parameters of RBG across different spaces exhibit various data distributions, which often results in prediction shifts on unseen machining parameters when using conventional approaches. In this article, we introduce a pairwise domain adaptation-assisted dual-task learning (PW-DA-DTL) method for copredicting material removal depth and averaged surface roughness with regard to new RBG machining parameter spaces. The multigate mixture-of-experts method is employed as the foundational framework for dual-task learning, effectively capturing and modeling the relationships between material removal depth and average surface roughness by leveraging their inherent task interdependencies. The pairwise domain adaptation strategy is put forward to simultaneously enhance sample diversity and mitigate cross-domain data distribution discrepancy between the existing and new RBG machining parameter spaces. Comparative experiments are presented to demonstrate the effectiveness and superiority of the proposed PW-DA-DTL method.

This article is concerned with the adaptive dynamic state estimation (DSE) problem for synchronous-generator-based multimachine power grids under measurement noise with unknown statistics. The statistical properties of the measurement noises are efficiently revealed by utilizing limited measurement data contained in a sliding window, and such data is employed to establish the base distribution of the noises, with the aid of the Gaussian mixture model and the kernel density estimation scheme. Subsequently, the component number of the base distribution of the measurement noises is reduced by designing a fuzzy C-means clustering algorithm with the Wasserstein distance criterion. An improved sliding-window-based adaptive cubature Kalman filtering scheme is then proposed, which leverages the already obtained statistical characteristics of the measurement noise and the concept of the Gaussian summation filter. Finally, the validity of the proposed adaptive DSE algorithm under various measurement noise statistics is illustrated by simulation studies conducted on the IEEE 39-bus system featuring three test scenarios.

This article focuses on the crucial role of reliable dc–dc converter operation for the stability of modern power electronic devices. Addressed is a common issue in the fault diagnosis of dc–dc converters: the tendency to rely on local feature fitting while the temporal continuity of electrical signals is neglected. An innovative diagnostic method that utilizes an adaptive wavelet transform from a data processing perspective is proposed. This technique can dynamically adjust the scale and translation parameters to adapt to the continuous changes in electrical signals caused by varying circuit conditions. From the standpoint of model improvement, the extended convolutional capsule network model is designed. Through multiscale feature extraction, integration of global-local attention mechanisms, and global vector analysis, this model effectively diagnoses fault features. It is demonstrated that our method is effective in extracting the time-continuity features of electrical signals, and exhibits significant advantages in diagnostic accuracy, performance metrics, and application generalization capability. Consequently, this study presents a holistic and effective approach for fault diagnosis in dc–dc converters.

With the rapid development of high-speed train, health monitoring of high-speed train traction power system has gradually become a popular research topic. The traction asynchronous motor, as a key component in the traction power systems, greatly affects the reliability, stability, and safety of high-speed train operation. Normally, when faults occur, the train needs to immediately slow down or even stop to avoid unimaginable losses, resulting in limited fault data. Traditional data-driven fault diagnosis methods may face the local optimum problem during the optimization process when training samples are insufficient. In this study, a novel gossip strategy-based fault diagnosis method is proposed to prevent the local optimum problem, thus improving fault diagnosis performance. The proposed gossip strategy-based fault diagnosis method is validated on the hardware-in-the-loop high-speed train traction control system simulation platform, and the experimental results unequivocally show that the proposed method outperforms other well-known methods.

This article addresses the real-time collision avoidance and adaptive cruise control (ACC) problems for autonomous vehicles within a cloud control platform. Collision avoidance and ACC are identified as essential components of autonomous driving as they directly impact the operational safety of the system. In the cloud control platform, a token bucket shaper is employed to regulate data transmission rates, ensuring the priority transmission of critical data while effectively preventing network congestion and cloud overload. The primary objective of this study is to achieve real-time collision avoidance ACC by comprehensively considering the influence of external noise disturbances and the token bucket shaper. Based on a signal smoothing method, a novel observer structure is first constructed to enhance state estimation performance. Then, an observer-based controller is designed to counteract process noise disturbances, thereby improving the control performance of the vehicle-following system. Subsequently, a real-time collision avoidance constraint index is formulated, and a method for solving this constraint is proposed, overcoming the effects of immeasurable states. The impact of the token bucket shaper and external noise disturbances on control performance is thoroughly analyzed, and sufficient conditions are derived to simultaneously ensure real-time collision avoidance and the bounded stability of vehicle-following error. Finally, the effectiveness of the proposed collision avoidance ACC algorithm is validated through a simulation example.

This article is concerned with the secure collision-free tracking control problem for autonomous vehicles with uncertainties, where system signals are transmitted through constrained communication networks. In such open and uncertain environments, the control performance of vehicles is seriously affected by privacy leakage, non-Gaussian noise, and obstacles. The aim of this research is to propose a tracking control scheme that ensures security, mean-square boundedness, and collision-free performance concurrently. Initially, to safeguard the privacy of the transmitted data and to enable secure tracking control, a dynamic encoding-based ElGamal encryption mechanism is introduced, which is further embedded in the design of the observer-based tracking controller. Subsequently, a collision-free chance-constrained index is proposed for achieving real-time obstacle avoidance by comprehensively considering the influence of stochastic noises. A thorough analysis is conducted to examine the impact of non-Gaussian noise and unmeasurable states on the performance of collision-free tracking control. Sufficient conditions are derived to guarantee the desired performance, and the corresponding control inputs are obtained by solving certain optimization problems subject to chance constraints. Finally, an illustrative example is provided to validate the effectiveness of the proposed secure collision-free tracking controller.

Pedestrian trajectory prediction is fundamental in various applications, such as autonomous driving, intelligent surveillance, and traffic management. Existing methods generally fall into two categories: model-driven approaches and data-driven approaches. However, both approaches have inherent limitations when applied to real-world scenarios, particularly in capturing the complex interactions between pedestrians and modeling the stochastic nature of human motion. Notably, there is a lack of research on integrating the strengths of model-driven and data-driven paradigms, which can better address these challenges. This paper aims to fill these limitations by proposing a novel model-data dual-driven approach, called Social Entropy Informer (SEI), for pedestrian trajectory prediction. SEI simultaneously models local and global pedestrian interactions while incorporating information entropy to capture human motion’s inherent randomness and uncertainty quantitatively, which provides a robust framework for predicting pedestrian trajectories. Furthermore, we propose a new loss function derived from information theory, which accounts for the stochasticity of pedestrian movement and enhances the model’s ability to generalize across diverse scenarios. The SEI framework integrates feature extraction, entropy-based stochastic modeling, and the new loss function, improving prediction accuracy and model interpretability. Experimental results demonstrate that SEI outperforms other benchmark methods in prediction accuracy.

In this article, the state estimation problem is studied for Markovian jump neural networks (MJNNs) within a digital network framework. The wireless communication channel with limited bandwidth is characterized by a constrained bit rate, and the occurrence of bit flips during wireless transmission is mathematically modeled. A transmission mechanism, which includes coding-decoding under bit-rate constraints and considers probabilistic bit flips, is introduced, providing a thorough characterization of the digital transmission process. A mode-dependent remote estimator is designed, which is capable of effectively capturing the internal state of the neural network. Furthermore, a sufficient condition is proposed to ensure the estimation error to remain bounded under challenging network conditions. Within this theoretical framework, the relationship between the neural network's estimation performance and the bit rate is explored. Finally, a simulation example is provided to validate the theoretical findings.

In this article, we consider the impulsive estimation problem for a specific category of discrete-time complex networks (CNs) characterized by Markovian switching topologies. The measurement outputs of the underlying CNs, transmitted to the observer over wireless networks, are subject to bit rate constraints. To effectively reduce the estimation error and enhance estimation performance, a mode-dependent impulsive observer is proposed that employs the impulse mechanism. The application of stochastic analysis techniques leads to the derivation of a sufficient condition for ensuring the mean-square boundedness of the estimation error dynamics. The upper bound of the error is then analyzed by iteratively exploring the Lyapunov relation at both impulsive and non-impulsive instants. Moreover, an optimization algorithm is presented for handling the bit rate allocation, which is coupled with the design of desired observer gains using the linear matrix inequality (LMI) approach. Within this theoretical framework, the relationship between the mean-square estimation performance and the bit rate allocation protocol is further elucidated. Finally, a simulation example is provided to demonstrate the validity and effectiveness of the proposed impulsive estimation approach.

This brief is concerned with the prediction problem of product popularity under a social network (SN) with positive-negative diffusion (PND). First, a PND model is proposed to enable the simulation of product diffusion, and three user states are defined. Second, an optimal and precise feature vector of every user is extracted through a multi-agent-system-based attention mechanism (MASAM) that is devised. The weight matrix shared in the mechanism of all agents is learned using a distributed learning algorithm provided in MASAM. Third, an MAS model for product diffusion on SN is established based on the feature representations from MASAM. Rules for agent interaction during PND diffusion are suggested, which accelerate the simulation of information spread in SN. Finally, comprehensive experiments are conducted to verify the effectiveness and efficiency of the proposed models and algorithms in prediction and to compare their performance with baseline methods. Furthermore, a case study is provided to illustrate the applicability and extendibility of the developed algorithm.

This article addresses the issue of multiple influences maximization under dynamic link strength (MIMDLS) in multi-agent systems (MASs). Initially, a novel model for dynamic link strength within MASs is suggested to facilitate the simulation of multiple influences diffusion. Subsequently, the MIMDLS problem is formulated with both competitive and cooperative scenarios being examined. In response, two diffusion models, specifically the competitive multiple influences independent cascade (Cp-MIIC) model and the cooperative multiple influences linear threshold (Cr-MILT) model, are designed for MASs. Furthermore, a distributed deep reinforcement learning (DRL) framework is established based on MASs by incorporating asynchronous training and updating processes for seed selection in the context of multiple influences. Moreover, the developed distributed DRL algorithm encompasses the estimation of Q value as well as the management of constraints within Cp-MIIC and Cr-MILT models. Finally, comprehensive experiments are conducted to: 1) validate the effectiveness and efficiency of the proposed models and algorithms in terms of multiple influence diffusion and 2) benchmark their performance against state-of-the-art methods.

This article is concerned with a recursive remote estimation problem for a class of nonlinear complex networks subject to the token bucket protocol (TBP) and amplify-and-forward (AF) relays. The influence of the TBP is considered, for the first time, in the context of networked state estimation, where the token consumptions are modeled in a stochastic manner, so as to describe the potential size variability of transmitted measurement signals. Once processed by the TBP, the signals, with stochastic channel coefficients, are transmitted to the remote estimator via AF relays, where a failure in transmission under the TBP may occur due to insufficient tokens in the bucket. An extended-Kalman-filter-based novel recursive estimator is proposed, and by solving Riccati-like difference equations, an upper bound of prediction/estimation error covariance is determined and further minimized through the design of an appropriate estimator gain. The impact of the TBP on estimation performance is also investigated. Some numerical simulations are presented to demonstrate the effectiveness of the proposed estimator and the effects of the TBP.

This article proposes predefined-time adaptive neural network (PTANN) and event-triggered PTANN (ET-PTANN) models to efficiently compute the time-varying tensor Moore-Penrose (MP) inverse. The PTANN model incorporates a novel adaptive parameter and activation function, enabling it to achieve strongly predefined-time convergence. Unlike traditional time-varying parameters that increase over time, the adaptive parameter is proportional to the error norm, thereby better allocating computational resources and improving efficiency. To further enhance efficiency, the ET-PTANN model combines an event trigger with the evolution formula, resulting in the adjustment of step size and reduction of computation frequency compared to the PTANN model. By conducting mathematical derivations, the article derives the upper bound of convergence time for the proposed neural network models and determines the minimum execution interval for the event trigger. A simulation example demonstrates that the PTANN and ET-PTANN models outperform other related neural network models in terms of computational efficiency and convergence rate. Finally, the practicality of the PTANN and ET-PTANN models is demonstrated through their application for mobile sound source localization.

This article is concerned with the design problem of an $H_{\infty }$ optimal fault detection (FD) filter for networked interval type-2 (IT2) fuzzy systems that are subjected to stochastic cyberattacks. To effectively reduce the utilization of constrained network resources, a new dynamically adjusted event-triggered weighted try-once-discard (DAET-WTOD) protocol is developed, in which two adaptive rules are constructed based on the measured output and the probability of denial-of-service (DoS) attacks. Furthermore, a fuzzy switched-like FD filter is designed with the purpose of detecting system fault signals, while simultaneously considering the DAET-WTOD protocol and stochastic cyberattacks. Subsequently, by utilizing an imperfect premise matching (IPM) scheme, an opposition-based learning adaptive differential evolution algorithm is proposed to deal with the networked IT2 fuzzy systems. This algorithm is capable of iteratively searching the membership function values of the fuzzy filter in real time, thereby achieving improved $H_{\infty }$ performance. Finally, some simulation results are provided to verify the feasibility and advantages of the proposed $H_{\infty }$ optimal FD technique.

In this article, the set-membership state estimation problem is investigated for a class of nonlinear complex networks under the FlexRay protocols (FRPs). In order to address practical engineering requirements, the multirate sampling is taken into account which allows for different sampling periods of the system state and the measurement. On the other hand, the FRP is deployed in the communication network from sensors to estimators in order to alleviate the communication burden. The underlying nonlinearity studied in this article is of a general nature, and an approach based on neural networks is employed to handle the nonlinearity. By utilizing the convex optimization technique, sufficient conditions are established in order to restrain the estimation errors within certain ellipsoidal constraints. Then, the estimator gains and the tuning scalars of the neural network are derived by solving several optimization problems. Finally, a practical simulation is conducted to verify the validity of the developed set-membership estimation scheme.

This article is concerned with the joint state and unknown input (SUI) estimation for a class of artificial neural networks (ANNs) with sensor resolution (SR) under the encoding-decoding mechanisms. The consideration of SR, which is an important specification of sensors in the real world, caters to engineering practice. Furthermore, the implementation of the encoding-decoding mechanism in the communication network aims to accommodate the limited bandwidth. The objective of this study is to propose a set-membership estimation algorithm that accurately estimates the state of the ANN without being influenced by the unknown input while accounting for the SR and the encoding-decoding mechanism. First, a sufficient condition is derived to ensure an ellipsoidal constraint on the estimation error. Then, by addressing an optimization problem, the design of the estimator gains is accomplished, and the minimal ellipsoidal constraint on the state estimation error is obtained. Finally, an example is provided to confirm the validity of the proposed joint SUI estimation scheme.

Multivariate time series forecasting (MTSF) is of significant importance in the enhancement and optimization of real-world applications. The task of MTSF poses substantial challenges due to the unpredictability of temporal patterns and the complexity in modeling the influence of all nonpredictive sequences on the target sequence at different time stages. Recent research has demonstrated the potential held by the Transformer algorithm to augment long-term forecasting capability. However, certain obstacles considerably obstruct the direct application of the Transformer to MTSF, such as an unsuitable embedding method, inadequate consideration of intervariable associations, and the intrinsic restriction of the point-wise objective function. To overcome these challenges, the Fusionformer, an effective Transformer-based forecasting model, is put forth in this article, which is characterized by three distinctive features: 1) the introduction of a segment-wise sequence embedding (SWSE) method allows for the conversion of the input sequence into multiple informative segments; 2) the implementation of a fusion attention mechanism (FAM), designed to capture predominant features across the time dimension and to model intricate intervariable dependencies; and 3) the development of an adversarial learning method, equipped with an auxiliary discriminator, facilitates the learning of data distribution, instead of progressively correcting the prediction error, thus substantially enhancing the MTSF's accuracy. Furthermore, a Fusionformer-based risk assessment (FRA) method is structured for open-pit mine slope failure early warning issue (SFEW), which aims to prevent potential disasters by accurately predicting future slope movement trends and assessing the probabilities of landslide occurrences. Experimental outcomes validate that Fusionformer outperforms existing forecasting methods, while the FRA framework provides valuable insights and practical guidance for real-world applications.

This work addresses the problem of recursive state estimation for networked control systems with unknown nonlinearities and binary-encoding mechanisms (BEMs). To enhance transmission reliability and reduce network resource consumption, BEMs are used to convert measurement signals into binary bit strings (BBSs) of limited length, which are then transmitted to the estimator through noisy communication channels. During transmission, random bit errors may occur in the BBSs due to channel noise. For the considered nonlinear networked control systems affected by random bit errors, a neural-network (NN)-based recursive estimation strategy is proposed, where an NN with a time-varying tuning scalar is employed to approximate the unknown nonlinearity of the networked control systems. By using the proposed strategy, the upper bounds of the estimation error of the system state and the trace of the estimation error of the NN weight (NNW) are first derived. These bounds are then minimized by recursively designing both the estimator gain matrix and the tuning scalar of the NNW. Finally, the effectiveness of the proposed estimation strategy is demonstrated through a numerical example.

This article is concerned with the distributed set-membership fusion estimation problem for a class of artificial neural networks (ANNs), where the dynamic event-triggered mechanism (ETM) is utilized to schedule the signal transmission from sensors to local estimators to save resource consumption and avoid data congestion. The main purpose of this article is to design a distributed set-membership fusion estimation algorithm that ensures the global estimation error resides in a zonotope at each time instant and, meanwhile, the radius of the zonotope is ultimately bounded. By means of the zonotope properties and the linear matrix inequality (LMI) technique, the zonotope restraining the prediction error is first calculated to improve the prediction accuracy and subsequently, the zonotope enclosing the local estimation error is derived to enhance the estimation performance. By taking into account the side-effect of the order reduction technique (utilized in designing the local estimation algorithm) of the zonotope, a sufficient condition is derived to guarantee the ultimate boundedness of the radius of the zonotope that encompasses the local estimation error. Furthermore, parameters of the local estimators are obtained via solutions to certain bilinear matrix inequalities. Moreover, the zonotope-based distributed fusion estimator is obtained through minimizing certain upper bound of the radius of the zonotope (that contains the global estimation error) according to the matrix-weighted fusion rule. Finally, the effectiveness of the proposed distributed fusion estimation method is illustrated via a numerical example.

This article is concerned with the secure state estimation problem for artificial neural networks (ANNs) subject to unknown-but-bounded noises, where sensors and the remote estimator are connected via open and bandwidth-limited communication networks. Using the encoding-decoding mechanism (EDM) and the Paillier encryption technique, a novel homomorphic encryption scheme (HES) is introduced, which aims to ensure the secure transmission of measurement information within communication networks that are constrained by bandwidth. Under this encoding-decoding-based HES, the data being transmitted can be encrypted into ciphertexts comprising finite bits. The emphasis of this research is placed on the development of a secure set-membership state estimation algorithm, which allows for the computation of estimates using encrypted data without the need for decryption, thereby ensuring data security throughout the entire estimation process. Taking into account the unknown-but-bounded noises, the underlying ANN, and the adopted HES, sufficient conditions are determined for the existence of the desired ellipsoidal set. The related secure state estimator gains are then derived by addressing optimization problems using the Lagrange multiplier method. Lastly, an example is presented to verify the effectiveness of the proposed secure state estimation approach.

This article is concerned with the proportional-integral-observer (PIO)-based fusion estimation problem for a class of artificial neural networks (ANNs) equipped with multiple sensors, which are constrained by bandwidth and subjected to unknown-but-bounded noises (UBBNs). For the purpose of efficient information communication, an approach known as the one-bit encoding mechanism (OBEM) is proposed that enables the encoding of scalar data using merely a single bit. Then, a local PIO-based set-membership estimator is devised for each sensor node, with the aim of achieving the desired estimation task while considering the possible data distortion due to OBEM and the existence of UBBNs. Subsequently, sufficient conditions are established to ensure the existence and effectiveness of the PIO-based set-membership estimator. Moreover, to enhance the global estimation performance, an ellipsoid-based fusion rule is introduced for all local PIO-based set-membership estimators. The performance of fusion estimation is then analyzed using set theory and the optimization method, leading to the determination of relevant parameters. Finally, the effectiveness and advantages of the proposed estimation algorithm are demonstrated through a simulation example.

In this article, we address the design issue of the proportional-integral observer (PIO) for partially state-saturated systems, which are affected by probabilistic quantizations and an accumulation-based event-triggered mechanism (ABETM). A comprehensive model is established to characterize partial state saturations, wherein only a part of the state variables are saturated while the remaining variables maintain normal conditions. To conserve communication resources, an ABETM is employed to determine the release of system measurements to the PIO. Before transmission over the communication network, the triggered signal is quantized through a probabilistic quantization mechanism. The goal of this article is to devise a PIO that guarantees ultimate boundedness of the estimation error dynamics in the mean square. Initially, a sufficient condition is derived to ensure that the estimation error dynamics are exponentially ultimately bounded in the mean square. Following this, necessary PIO gains are identified by solving specific matrix inequalities, and the efficacy of this proposed PIO approach is validated using a three-tank system simulation.

This article investigates the recursive state estimation problem for a class of nonlinear cyber-physical systems (CPSs) operating under a token bucket strategy regulated by a random access protocol (RAP). Communication between sensor nodes and the remote estimator takes place over a shared network, where only one sensor node is permitted to access the network at each time instant to prevent data collisions. The transmission sequence of sensor nodes is governed by RAP scheduling, which is modeled as a sequence of independent and identically distributed variables representing the selected node granted network access. To efficiently manage limited communication resources, a token bucket strategy is employed. The measurement signal from the selected node is transmitted to the estimator only if a sufficient number of tokens are available in the bucket to meet the required token consumption. The objective is to design a state estimation algorithm that minimizes the estimation error covariance (EEC) by appropriately determining the estimator gain at each time step. The desired estimator gain is computed recursively by solving two Riccati-like difference equations. Finally, an illustrative example is presented to validate the effectiveness of the proposed estimation method.

This article examines the nonfragile bipartite tracking consensus issue in the context of sampled-data nonlinear multiagent systems (MASs) undergoing denial-of-service (DoS) attacks and control gain fluctuations, where both cooperative and competitive interactions between the agents over the network are taken into account. During the DoS attacks, with communication services being denied, a halt in data transmission among the agents is experienced, which might result in performance degradation, undesirable oscillatory behavior, or even hinders the agents from performing their intended tasks. Consequently, there emerges a pressing requirement for the analysis and design of a secure bipartite tracking consensus protocol for MASs under the threat of DoS attacks. In pursuit of this goal, a modified Halanay-like inequality is initially established, which provides a basis for us to derive certain sufficient conditions ensuring the MASs to achieve the bipartite tracking consensus, despite the disruptive presence of malicious DoS attacks. Additionally, the control gain matrix can be readily computed by resolving a collection of linear matrix inequalities. For specific scenarios that demand reduced computational complexity, the matrix decoupling method is introduced, enabling a reduction in the dimensionality of the matrix inequalities and, consequently, facilitating its straightforward application to large-scale MASs. This article culminates in a numerical simulation, which is performed to validate the developed results.

This article is concerned with the privacy preservation problem for scaled consensus in nonlinear multiagent systems (MASs) through sampled data. In scaled consensus, the aim is for agents’ states to achieve specified proportions rather than converging to a single value, and this approach encompasses standard consensus, bipartite consensus, and cluster consensus within its framework. To prevent the leakage of sensitive data during communication, a novel privacy-preserving scaled distributed protocol is proposed. This protocol uses homomorphic encryption, whereby agents initially encrypt their information and transmit it in ciphertext form to neighboring agents. The control protocol is then reconstructed by the agents using the encrypted information received from their neighbors. A modified Halanay-like inequality is formulated and, by leveraging algebraic graph theory and the Lyapunov stability theorem, sufficient conditions are established to ensure that the MASs can achieve exponentially ultimately bounded scaled consensus. Furthermore, a convex optimization method is adopted to identify the optimal control gain so as to maximize the allowable sampling interval bound. The theoretical results are substantiated through a numerical simulation.

Dynamic state estimation (DSE) plays a vitally important role in modern power systems, and the reliance on the communication network often render the systems to cyber-threats. This paper investigates the secure DSE problem for the multi-generator power grids in the presence of randomly occurring cyber-attacks. To facilitate the decentralized DSE, the synchronous generator is decoupled form the large-scale interconnected power grid with the aid of model decoupling method. A hybrid cyber-attack model, which includes three typical and representative attacks (i.e., denial-of-service attacks, bias injection attacks and replay attacks), is designed and launched in a random way. Attention is devoted to the secure algorithm design problem to light the negative impacts on the DSE performance from the nonlinearity/non-Gaussianity and the random occurrences of the cyber-attacks. Specifically, i) a likelihood function modification method is established where the knowledge of the hybrid-attack model is fully considered; and ii) the associated weights of the particles are updated according to the proposed likelihood function to resist the impacts caused by the randomly occurring cyber-attacks. Finally, simulation experiments with four scenarios are implemented on the IEEE 39-bus system and the corresponding analyses show the validity of the decentralized secure DSE scheme.

In this paper, a novel outlier detection method is proposed for industrial data analysis based on the fuzzy C-means (FCM) algorithm. An adaptive switching randomly perturbed particle swarm optimization algorithm (ASRPPSO) is put forward to optimize the initial cluster centroids of the FCM algorithm. The superiority of the proposed ASRPPSO is demonstrated over five existing PSO algorithms on a series of benchmark functions. To illustrate its application potential, the proposed ASRPPSO-based FCM algorithm is exploited in the outlier detection problem for analyzing the real-world industrial data collected from a wire arc additive manufacturing pilot line in Sweden. Experimental results demonstrate that the proposed ASRPPSO-based FCM algorithm outperforms the standard FCM algorithm in detecting outliers of real-world industrial data.Note to Practitioners—Electric arc (which is governed by the current and arc voltage) plays a significant role in monitoring the operating status of the wire arc additive manufacturing (WAAM) process. The nominal periodic current and voltage may occasionally change abruptly due to anomalies (such as arc instability, unstable metal transfer, geometrical deviations, and surface contaminations), which would affect the quality of the fabricated component. This paper focuses on detecting possible anomalies by analyzing the current and voltage during the WAAM process. A novel clustering-based outlier detection method is proposed for anomaly detection where abnormal and normal instances are categorized into two separate clusters. A new particle swarm optimization algorithm is put forward to optimize the initial cluster centroid so as to improve the detection accuracy. The proposed outlier detection method is applied to real-world data collected from a WAAM pilot line for detecting abnormal instances. Experimental results demonstrate the effectiveness of the proposed outlier detection method. The proposed outlier detection method can be applied to other industrial applications including electrical engineering, mechanical engineering and medical engineering. In the future, we aim to develop an online outlier detection system based on the proposed method for real-time for anomaly detection and defect prediction.

This paper is concerned with the contact force estimation problem of robot manipulators based on imperfect dynamic models of the manipulator and the contact force. To handle the imperfect dynamic information of the manipulator, a hybrid model, consisting of the nominal model and the residual dynamics, is established for the manipulator, and the Gaussian process regression (GPR) technique is employed to learn the mean and covariance of the residual dynamics. On this basis, a virtual measurement equation is established for contact force estimation and a Gaussian process adaptive disturbance Kalman filter (GPADKF) is developed where the variational Bayes technique is employed to achieve online identification of the noise statistics in the force dynamics. The GPADKF is capable of decoupling the contact force from residual dynamics and system noises, thereby reducing the dependence on accurate dynamic models of the manipulator and the contact force. Simulation and experimental results demonstrate that the proposed scheme outperforms the state-of-art methods.Note to Practitioners—Contact force estimation for robot manipulators can be achieved by fusing the dynamic models of the manipulator and the contact force. When both models are imprecise, the traditional inverse dynamics-based and disturbance Kalman filter-based approaches can no longer provide accurate force estimates. To handle this challenge, a computationally efficient hybrid dynamic model is established for the manipulator, which consists of the nominal model and a residual dynamics compensation term learned from offline data via the GPR. On this basis, an adaptive disturbance Kalman filter is constructed by using the variational Bayes technique to deal with the inaccurate noise covariance matrix in the force dynamic model. Compared with the existing approaches, the force estimate obtained via the proposed scheme is more accurate and reliable, as refined noise covariance matrices (provided by both the GPR and the variational Bayes procedure) have been adopted in the Kalman gain calculation. The proposed GPADKF method is the extension of the composite disturbance filtering (CDF) framework. With the proposed scheme, the dependency on the perfect dynamic models in contact force estimation can be significantly reduced, and this makes our approach especially suitable for contact force estimation problems under unfamiliar and complicated environments.

In this article, a novel dynamic multiobjective optimization algorithm (DMOA) is proposed based on a designed hierarchical response system (HRS). Named HRS-DMOA, the proposed algorithm mainly aims at integrating merits from the mainstream ideas of dynamic behavior handling (i.e., the diversity-, memory-, and prediction-based methods) in order to make flexible responses to environmental changes. In particular, by two predefined thresholds, the environmental changes are quantified as three levels. In case of a slight environmental change, the previous Pareto set-based refinement strategy is recommended, while the diversity-based reinitialization method is applied in case of a dramatic environmental change. For changes occurring at a medium level, the transfer-learning-based response is adopted to make full use of the historical searching experiences. The proposed HRS-DMOA is comprehensively evaluated on a series of benchmark functions, and the results show an improved comprehensive performance as compared with four popular baseline DMOAs in terms of both convergence and diversity, which also outperforms other two state-of-the-art DMOAs in ten out of 14 testing cases, exhibiting the competitiveness and superiority of the algorithm. Finally, extensive ablation studies are carried out, and from the results, it is found that as compared with randomly selecting the response methods, the proposed HRS enables more reasonable and efficient responses in most cases. In addition, the generalization ability of the proposed HRS as a flexible plug-and-play module to handle dynamic behaviors is proven as well.

In this article, the ultimately bounded synchronization problem is investigated for a class of discrete-time stochastic complex networks under the pinning control strategy. Communication between system nodes and the remote controller is facilitated via wireless networks subject to bit rate constraints. The system model is distinguished by the inclusion of randomly occurring nonlinearities. A coding-decoding transmission mechanism under constrained bit rates is introduced to characterize the digital transmission process. To achieve synchronization of the network nodes with the unforced target node, a pinning controller is specifically devised based on the information from partially selected nodes. Through the application of the stochastic analysis method, a sufficient condition is derived for ensuring the mean-square boundedness of the synchronization error system. In addition, an optimization algorithm is introduced to address bit rate allocation and the design of desired controller gains. Within the presented theoretical framework, the correlation between the mean-square synchronization performance and bit rate allocation is further elucidated. To conclude, a simulation example is provided to substantiate the efficacy of the recommended pinning control approach.

In this article, the remote estimation problem is addressed for a class of discrete-time complex networks under the influence of probabilistic quantization and amplify-and-forward (AF) relays. The underlying complex network model, which is inherently nonlinear and stochastic, is affected by additive process and measurement noises. Owing to the limited bandwidth of the transmission channel, the measurement outputs are quantized by a probabilistic quantizer prior to transmission. To enhance the signal quality over long-distance transmissions, the quantized measurements are sent to AF relays and subsequently forwarded to the estimator. Utilizing the unscented Kalman filter approach, a novel state estimator is designed to minimize an upper bound on the estimation error covariance. Moreover, sufficient conditions are derived to ensure that the estimation error is exponentially bounded in the mean-square sense. Lastly, the efficacy of the proposed scheme is illustrated through numerical simulations.

This article investigates the sliding mode control (SMC) problem for a class of uncertain 2-D systems described by the Roesser models with a bounded disturbance. In order to reduce the communication usage between the controller and the actuators, it is supposed that only one actuator node can gain the access to the network at each sampling time along horizontal or vertical direction, where a proper 2-D round-robin protocol is designed to periodically regulate the access token and a set of zero-order holders (ZOHs) is employed to keep the other actuator nodes unchanged until the next renewed signal arrives. Based on a novel 2-D common sliding function, a token-dependent 2-D SMC scheme with first-order sliding mode is appropriately constructed to cope with the impacts from the periodic scheduling signal and the ZOHs. Furthermore, a novel super-twisting-like 2-D SMC scheme with second-order sliding mode is designed to improve the robustness against the bounded disturbance. By resorting to token-dependent Lyapunov-like function, sufficient conditions are obtained to guarantee the ultimate boundedness of the horizontal and vertical states as well as the 2-D common sliding function. For acquiring the optimized gain matrices, two searching algorithms are formulated to solve two optimization problems arising from finding optimized control performance. Finally, two comparative examples are exploited to demonstrate the effectiveness and the advantageous of the proposed first- and second-order 2-D SMC design schemes under round-robin scheduling mechanism.

This article deals with the distributed proportional–integral state estimation problem for nonlinear systems over sensor networks (SNs), where a number of spatially distributed sensor nodes are utilized to collect the system information. The signal transmissions among different sensor nodes are realized via their individual channels subject to energy-constrained Denial-of-Service (EC-DoS) cyber-attacks launched by the adversaries whose aim is to block the nodewise communications. Such EC-DoS attacks are characterized by a sequence of attack starting time-instants and a sequence of attack durations. Based on the measurement outputs of each node, a novel distributed fuzzy proportional–integral estimator is proposed that reflects the topological information of the SNs. The estimation error dynamics is shown to be regulated by a switching system under certain assumptions on the frequency and the duration of the EC-DoS attacks. Then, by resorting to the average dwell-time method, a unified framework is established to analyze the dynamical behaviors of the resultant estimation error system, and sufficient conditions are obtained to guarantee the stability as well as the weighted$H_{\infty}$performance of the estimation error dynamics. Finally, a numerical example is given to verify the effectiveness of the proposed estimation scheme.

This article focuses on the recursive filtering problem for networked time-varying systems with randomly occurring measurement outliers (ROMOs), where the so-called ROMOs denote a set of large-amplitude perturbations on measurements. A new model is presented to describe the dynamical behaviors of ROMOs by using a set of independent and identically distributed stochastic scalars. A probabilistic encoding-decoding scheme is exploited to convert the measurement signal into the digital format. For the purpose of preserving the filtering process from the performance degradation induced by measurement outliers, a novel recursive filtering algorithm is developed by using the active detection-based method where the "problematic" measurements (i.e., the measurements contaminated by outliers) are removed from the filtering process. A recursive calculation approach is proposed to derive the time-varying filter parameter via minimizing such the upper bound on the filtering error covariance. The uniform boundedness of the resultant time-varying upper bound is analyzed for the filtering error covariance by using the stochastic analysis technique. Two numerical examples are presented to verify the effectiveness and correctness of our developed filter design approach.

In this article, a novel adaptive robust predefined-time nonsingular sliding mode control (ARPTNSMC) scheme is investigated, which aims to achieve fast and accurate tracking control of a quadrotor subjected to external disturbance. Inspiration is drawn from a fuzzy neural network that is constructed by fuzzy logic and zeroing neural network (ZNN). Distinct from most sliding mode control approaches, two nonsingular sliding mode surfaces are formulated by employing general ZNN approaches and differentiable predefined-time activation functions. Furthermore, for the compensation of external disturbance, a dynamic adaptive parameter and a fuzzy adaptive parameter are designed in the attitude control law. The fuzzy adaptive parameter, generated by the Takagi–Sugeno fuzzy logic system, is incorporated to enhance the robustness while reducing the chattering phenomena resulting from the discontinuous sign function. Theoretical proofs are provided to demonstrate the predefined-time convergence and robustness of the closed-loop system. Finally, two trajectory tracking examples are offered to validate the convergence, robustness, and low-chattering characteristics of the closed-loop system under the developed ARPTNSMC scheme.

In this article, a novel dynamic multiobjective optimization algorithm (DMOA) with a cascaded fuzzy system (CFS) is developed, which aims to promote objective knowledge transfer from an innovative perspective of comprehensive information characterization. This development seeks to overcome the bottleneck of negative transfer in evolutionary transfer optimization (ETO)-based algorithms. Specifically, previous Pareto solutions, center- and knee-points of multisubpopulation are adaptively selected to establish the source domain, which are then assigned soft labels through the designed CFS, based on a thorough evaluation of both convergence and diversity. A target domain is constructed by centroid feed-forward of multisubpopulation, enabling further estimations on learning samples with the assistance of the kernel mean matching (KMM) method. By doing so, the property of nonindependently identically distributed data is considered to enhance efficient knowledge transfer. Extensive evaluation results demonstrate the reliability and superiority of the proposed CFS-DMOA in solving dynamic multiobjective optimization problems, showing significant competitiveness in terms of mitigating negative transfer as compared to other state-of-the-art ETO-based DMOAs. Moreover, the effectiveness of the soft labels provided by CFS in breaking the “either/or” limitation of hard labels is validated, facilitating a more flexible and comprehensive characterization of historical information, thereby promoting objective and effective knowledge transfer.

In this paper, we investigate the so-called$H_{\infty }$fuzzy proportional-integral observer (PIO) design problem for a class of nonlinear systems subject to relay effects, data missing, and multi-rate measurements. The considered multi-rate phenomenon is defined as the employment of sensors with diverse sampling periods due to specific engineering requirements. During the long transmission from multi-rate sensors to the remote fuzzy observer, the amplify-and-forward relay scheme is utilized to facilitate data communication, in which the measurement outputs are first directed to the relay nodes and then sent to the observer side. A unified model, incorporating both fast and slow sampling, is described using the switching system method. Subsequently, a fuzzy PIO is proposed by utilizing estimated premise variables along with current and historical system information. Through the application of stochastic analysis theory, the error dynamics of the state estimation is examined, and the observer gain matrices are determined by solving a specific convex optimization problem. Ultimately, two simulation experiments are conducted to validate the efficacy and utility of the formulated fuzzy PIO.

In this article, the problem of observer-based fuzzy proportional–integral–derivative (PID) tracking control is studied for networked nonlinear systems subject to protocol constraints and norm-bounded noises. The nonlinear plant under consideration is represented by an affine fuzzy model with immeasurable premise variables. The utilization of the try-once-discard protocol is proposed for information exchange between sensors and the controller, in order to mitigate the data transmission burden. An observer-based PID controller is put forward to achieve the desired tracking task and handle immeasurable premise variables, with sufficient consideration given to both available measurements and the reference trajectory. By analyzing the dynamics of the controlled tracking error system through the construction of a piecewise Lyapunov-like functional, the exponential ultimate boundedness of the tracking error dynamics is ensured. The controller parameters are designed using convex optimization technique and matrix theory, such that the tracking error dynamics is exponentially ultimately bounded. Finally, the validity and merits of the developed controller design method are verified through a simulation example.

This article addresses the problem of observer-based fuzzy proportional-integral-derivative (PID) control for a class of nonlinear systems subject to degraded measurements and randomly perturbed sampling periods (RPSPs). In the existing results, the degraded measurements and RPSPs are handled separately, where the sampling of different sensors is usually assumed to be synchronous. In our work, a comprehensive model is built to reflect the joint effects of degraded measurements and RPSPs by using a series of stochastic variable sequences and a set of Markov processes. In this model, the sampling periods of each sensor are allowed to be diverse, time-varying, and randomly perturbed, thereby fully capturing the environmental effects and device constraints. Different from the existing literature that uses proportional type controllers, an observer-based fuzzy PID controller with a modified structure is proposed, which fully utilizes the system information. To overcome the difficulties of the incomplete measurement information, some auxiliary variables related to the sampling periods are introduced under which the measurement output is transformed into a form delayed with stochastic delays. Subsequently, by using the special variable separation and inequality technique, sufficient conditions are derived to ensure the exponentially ultimate boundedness of the closed-loop system in the mean-square sense. The desired gains for the observer and PID controller are obtained through the solution of an optimization problem. Last, the effectiveness of the developed approach is demonstrated through simulation examples.

This article is concerned with the privacy-preserving distributed economic dispatch problem of microgrids. The main goal of this work is to develop a privacy-preserving distributed optimization algorithm over directed networks, aiming to achieve supply-demand balance at the lowest economic cost under practical constraints while preventing the leakage of power-sensitive information. For this purpose, a distributed optimization algorithm with aconstantstep size is proposed by combining the decentralized exact first-order algorithm with the push-sum protocol, which offers an advantage in terms of fast convergence. In addition, to ensure privacy preservation, a state-decomposition approach is employed by randomly dividing the state into two parts, where only partial state information is transmitted. Moreover, the effectiveness of the privacy-preserving scheme against honest-but-curious nodes and external eavesdroppers is demonstrated through rigorous analysis. Finally, simulation studies demonstrate the validity and superiority of the developed privacy-preserving distributed algorithm.

This article is concerned with the secure distributed economic dispatch (DED) problem of microgrids. A quantized distributed optimization algorithm using the Paillier encryption–decryption scheme is developed. This algorithm is designed to optimally coordinate the power outputs of a collection of distributed generators (DGs) in order to meet the total load demand at the lowest generation cost under the DG capacity limits while ensuring communication efficiency and security. First, to facilitate data encryption and reduce data release, a novel dynamic quantization scheme is integrated into the DED algorithm, through which the effects of quantization errors can be eliminated. Next, utilizing matrix norm analysis and mathematical induction, a sufficient condition is provided to demonstrate that the developed DED algorithm converges precisely to the optimal solution under finite quantization levels (and even the three-level quantization usingsigntransmissions). Moreover, an encryption–decryption scheme is developed based on quantized outputs, which ensures confidential communication by leveraging the homomorphic property of the Paillier cryptosystem. Finally, the effectiveness and superiority of the implemented secure distributed algorithm are confirmed through a simulated example.

In this article, the state estimation problem is investigated for permanent magnet synchronous motors with sensor degradations under the encoding–decoding scheme. To reduce the network communication burden and enhance data transmission security, a uniform quantization-based encoding–decoding strategy is introduced in the sensor-to-estimator channel, which allows the transmitted power signals to be converted into digital format. Furthermore, the sensor degradation is represented by using a set of independent stochastic variables obeying uniform distributions. The primary objective of this article is to design a recursive state estimation algorithm such that, in the presence of sensor degradations and encoding–decoding strategy, a minimal upper bound on the estimation error covariance is derived by designing an appropriate estimator gain matrix. Simulation experiments for permanent magnet synchronous motors are conducted to validate the efficacy of the proposed recursive state estimation algorithm.

A practical yet challenging scenario in transfer learning is unsupervised domain adaptation (UDA), where knowledge is transferred from a labeled source domain to unlabeled target domains. The crucially important role of domain-variant characteristics is often neglected by most existing UDA methods, which can deteriorate adaptation performance and result in negative transfer. In this article, an optimal unsupervised domain adaptation (OUDA) algorithm is proposed in order to address this issue, which balances the invariance of domain-sharing features and the variance of domain-specific features. In the proposed approach, a gradient adversarial adaptation (GAA) method is introduced to align the gradient directions of source and target features within the same category, thereby facilitating knowledge transfer. In addition, a local manifold embedding (LME) technique is proposed to preserve the intrinsic geometric structure of the original feature space while implementing distribution alignment, providing distinguishable features for UDA. To stabilize the process of knowledge transfer, an evolutionary control strategy is developed to adaptively control the tradeoff between the GAA and LME by employing the particle swarm optimization algorithm. Extensive experiments are conducted on cross-domain natural gas pipeline fault diagnosis, and the results on nine cross-domain classification tasks indicate that our OUDA algorithm outperforms the existing state-of-the-art UDA methods. Moreover, the performance analysis in terms of accuracy, loss, and domain divergence demonstrates the superior stability of the proposed OUDA algorithm in dealing with unsupervised knowledge transfer.

This article is concerned with domain generalization (DG), a practical yet challenging scenario in transfer learning where the target data are not available in advance. The key insight of DG is focused on learning a robust model that can generalize to the unseen domain by leveraging knowledge from the source domain. To this end, we propose a novel algorithm known as support-sample-assisted Adversarial Attacks (SSAA) for DG. In the SSAA algorithm, an attack–defense strategy is deployed to enhance the target model's generalizability and transferability. This strategy includes a nontargeted attack stage, during which attack samples are generated to form pseudotarget domains with near-realistic covariate shifts. Subsequently, in the model defense stage, a biclassifier structure is used to distinguish support samples from the generated attack samples. These support samples form a new decision boundary encompassing all unseen samples, prompting an extension of the existing decision boundary to meet these samples. Experimental results on cross-domain fault diagnosis tasks suggest that SSAA outperforms current state-of-the-art DG methods, indicating a promising avenue for further DG development.

Data augmentation (DA) has the potential to address the issue of imbalanced and insufficient datasets (I&ID) in pipeline fault diagnosis. However, the majority of existing DA methods for time series are inspired by computer vision techniques, ignoring the temporal dynamic properties and fine-grained fault features, which leads to limited performance of the augmentation. To tackle this problem, we introduce a novel DA approach called the subdomain-alignment adversarial self-attention network (SA-ASN), which takes into account both temporal association and semantic correlation. Our approach features a novel temporal association learning (TAL) mechanism, which transfers temporal information from the discriminator to the generator via a customized knowledge-sharing structure, improving the reliability of synthetic long-range associations. Additionally, we introduce a prototype-assisted subdomain alignment (PASA) strategy that forms a hierarchical structure in the synthetic dataset by incorporating local semantic correlation into the model training. With the support of TAL and PASA, our SA-ASN algorithm enhances the authenticity of temporal structure at the instance level and improves the discriminability of fault features at the category level. Our experimental results show that the SA-ASN algorithm provides a more diverse and accurate augmentation of pipeline data. The effectiveness of our SA-ASN algorithm encourages the use of data-driven diagnostic models in complex real-world oilfield pipeline networks.

Multimedia images have become an important way for the sharing of digital information. However, advanced editing tools provide easy methods for malicious content modification, resulting in less viable information. Therefore, there is an urgent need to design an algorithm for image tampering detection and localization, which is capable of locating the authenticity region of the received image, thus delivering the accurate information and assisting in correct decision making in industries and other fields. In this article, a novel dual-pathway multiscale network (DPMSN) is proposed for the image forgery detection, which mainly focuses on extracting the edge information. In particular, a dual-pathway structure is deployed to align visual features in red, green and blue (RGB) space and edge information in LAB space, where a coarse prediction mask is generated to promote accurate localization of the forged regions. By applying the variation convolution operators, comprehensive attention can be paid to various forged regions in multiple sizes. Moreover, in the multiscale fusion module, features at different stages and other low-level information are sufficiently fused to realize a robust presentation of the forged regions. Experimental results show the effectiveness of DPMSN as compared with other state-of-the-art image forgery detection models and the great robustness when facing image attacks, which means DPMSN is a trustworthy forgery detection approach in the industrial field.

This brief is concerned with the problem of kernel adaptive filtering for a complex network. First, a coupled kernel least mean square (KLMS) algorithm is developed for each node to uncover its nonlinear measurement function by using a series of input-output data. Subsequently, an upper bound is derived for the step-size of the coupled KLMS algorithm to guarantee the mean square convergence. It is shown that the upper bound is dependent on the coupling weights of the complex network. Especially, an optimal step size is obtained to achieve the fastest convergence speed and a suboptimal step size is presented for the purpose of practical implementations. Besides, a coupled kernel recursive least square (KRLS) algorithm is further proposed to improve the filtering performance. Finally, simulations are provided to verify the validity of the theoretical results.

In this article, a novel multi-strategy adaptive selection-based dynamic multiobjective optimization algorithm (MSAS-DMOA) is proposed, which adopts the non-inductive transfer learning (TL) paradigm to solve dynamic multiobjective optimization problems (DMOPs). In particular, based on a scoring system that evaluates environmental changes, the source domain is adaptively constructed with several optional groups to enrich the knowledge. Along with a group of guide solutions, the importance of historical experiences is estimated via the kernel mean matching (KMM) method, which avoids designing strategies to label individuals. The proposed MSAS-DMOA is comprehensively evaluated on 14 DMOPs, and the results show an overwhelming performance improvement in terms of both convergence and diversity as compared with other four popular DMOAs. In addition, ablation studies are also conducted to validate the superiority of the applied strategies in MSAS-DMOA, which can effectively alleviate the negative transfer phenomenon. Without the conventional labeling procedure, the proposed method also yields satisfactory results, which can provide valuable reference for designing other evolutionary transfer optimization (ETO) algorithms.

This article is concerned with the state estimation problem for a class of complex networks (CNs) with uncertain inner couplings and packet losses over communication networks. The inner couplings are allowed to be uncertain and varying in a specific interval. The amplify-and-forward (AaF) relay protocols are introduced to improve the communication quality and enhance the propagation distance. The Bernoulli random variables are used to characterize the randomly occurring packet losses encountered in communication channels. The focus of this article is on the design of a state estimator for each node of CNs such that a prescribed performance constraint is satisfied for the dynamical error system over a finite horizon. A sufficient condition is first provided to verify the existence of the desired state estimator, and the estimator gain is then determined by solving two coupled backward Riccati difference equations (RDEs). Subsequently, a recursive state estimation algorithm is put forward that is suitable for online computation. Finally, a numerical example is given to demonstrate the effectiveness of the proposed estimation method.

In this brief, the state estimation problem is investigated for a class of randomly delayed artificial neural networks (ANNs) subject to probabilistic saturation constraints (PSCs) and non-Gaussian noises under the redundant communication channels. A series of mutually independent Bernoulli distributed white sequences are introduced to govern the random occurrence of the time delays, the saturation constraints, and the transmission channel failures. A comprehensive redundant-channel-based communication mechanism is constructed to attenuate the phenomenon of packet dropouts so as to enhance the quality of data transmission. To compensate for the influence of randomly occurring time delays, the corresponding occurrence probability is exploited in the process of particle generation. In addition, an explicit expression of the likelihood function is established based on the statistical information to account for the impact of PSCs and redundant channels. By virtue of the modified operations of particle propagation and weight update, a particle-filter-based state estimation algorithm is proposed with mild restriction on the system type. Finally, an illustrative example with Monte Carlo simulations is provided to demonstrate the effectiveness of the developed state estimation scheme.

This article is concerned with the maximum correntropy filtering (MCF) problem for a class of nonlinear complex networks subject to non-Gaussian noises and uncertain dynamical bias. With aim to utilize the constrained network bandwidth and energy resources in an efficient way, a componentwise dynamic event-triggered transmission (DETT) protocol is adopted to ensure that each sensor component independently determines the time instant for transmitting data according to the individual triggering condition. The principal purpose of the addressed problem is to put forward a dynamic event-triggered recursive filtering scheme under the maximum correntropy criterion, such that the effects of the non-Gaussian noises can be attenuated. In doing so, a novel correntropy-based performance index (CBPI) is first proposed to reflect the impacts from the componentwise DETT mechanism, the system nonlinearity, and the uncertain dynamical bias. The CBPI is parameterized by deriving upper bounds on the one-step prediction error covariance and the equivalent noise covariance. Subsequently, the filter gain matrix is designed by means of maximizing the proposed CBPI. Finally, an illustrative example is provided to substantiate the feasibility and effectiveness of the developed MCF scheme.

This article is concerned with a new partial-neurons-based proportional-integral observer (PIO) design problem for a class of artificial neural networks (ANNs) subject to bounded disturbances. For the purpose of improving the reliability of the data transmission, the multiple description encoding mechanisms are exploited to encode the measurement data into two identically important descriptions, and the encoded data are then transmitted to the decoders via two individual communication channels susceptible to packet dropouts, where Bernoulli-distributed stochastic variables are utilized to characterize the random occurrence of the packet dropouts. An explicit relationship is discovered that quantifies the influences of the packet dropouts on the decoding accuracy, and a sufficient condition is provided to assess the boundedness of the estimation error dynamics. Furthermore, the desired PIO parameters are calculated by solving two optimization problems based on two metrics (i.e., the smallest ultimate bound and the fastest decay rate) characterizing the estimation performance. Finally, the applicability and advantage of the proposed PIO design strategy are verified by means of an illustrative example.

In this article, the sequential fusion estimation problem is investigated for multirate complex networks (MRCNs) with uniformly quantized measurements. The process and measurement noises, which are unknown-yet-bounded (UYB), are restrained into a family of zonotopes, and the multiple sensors are allowed to have different sampling periods. To facilitate digital transmissions, the sensor measurements are uniformly quantized before being sent to the remote estimator. The purpose of this article is to design a sequential set-membership estimator such that, in the simultaneous presence of UYB noises, multirate samplings, and uniform quantization effects, the estimation error (after each measurement update) is confined to a zonotope with minimum F -radius at each time instant. By introducing certain virtual measurements, the MRCNs are first transformed into single-rate ones exhibiting a switching phenomenon. Then, by utilizing the properties of zonotopes, the desired zonotopes are derived, which contain the estimation error dynamics after each measurement update. Subsequently, the gain matrices of the sequential estimator are derived by minimizing the F -radii of these zonotopes, and the uniform boundedness is analyzed for the F -radius of the zonotope containing the estimation error after all measurement updates. Furthermore, sufficient conditions are derived to ensure the existence of the desired uniform upper/lower bounds. Finally, an illustrated example is proposed to show the effectiveness of the proposed sequential fusion estimation method.

This article investigates the privacy-preserving distributed economic dispatch (DED) problem of islanded microgrids. To improve the convergence rate of the DED algorithm, anacceleratedconsensus scheme is adopted by utilizing a short memory. Then, a privacy-preserving strategy is introduced to prevent sensitive information leakage by adding well-designed perturbations into the proposed consensus algorithm at the initial time instant. The primary objective of this article is to design a privacy-preserving accelerated consensus scheme to achieve a balance between supply and demand at the globally minimized cost while preserving the initial local demand information. By virtue of rigorous algebra manipulation and mathematical induction, a unified framework is established under which the convergence, the optimal convergence rate, and the optimality of the proposed DED algorithm are simultaneously analyzed, and the main results are extended to satisfy the privacy-preserving needs. Furthermore, the proposed privacy-preserving DED algorithm is shown to be resilient against both internal (honest-but-curious) and external eavesdroppers. Finally, the effectiveness of the developed privacy-preserving accelerated consensus algorithm is validated on the IEEE 39-bus power systems.

A new distributed filtering problem is fully studied in this article for time-varying systems over sensor networks under measurement errors and measurement censoring, where the measurement error is modeled as stochastic sensor bias driven by a dynamical equation and the measurement censoring is described by the Tobit measurement model. To reduce data congestion and transmission burden, a weighted try-once-discard protocol (WTODP) is applied to transmission channels to efficiently orchestrate data communication. The transmission priority is determined in a dynamical way depending on the importance of missions. The aim of this article is to construct an optimal distributed Tobit Kalman filter (TKF) such that filter parameters are rigorously determined in the minimum mean squared error sense under the consideration of the bias, censoring and WTODP effects. Specifically, sparsity of the network topology is comprehensively considered by using the novel matrix simplification technique. Furthermore, the resultant filtering error is ensured to be exponentially bounded in the mean squared sense. Finally, an illustrative example is used to show the applicability of the proposed filter.

This article is concerned with the distributed set-membership estimation problem for a class of discrete time-varying systems over binary sensor networks. For the binary sensors, the cases of fixed and time-varying thresholds are considered. In both the cases, the information useful for state estimation purposes is extracted by utilizing the crossings of binary measurements at two adjacent time instants, and then distributed estimators are constructed for each sensor node with the aid of the available measurements, where a set of vector saturation functions is introduced to resist the adverse effect of outliers during signal transmission. A novel distributed set-membership performance index is provided by averaging over the ellipsoidal constraints of all the sensor nodes, and the local performance analysis method is employed to establish sufficient criteria that guarantee the existence of desired estimators whose parameters are then derived for every node by recursively optimizing certain ellipsoids in the sense of matrix trace. The applicability and feasibility of the distributed set-membership schemes developed in this article are verified by two illustrative examples.

A latent factor analysis (LFA) model can be efficiently built via the stochastic gradient descent (SGD) algorithm to address high-dimensional and incomplete (HDI) data generated by various big data-related applications. However, its performance depends hugely on its hyperparameters, which is conventionally decided through the grid-search that yields extremely high-computational costs. Particle swarm optimization (PSO)-based hyperparameter adaptation provides a potential solution to this severe problem, while it leads to accuracy loss due to the untimely convergence of the PSO algorithm. Moreover, existing PSO extensions mostly perplex the evolution scheme to make the hyperparameter adaptation time-consuming and computationally expensive. Aiming at implementing an accurate and adaptive LFA model, a pseudo gradient-adjusted PSO (PGA-PSO) algorithm with twofold ideas is proposed: 1) modeling the position transitions in a PSO algorithm as the pseudo gradients for optimizing each particle’s position and 2) incorporating the principle of adaptive moment estimation into the pseudo gradient estimation to make it consider previous pseudo gradient information for addressing the untimely issue. With it, a PGA-PSO-incorporated LFA (PPL) model is successfully constructed. The empirical studies on six HDI datasets demonstrate that owing to the efficient hyperparameter adaptation implemented by the proposed PGA-PSO algorithm, the obtained PPL model surpasses state-of-the-art LFA models in accuracy when predicting an HDI matrix’s missing data. Hence, it satisfies the scalability and efficiency demands emerging from practical applications.

This article is concerned with the finite-horizon$H_{\infty}$state estimation problem for a specific class of nonlinear complex dynamical networks (CDNs) which are subject to random couplings and packet dropouts. The random coupling strengths among network nodes are characterized by a set of random variables with known statistical information. Three sequences of Bernoulli distributed random variables are utilized to model the packet dropouts over different communication channels. A decode-and-forward relay-based strategy is implemented to enhance the quality of communication by controlling the signal transmission in each sensor-to-estimator channel. The primary goal of this investigation is to create an appropriate state estimator for each node of the CDN, enabling the fulfillment of a specific$H_{\infty}$performance requirement for the estimation error dynamics over a finite horizon. Through the use of stochastic analysis techniques and matrix operations, a preliminary sufficient condition is given to meet the finite-horizon$H_{\infty}$performance requirement. The expected estimator gains are subsequently determined, which are defined in terms of the solutions to a series of recursive matrix inequalities. The effectiveness of the proposed relay-based estimation scheme is ultimately demonstrated through a numerical example.

This article addresses the privacy-preserving control issue for two-dimensional systems with probabilistic constraints. According to the exclusive or logical operation and the dynamic coding–decoding rule, a privacy-preserving mechanism (PPM) is developed, under which the transmitted data is efficiently compressed and encrypted into a ciphertext with finite bits. A PPM-based controller is designed that simultaneously guarantees a prescribed probabilistic constraint, mean-square boundedness, and privacy performance. Mathematical techniques, including mathematical induction, Chebyshev inequality, and matrix analysis, are employed to establish sufficient conditions for the presence of the desired controller gains. Additionally, the privacy and secrecy performance of the PPM is analyzed and simulation examples are presented to showcase the efficacy of the proposed controller design method.

This paper is concerned with the set-membership state estimation problem for power harmonics under quantization effects by using the dynamic event-triggered mechanism. The underlying system is subject to unknown but bounded noises that are confined to a sequence of zonotopes. The data transmissions are realized over a digital communication channel, where the measurement signals are quantized by a logarithmic-uniform quantizer before being transmitted from the sensors to the remote estimator. Moreover, a dynamic event-triggered mechanism is introduced to reduce the number of unnecessary data transmissions, thereby relieving the communication burden. The objective of this paper is to design a zonotopic set-membership estimator for power harmonics with guaranteed estimation performance in the simultaneous presence of 1) unknown but bounded noises, 2) quantization effects and 3) dynamic event-triggered executions. By resorting to the mathematical induction method, a unified set-membership estimation framework is established, within which a family of zonotopic sets is first derived that contains the estimation errors and, subsequently, the estimator gain matrices are designed by minimizing the$F$-radii of these zonotopic sets. The effectiveness of the proposed estimation scheme is verified by a series of simulation experiments.

In this article, the sliding mode control problem is addressed for a class of sampled-data systems subject to deception attacks. The sampling periods undergo component-wise random perturbations that are governed by a Markovian chain. The component of the sampled output is transmitted via an individual communication channel that is vulnerable to deception attacks, and Bernoulli-distributed stochastic variables are utilized to characterize the random occurrence of the deception attacks initiated by the adversaries. A sliding mode controller is designed to drive the state into the sliding domain around the specified sliding surface, and sufficient conditions are derived to guarantee the exponentially ultimate boundedness of the resultant closed-loop system in the mean-square sense. Furthermore, an optimization problem is established to pursue locally optimal control performance. Finally, a simulation example is given to verify the effectiveness and advantages of the developed controller design approach.

This article is concerned with the distributed state estimation problem over wireless sensor networks (WSNs), where each smart sensor is capable of harvesting energy from the external environment with a certain probability. The data transmission between neighboring nodes is dependent on the energy level of each sensor, and the internode communication is deemed as a failure when the current energy level is inadequate to guarantee the normal data transmission. Considering the intermittent information exchange over WSNs, a novel distributed state estimator is first constructed via introducing a set of indicator functions, and then the evolution of the probability distribution of energy level and its steady-state distribution is systematically discussed by resorting to the eigenvalue analysis approach and the mathematical induction. Furthermore, the optimal estimator gain is derived by minimizing the trace of the estimation error covariance under known communication sequences. In addition, the convergence of the minimized upper bound of the expected estimation error covariance is analyzed under any initial condition. Finally, an illustrative example regarding the target tracking problem is provided to verify the validity of the obtained theoretical results.

This article is concerned with the differentially private average consensus (DPAC) problem for a class of multiagent systems with quantized communication. By constructing a pair of auxiliary dynamic equations, a logarithmic dynamic encoding-decoding (LDED) scheme is developed and then utilized during the process of data transmission, thereby eliminating the effect of quantization errors on the consensus accuracy. The primary purpose of this article is to establish a unified framework that integrates the convergence analysis, the accuracy evaluation, and the privacy level for the developed DPAC algorithm under the LDED communication scheme. By means of the matrix eigenvalue analysis method, the Jury stability criterion, and the probability theory, a sufficient condition (with respect to the quantization accuracy, the coupling strength, and the communication topology) is first derived to ensure the almost sure convergence of the proposed DPAC algorithm, and the convergence accuracy and privacy level are thoroughly investigated by resorting to the Chebyshev inequality and ϵ -differential privacy index. Finally, simulation results are provided to illustrate the correctness and validity of the developed algorithm.

In this article, a new distributed filtering problem is studied for a class of state-saturated time-varying systems over sensor networks under measurement censoring, where the censored measurements are described by the Tobit measurement model. To curb the data collision and ease communication burden, a random access protocol (RAP) is implemented onto the sensor-to-filter channels to orchestrate the transmission sequence of multiple sensor nodes. The purpose of the addressed problem is to construct a state-saturated distributed filter such that upper bounds (on filtering error covariances) are guaranteed and filter parameters are determined to accommodate both measurement censoring and state saturation under the RAP. By means of matrix difference equations, the desired upper bounds are first acquired and later minimized through appropriately designing filter parameters. Particularly, the sparsity issue with respect to the network topology is tackled via the employing certain matrix simplification technique. A simulation example is finally presented to showcase the applicability of the proposed state-saturated distributed filtering algorithm.

This article is concerned with the influence maximization (IM) problem under a network with probabilistically unstable links (PULs) via graph embedding for multiagent systems (MASs). First, two diffusion models, the unstable-link independent cascade (UIC) model and the unstable-link linear threshold (ULT) model, are designed for the IM problem under the network with PULs. Second, the MAS model for the IM problem with PULs is established and a series of interaction rules among agents are built for the MAS model. Third, the similarity of the unstable structure of the nodes is defined and a novel graph embedding method, termed the unstable-similarity2vec (US2vec) approach, is proposed to tackle the IM problem under the network with PULs. According to the embedding results of the US2vec approach, the seed set is figured out by the developed algorithm. Finally, extensive experiments are conducted to: 1) verify the validity of the proposed model and the developed algorithms and 2) illustrate the optimal solution for IM under different scenarios with PULs.

With the rapid development of services computing in the past decade, Quality-of-Service (QoS)-aware selection of Web services has become a hot yet thorny issue. Conducting warming-up tests on a large set of candidate services for QoS evaluation is time consuming and expensive, making it vital to implement accurate QoS-estimators. Existing QoS-estimators barely consider the temporal patterns hidden in QoS data. However, such data are naturally time dependent. For addressing this critical issue, this study presents a Kalman-filter-incorporated latent factor analysis (KLFA)-based QoS-estimator for accurate representation to temporally dynamic QoS data. Its main idea is to make the user latent features (LFs) time dependent, while the service ones time consistent. A novel iterative training scheme is designed, where the user LFs are learned through a Kalman filter for precisely modeling the temporal patterns, and the service ones are alternatively trained via an alternating least squares algorithm for precisely representing the historical QoS data. Empirical studies on large-scale and real Web service QoS datasets demonstrate that the proposed KLFA model significantly outperforms state-of-the-art QoS-estimators in estimation accuracy for dynamic QoS data.

In this article, the proportional-integral observer design problem is studied for a class of multirate networked systems subject to constrained bit rate. The sensor sampling period is allowed to be different from the system updating period and, to facilitate the observer design, the underlying multirate system is cast into a general single-rate one by resorting to the lifting technique. In order to curb the communication burden and promote the data security, the encoding-decoding procedure is implemented on the sensor-to-observer channel to convert the measurement signals into binary codewords. A sufficient condition is first proposed to reveal the fundamental relationship between the bit-rate constraints and the decoding accuracy, and then the exponentially ultimate boundedness of the error dynamics is assessed with the aid of the Lyapunov method. Subsequently, the desired observer gains are determined by solving two optimization problems with the aim to achieve two distinct performance indices, namely, the smallest ultimate bound and the fastest decay rate. Finally, the validity of the developed observer design approach is thoroughly demonstrated via the simulation examples.

In this article, the distributed set-membership fusion filtering problem is investigated for a class of nonlinear 2-D shift-varying systems subject to unknown-but-bounded noises over sensor networks. The sensors are communicated with their neighbors according to a given topology through wireless networks of limited bandwidth. With the purpose of relieving the communication burden as well as enhancing the transmission security, a logarithmic-type encoding-decoding mechanism is introduced for each sensor node so as to encode the transmitted data with a finite number of bits. A distributed set-membership filter is designed to determine the local ellipsoidal set that contains the system state by only utilizing the data from the local sensor node and its neighbors, where the proposed filter scheme is truly distributed with desirable scalability. Then, a new ellipsoid-based fusion rule is developed for the designed set-membership filters in order to form the fused ellipsoidal set that has a globally smaller volume than all local ellipsoidal sets. With the aid of the mathematical induction technique, the set theory, and the convex optimization approach, sufficient conditions are derived for the existence of the desired distributed set-membership filters and the fusion weights. Then, the filter parameters and the fusion weights are acquired by solving a set of constrained optimization problems. Finally, an illustrative example is given to demonstrate the effectiveness of the proposed fusion filtering algorithm.

In this article, the tracking control problem is investigated for a type of linear networked systems subject to the round-Robin (RR) protocol scheduling and impulsive transmission outliers (ITOs). The communication between the controller and sensors is implemented through a shared network, on which the signal transmissions are scheduled by the RR protocol. The considered ITOs are modeled by a sequence of impulsive signals whose amplitudes (i.e., the norms of all impulsive signals) and interval lengths (i.e., the duration between all adjacent impulsive signals) are greater than two known thresholds, respectively. The occurrence moment for each ITO is first examined by using a certain outlier detection approach, and then a novel parameter-dependent tracking controller is proposed to protect the tracking performance from ITOs by removing the "harmful" signals (i.e., the transmitted signals contaminated by ITOs). Sufficient conditions are presented to ensure the exponentially ultimate boundedness of the resulted tracking error, and the controller gain matrices are subsequently designed by solving a constrained optimization problem. Finally, a simulation example is provided to demonstrate the effectiveness of our developed outlier-resistant tracking control scheme.

In this article, the security-guaranteed fuzzy networked state estimation issue is investigated for a class of two-dimensional (2-D) systems with norm-bounded disturbances. Considering the structural specificity of the 2-D systems, the membership function in the Takagi–Sugeno fuzzy model is established to reflect the spatial information. Multiple sensor arrays are utilized to improve the observation diversity and overcome the measurement obstacle induced by geographical restrictions. The network-based deception attacks, occurring in a probabilistic fashion, are characterized by a set of Bernoulli distributed random variables. By resorting to the 2-D fuzzy blending and augmentation operations, the error dynamics of the$s$th 2-D fuzzy estimator is formulated and, subsequently, theglobally asymptotical stabilityof the local error dynamics is studied in virtue of Lyapunov stability theory, fuzzy theory, and stochastic analysis technique. Then, sufficient conditions are derived to ensure the so-called$(\varrho _{1},\varrho _{2},\varrho _{3},\rho _{s})$-securityof the local error dynamics. Furthermore, the estimation fusion problem of the local fuzzy estimators is discussed and the corresponding$(\varrho _{1},\varrho _{2},\varrho _{3},\rho _{s})$-securityis also guaranteed. Finally, an illustrative example is provided to demonstrate the rationality and the effectiveness of the proposed state estimation algorithm.

This article investigates the ultimately bounded proportional–integral–derivative (PID) control problem for a class of discrete-time Takagi–Sugeno fuzzy systems subject to unknown-but-bounded noises and protocol constraints. The signal transmissions from sensors to the remote controller are realized via a communication network, where the FlexRay protocol is employed to flexibly schedule the information exchange. The FlexRay protocol is characterized by both the time- and event-triggered mechanisms, which are conducted in a cyclic manner. By using a piecewise approach, the measurement outputs affected by the FlexRay protocol are established based on a switching model. Then, a fuzzy PID controller is proposed with a concise and realizable structure. To evaluate the performance of the controlled system, a special time sequence is introduced that accounts for the behavior of the FlexRay protocol. Subsequently, a general framework is obtained to verify the boundedness of the closed-loop system, and then, the controller gains are designed by minimizing the bound of the concerned variables. Finally, a simulation study is conducted to validate the effectiveness of the developed control scheme.

In this article, the consensus control problem is investigated for mobile train lifting jack systems (TLJSs) of electric multiple units in the context of distributed industrial systems. First, a kind of global consensus controller is dedicatedly designed to deal with the underlying actuator limitation that is a typical phenomenon in mobile TLJSs and is referred to as actuator saturation. In order to better cater for the impact from the TLJSs, we consider the asymmetric actuator saturations (rather than their symmetric counterparts) whose side effects are later attenuated by means of a novel Lyapunov-function-based method. A series of experiments are conducted on mobile TLJSs so as to illustrate the effectiveness of the proposed consensus control algorithm.

This article addresses the particle filtering problem for a class of nonlinear/non-Gaussian systems with quantized measurements and multiple degrading sensors. A degradation variable described by the Wiener process is proposed to describe the phenomenon of sensor degradation that is often encountered in engineering practice. The measurement output of each sensor is quantized by a uniform quantizer before being sent to the remote filter. An augmented system is constructed, which aggregates the original system state and the degradation variables. In the presence of the sensor degradation and the quantization errors, a new likelihood function at the remote filter is calculated by resorting to all the transmitted measurements. According to the mathematical characterization of the likelihood function, a novel particle filtering algorithm is developed, where the parameters of both the degradation processes and the quantization functions are exploited to obtain the modified importance weights. Finally, the effectiveness of the proposed method is shown via a target tracking example with bearing measurements.

Chapter 2 : This chapter addresses the state estimation (SE) problems for renewable-electricity-generation (REG)-based microgrids, where the measurement outliers are also considered. Different from the traditional weighted least square or Kalman filter (KF)-based methods, which lack robustness to outliers and fail to adapt to microgrid dynamics, the chapter proposes a novel outlier-resistant recursive SE algorithm. Key innovations include embedding a saturation function to constrain the innovation term, mitigating adverse impacts from outlier-contaminated data without requiring prior knowledge of outlier characteristics (e.g. type, frequency, or probability). The algorithm also guarantees an upper bound on the estimation error covariance, which is minimized by optimally designing the estimator gain. Based on three simulation scenarios (gross errors, large measurement noises, and random outliers) on an islanded microgrid with two REG units, it can be found that the proposed approach outperforms conventional state estimation methods in estimation accuracy and robustness. In summary, this work provides a practical solution for real-time monitoring, security assessment, and situational awareness of REG-based microgrids, which is useful in enhancing the reliable operation in both grid-connected and islanded modes.

In this article, the event-based recursive state estimation problem is investigated for a class of stochastic complex dynamical networks under cyberattacks. A hybrid cyberattack model is introduced to take into account both the randomly occurring deception attack and the randomly occurring denial-of-service attack. For the sake of reducing the transmission rate and mitigating the network burden, the event-triggered mechanism is employed under which the measurement output is transmitted to the estimator only when a preset condition is satisfied. An upper bound on the estimation error covariance on each node is first derived through solving two coupled Riccati-like difference equations. Then, the desired estimator gain matrix is recursively acquired that minimizes such an upper bound. Using the stochastic analysis theory, the estimation error is proven to be stochastically bounded with probability 1. Finally, an illustrative example is provided to verify the effectiveness of the developed estimator design method.

In this article, the cluster synchronization control problem is studied for discrete-time complex dynamical networks when the data transmission is subject to constrained bit rate. A bit-rate model is presented to quantify the limited network bandwidth, and the effects from the constrained bit rate onto the control performance of the cluster synchronization are evaluated. A sufficient condition is first proposed to guarantee the ultimate boundedness of the error dynamics of the cluster synchronization, and then, a bit-rate condition is established to reveal the fundamental relationship between the bit rate and the certain performance index of the cluster synchronization. Subsequently, two optimization problems are formulated to design the desired synchronization controllers with aim to achieve two distinct synchronization performance indices. The codesign issue for the bit-rate allocation protocol and the controller gains is further discussed to reduce the conservatism by locally minimizing a certain asymptotic upper bound of the synchronization error dynamics. Finally, three illustrative simulation examples are utilized to validate the feasibility and effectiveness of the developed synchronization control scheme.

In this article, a delay-range-dependent approach is put forward to tackle the state estimation problem for delayed impulsive neural networks. A new type of nonlinear function, which is more general than the normal sigmoid function and functions constrained by the Lipschitz condition, is adopted as the neuron activation function. To effectively alleviate data collisions and save energy, the round-robin protocol is utilized to mitigate the occurrence of unnecessary network congestion in communication channels from sensors to the estimator. With the aid of the Lyapunov stability theory, a state observer is constructed such that the estimation error dynamics are asymptotically stable. The observer existence is ensured by resorting to a set of delay-range-dependent criteria which is dependent on both the impulsive time instant and a coefficient matrix. In addition, the synthesis of the observer is discussed by using linear matrix inequalities. Simulations are provided to illustrate the reasonability of our delay-range-dependent estimation approach.

Large-scale undirected weighted networks are frequently encountered in big-data-related applications concerning interactions among a large unique set of entities. Such a network can be described by a Symmetric, High-Dimensional, and Incomplete (SHDI) matrix whose symmetry and incompleteness should be addressed with care. However, existing models fail in either correctly representing its symmetry or efficiently handling its incomplete data. For addressing this critical issue, this study proposes an Alternating-Direction-Method of Multipliers (ADMM)-based Symmetric Non-negative Latent Factor Analysis (ASNL) model. It adopts fourfold ideas: 1) implementing the data density-oriented modeling for efficiently representing an SHDI matrix's incomplete and imbalanced data; 2) separating the non-negative constraints from the decision parameters to avoid truncations during the training process; 3) incorporating the ADMM principle into its learning scheme for fast model convergence; and 4) parallelizing the training process with load balance considerations for high efficiency. Empirical studies on four SHDI matrices demonstrate that ASNL significantly outperforms several state-of-the-art models in both prediction accuracy for missing data of an SHDI and computational efficiency. It is a promising model for handling large-scale undirected networks raised in real applications.

In this article, the simultaneous state and fault estimation problem is investigated for a class of nonlinear 2-D shift-varying systems, where the sensors and the estimator are connected via a communication network of limited bandwidth. With the purpose of relieving the communication burden and enhancing the transmission security, a new encoding-decoding mechanism is put forward so as to encode the transmitted data with a finite number of bits. The aim of the addressed problem is to develop a neural-network (NN)-based set-membership estimator for jointly estimating the system states and the faults, where the estimation errors are guaranteed to reside within an optimized ellipsoidal set. With the aid of the mathematical induction technique and certain convex optimization approaches, sufficient conditions are derived for the existence of the desired set-membership estimator, and the estimator gains and the NN tuning scalars are then presented in terms of the solutions to a set of optimization problems subject to ellipsoidal constraints. Finally, an illustrative example is given to demonstrate the effectiveness of the proposed estimator design method.

In this article, the adaptive neural-network-based (NN-based) set-membership state estimation problem is studied for a class of nonlinear systems subject to bit rate constraints and unknown-but-bounded noises. The measurement output signals are transmitted from sensors to a remote estimator via a bit rate constrained communication channel. To relieve the communication burden and ameliorate the state estimation accuracy, a bit rate allocation mechanism is put forward for the sensor nodes by solving a constrained optimization problem. Subsequently, through the NN learning method, an NN-based set-membership estimator is designed to determine an ellipsoidal set that contains the system state, where the proposed estimator relies upon a prediction-correction structure. With the help of the mathematical induction technique and the set theory, sufficient conditions are obtained to ensure the existence of both the adaptive tuning parameters and the set-membership estimators, and then, the corresponding parameters and estimator gains are calculated by solving a set of optimization problems. In addition, the monotonicity of the upper bound on the squared estimation error with respect to the bit rate and the convergence of the NN weight are analyzed, respectively. Finally, an illustrative example is given to demonstrate the effectiveness of the proposed state estimation algorithm.

This article is concerned with the partial-node-based (PNB) state estimation problem for delayed complex networks (DCNs) subject to intermittent measurement outliers (IMOs). In order to describe the intermittent nature of outliers, several sequences of shifted gate functions are adopted to model the occurrence moments and the disappearing moments of IMOs. Two outlier-related indices, namely, minimum and maximum interval lengths, are employed to parameterize the "occurrence frequency" of IMOs. The norm of the addressed outlier is allowed to be greater than a certain fixed threshold, and this distinguishes the outlier from the extensively studied norm-bounded noise. By adopting the input-output models of the considered complex network, a novel multiple-order-holder (MOH) approach is developed to resist the effects of IMOs by dedicatedly designing a weighted average of certain non-IMO measurements, and then, a PNB state estimator is constructed based on the outputs of the MOHs. Sufficient conditions are proposed to ensure the exponentially ultimate boundedness (EUB) of the resultant estimation error, and the estimator gain matrices are subsequently obtained by solving a constrained optimization problem. Finally, two simulation examples are provided to demonstrate the effectiveness of our developed outlier-resistant PNB state estimation scheme.

In this article, the distributed formation-containment (FC) control issue is investigated for a class of discrete-time multiagent systems (DT-MASs) under the event-triggered communication mechanism. In order to save the communication cost and improve the utilization of communication resources, a novel dynamic event-triggered (DET) mechanism is developed by adding an auxiliary variable for each agent system, which is able to dynamically adjust the triggering threshold. A distributed FC control scheme under the DET mechanism is proposed for all the leaders and followers based on the available relative outputs. The purpose of the addressed problem is to design the FC controller such that all the leaders achieve a formation shape and all the followers converge into such a convex hull. To this end, the considered DT-MASs are first decoupled into a diagonal form by resorting to the property of the Laplacian matrix as well as the inequality technique. Then, two sufficient conditions are established to ensure the desired FC performance. Furthermore, the corresponding FC controller parameters are obtained in terms of the solutions to two matrix inequalities which only depend on the maximum and minimum nonzero eigenvalues of the Laplacian matrix. Finally, an illustrative example is provided to verify the validity of the developed control scheme.

In this article, a new outlier-resistant mechanism is proposed to deal with the variance-constrained filtering problem for a class of networked systems subject to sensor resolution under the round-robin protocol (RRP). Sensor resolution, which serves as an important index in determining measurement accuracy, is taken into account in the addressed filtering problem, and the sensor-resolution-induced uncertainty is tackled by using an upper-bounding technique. The RRP is employed to regulate the order of signal transmission in order to relieve communication overhead. In the case of measurement outliers, a tailored saturation function is dedicatedly introduced to the filter structure for the purpose of suppressing the outlier-corrupted innovations, thereby maintaining satisfactory filtering performance. By solving a matrix difference equation, an upper bound is first acquired on the error covariance of the devised filter, and the associated filter parameters are subsequently determined through minimizing the acquired bound. The validity of the developed variance-constrained filter design approach is thoroughly demonstrated via two simulation examples.

This article is concerned with the dissipative deconvolution filtering issue for networked two-dimensional (2-D) digital systems. By orchestrating the sensor outputs with a prescribed dynamic transmission order, a new multinode Round-Robin protocol (RRP) under the 2-D setting is developed on the sensor-to-filter channel to ease the communication overheads, and a novel compensation strategy is proposed for enhancing the deconvolution filter performance. A sufficient condition is established for ensuring the dissipativity of the deconvolution filter in terms of coupled matrix inequalities. Furthermore, a particle swarm optimization (PSO) algorithm is formulated to design the filter gain with certain optimized performance. Finally, by transforming the color image into three gray images, the proposed algorithms are applied to the remote color image restoration problem under RRP. It is shown that the use of RRP saves 33.3% communication burden, and the saving of communication resources reduces the signal-to-noise ratio (SNR) by 18.5%. In comparison to the RRP using zero-input compensation strategy in available literature, the introduction of the novel multinode RRP with compensation strategy can indeed improve the reconstruction SNR.

In this article, as inspired by multiagent systems, a novel leader–follower-based particle swarm optimization (LFPSO) algorithm is presented where the particles are classified into leaders and followers according to their respective roles. The leaders are responsible for searching a wide range of the optimal candidate solutions so as to ensure the diversity of the particle population, and the followers are dedicated to seeking the global-best solution in order to guarantee the convergence of particles. A controller parameter is introduced to fine tune the impact of the leaders on the followers. Owing to the leader–follower mechanism, the proposed LFPSO algorithm not only maintains the diversity of the particle population but also improves the possibility of escaping from the locally optimal solution. It is demonstrated via experimental results that the proposed LFPSO algorithm significantly improves the accuracy and convergence rate of conventional particle swarm optimization algorithms. Furthermore, the LFPSO algorithm is successfully applied to denoise real-time signals in oilfield pipeline network and its superiority over existing denoising algorithms is verified as well.

In this article, the distributed model predictive control (MPC)-based noncooperative game problem is dealt with for the discrete-time multiplayer systems (MPSs) with an undirected graph. To reflect the reality, the state and input constraints are considered along with the matched disturbances and unmatched disturbances. The disturbance-observer-based composite MPC strategy is put forward which optimizes a given cost function over the receding horizon while eliminating the matched disturbances. An iterative algorithm is developed such that the model predictive dynamic game (MPDG) converges to the so-called$\varepsilon $-Nash equilibrium in a distributed manner. Sufficient conditions are established to guarantee the convergence of the proposed algorithm. In addition, easy-to-check conditions are also provided to ensure the uniform boundedness of the studied MPSs. Finally, a numerical example of a group of spacecrafts is provided to verify the effectiveness of the proposed methodology.

In this article, a proportional-integral-derivative (PID) tracking control problem is studied for a class of linear discrete-time systems under multiple description encoding mechanisms (MDEMs). The data transmissions on the sensor-to-controller channels are subject to packet dropouts whose occurrences are random and governed by two Bernoulli-distributed sequences of certain probability distributions. In order to improve the reliability of data transmission, an MDEM is put forward, with which the data is encoded into two descriptions of identical importance before being transmitted to the decoders through two individual communication channels. The aim of this article is to develop a PID tracking controller for guaranteeing the ultimate boundedness of the resulting tracking error, and the corresponding controller gains are obtained by solving an optimization problem. Moreover, the effect of the packet dropouts on the decoding accuracy is explicated via assessing the boundedness in respect to the decoding error. A simulation example is finally presented to showcase the applicability of the proposed PID tracking control scheme.

The nnUNet is a fully automated and generalisable framework which automatically configures the full training pipeline for the segmentation task it is applied on, while taking into account dataset properties and hardware constraints. It utilises a basic UNet type architecture which is self-configuring in terms of topology. In this work, we propose to extend the nnUNet by integrating mechanisms from more advanced UNet variations such as the residual, dense, and inception blocks, resulting in three new nnUNet variations, namely the Residual-nnUNet, Dense-nnUNet, and Inception-nnUNet. We have evaluated the segmentation performance on eight datasets consisting of 20 target anatomical structures. Our results demonstrate that altering network architecture may lead to performance gains, but the extent of gains and the optimally chosen nnUNet variation is dataset dependent.

In this article, the problem of consensus control is investigated for a class of multiagent systems (MASs) with both stochastic noises and nonidentical exogenous disturbances. The signal transmission among agents is implemented through a digital communication network subject to both uniform quantization and round-robin protocol as a reflection of network constraints. The consensus strategy is designed by adopting the estimates of the relative states of the agent to its neighbors, which renders the distributed nature of the controller. A new consensus concept, namely, quasiconsensus in probability, is employed to evaluate the state response of the agents to the stochastic noises, the exogenous disturbances, and the quantization error. An augmented system is first formed that relies on the deviations of the individual state from the average state, the observer error of the relative state, as well as the relative measurement output. Based on the augmented model, an analysis approach on dynamical behaviors is developed to facilitate the consensus analysis of MASs by means of the switching Lyapunov function technique and the stochastic analysis methods. Then, the existence condition and the explicit expression of the time-varying gain matrices are proposed for the expected controller by resorting to the feasibility of several matrix inequalities. Numerical simulation results are presented to demonstrate the applicability of the theoretical results.

In this article, an adaptive learning framework is established for a deep weighted sparse autoencoder (AE) by resorting to the multiobjective evolutionary algorithm (MOEA). The weighted sparsity is introduced to facilitate the design of the varying degrees of the sparsity constraints imposed on the hidden units of the AE. The MOEA is exploited to adaptively seek appropriate hyperparameters, where the divide-and-conquer strategy is implemented to enhance the MOEA's performance in the context of deep neural networks. Moreover, a sharing scheme is proposed to further reduce the time complexity of the learning process at the slight expense of the learning precision. It is shown via extensive experiments that the established adaptive learning framework is effective, where different sparse models are utilized to demonstrate the generality of the proposed results. Then, the generality of the proposed framework is examined on the convolutional AE and VGG-16 network. Finally, the developed framework is applied to the blind image quantity assessment that illustrates the applicability of the established algorithms.

In this article, the recursive filtering problem is investigated for state-saturated complex networks (CNs) subject to uncertain coupling strengths (UCSs) and deception attacks. The measurement signals transmitted via the communication network may suffer from deception attacks, which are governed by Bernoulli-distributed random variables. The purpose of the problem under consideration is to design a minimum-variance filter for CNs with deception attacks, state saturations, and UCSs such that upper bounds on the resulting error covariances are guaranteed. Then, the expected filter gains are acquired via minimizing the traces of such upper bounds, and sufficient conditions are established to ensure the exponential mean-square boundedness of the filtering errors. Finally, two simulation examples (including a practical application) are exploited to validate the effectiveness of our designed approach.

In this article, a frequency-domain approach is developed to deal with the global consensus problem for a class of general second-order multiagent systems (MASs) subject to actuator saturations. By employing the describing function and the generalized Nyquist criterion, the global consensus problem is thoroughly investigated for both undirected and directed topologies. First, the describing function is introduced to characterize the actuator saturations in the s -plane, and the inherent representation error is quantitatively analyzed from a frequency-domain perspective. Then, by means of the Kronecker product, the addressed consensus problem of the MAS is transformed into a corresponding stability analysis problem for a certain multi-input-multi-output (MIMO) system and, consequently, the generalized Nyquist criterion for MIMO systems is exploited to derive the condition for the global consensus of the MAS where the impact from the actuator saturation is explicitly reflected. Finally, numerical simulations are provided to illustrate the validity of the proposed theoretical result.

In this article, a delay-compensation-based state estimation (DCBSE) method is given for a class of discrete time-varying complex networks (DTVCNs) subject to network-induced incomplete observations (NIIOs) and dynamical bias. The NIIOs include the communication delays and fading observations, where the fading observations are modeled by a set of mutually independent random variables. Moreover, the possible bias is taken into account, which is depicted by a dynamical equation. A predictive scheme is proposed to compensate for the influences induced by the communication delays, where the predictive-based estimation mechanism is adopted to replace the delayed estimation transmissions. This article focuses on the problems of estimation method design and performance discussions for addressed DTVCNs with NIIOs and dynamical bias. In particular, a new distributed state estimation approach is presented, where a locally minimized upper bound is obtained for the estimation error covariance matrix and a recursive way is designed to determine the estimator gain matrix. Furthermore, the performance evaluation criteria regarding the monotonicity are proposed from the analytic perspective. Finally, some experimental comparisons are proposed to show the validity and advantages of the new DCBSE approach.

In this article, a resilient$H_{\infty }$approach is put forward to deal with the state estimation problem for a type of discrete-time delayed memristive neural networks (MNNs) subject to stochastic disturbances (SDs) and dynamic event-triggered mechanism (ETM). The dynamic ETM is utilized to mitigate unnecessary resource consumption occurring in the sensor-to-estimator communication channel. To guarantee resilience against possible realization errors, the estimator gain is permitted to undergo some norm-bounded parameter drifts. For the delayed MNNs, our aim is to devise an event-based resilient$H_{\infty }$estimator that not only resists gain variations and SDs but also ensures the exponential mean-square stability of the resulting estimation error system with a guaranteed disturbance attenuation level. By resorting to the stochastic analysis technique, sufficient conditions are acquired for the expected estimator and, subsequently, estimator gains are obtained via figuring out a convex optimization problem. The validity of the$H_{\infty }$estimator is finally shown via a numerical example.

In this article, a pinning control strategy is developed for the finite-horizon$H_{\infty }$synchronization problem for a kind of discrete time-varying nonlinear complex dynamical network in a digital communication circumstance. For the sake of complying with the digitized data exchange, a feedback-type dynamic quantizer is introduced to reflect the transformation from the raw signals into the discrete-valued ones. Then, a quantized pinning control scheme takes place on a small fraction of the network nodes with the hope of cutting down the control expenses while achieving the expected global synchronization objective. Subsequently, by resorting to the completing-the-square technique, a sufficient condition is established to ensure the finite-horizon$H_{\infty }$index of the synchronization error dynamics against both quantization errors and external noises. Moreover, a controller design algorithm is put forward via an auxiliary$H_{2}$-type criterion, and the desired controller gains are acquired in terms of two coupled backward Riccati equations. Finally, the validity of the presented results is verified via a simulation example.

This article is concerned with the problem of recursive state estimation for a class of multirate multisensor systems with distributed time delays under the round-robin (R-R) protocol. The state updating period of the system and the sampling period of the sensors are allowed to be different so as to reflect the engineering practice. An iterative method is presented to transform the multirate system into a single-rate one, thereby facilitating the system analysis. The R-R protocol is introduced to determine the transmission sequence of sensors with the aim to alleviate undesirable data collisions. Under the R-R protocol scheduling, only one sensor can get access to transmit its measurement at each sampling time instant. The main purpose of this article is to develop a recursive state estimation scheme such that an upper bound on the estimation error covariance is guaranteed and then locally minimized through adequately designing the estimator parameter. Finally, simulation examples are provided to show the effectiveness of the proposed estimator design scheme.

Clustering, as an important part of data mining, is inherently a challenging problem. This article proposes a differential evolution algorithm with adaptive niching and k -means operation (denoted as DE_ANS_AKO) for partitional data clustering. Within the proposed algorithm, an adaptive niching scheme, which can dynamically adjust the size of each niche in the population, is devised and integrated to prevent premature convergence of evolutionary search, thus appropriately searching the space to identify the optimal or near-optimal solution. Furthermore, to improve the search efficiency, an adaptive k -means operation has been designed and employed at the niche level of population. The performance of the proposed algorithm has been evaluated on synthetic as well as real datasets and compared with related methods. The experimental results reveal that the proposed algorithm is able to reliably and efficiently deliver high quality clustering solutions and generally outperforms related methods implemented for comparisons.

In this article, the nonfragile filtering issue is addressed for complex networks (CNs) with switching topologies, sensor saturations, and dynamic event-triggered communication protocol (DECP). Random variables obeying the Bernoulli distribution are utilized in characterizing the phenomena of switching topologies and stochastic gain variations. By introducing an auxiliary offset variable in the event-triggered condition, the DECP is adopted to reduce transmission frequency. The goal of this article is to develop a nonfragile filter framework for the considered CNs such that the upper bounds on the filtering error covariances are ensured. By the virtue of mathematical induction, gain parameters are explicitly derived via minimizing such upper bounds. Moreover, a new method of analyzing the boundedness of a given positive-definite matrix is presented to overcome the challenges resulting from the coupled interconnected nodes, and sufficient conditions are established to guarantee the mean-square boundedness of filtering errors. Finally, simulations are given to prove the usefulness of our developed filtering algorithm.

This article tackles the recursive filtering problem for an array of 2-D systems over sensor networks with a given topology. Both the measurement degradations of the network outputs and the stochastic perturbations of network couplings are modeled to reflect engineering practice by introducing some random variables with given statistics. The goal of the addressed problem is to devise the distributed recursive filters capable of cooperatively estimating the true state in order to ensure locally minimal upper bound (UB) on the second-order moment of the filtering error (also viewed as the general error variance). For this purpose, the general error variance regarding the underlying target plant is first provided to facilitate the subsequent filter design, and then a certain UB on the error variance is constructed by exploiting the stochastic analysis and the induction approach. Furthermore, in view of the inherent sparsity of the sensor network, the gain parameters of the desired distributed filters are determined, and the proposed recursive filtering algorithm is shown to be scalable. Finally, an illustrative example is given to demonstrate the validity of the established filtering strategy.

This article is concerned with the multiloop decentralized$H_{\infty }$fuzzy proportional–integral–derivative-like (PID-like) control problem for discrete-time Takagi–Sugeno fuzzy systems with time-varying delays under dynamical event-triggered mechanisms (ETMs). The sensors of the plant are grouped into several nodes according to their physical distribution. For resource-saving purposes, the signal transmission between each sensor node and the controller is implemented based on the dynamical ETM. Taking the node-based idea into account, a general multiloop decentralized fuzzy PID-like controller is designed with fixed integral windows to reduce the potential accumulation error. The overall decentralized fuzzy PID-like control scheme involves multiple single-loop controllers, each of which is designed to generate the local control law based on the measurements of the corresponding sensor node. These kinds of local controllers are convenient to apply in practice. Sufficient conditions are obtained under which the controlled system is exponentially stable with the prescribed$H_{\infty }$performance index. The desired controller gains are then characterized by solving an iterative optimization problem. Finally, a simulation example is presented to demonstrate the correctness and effectiveness of the proposed design procedure.

In this article, we consider the$H_{\infty }$proportional-integral (PI) state estimation (SE) problem for discrete-time T–S fuzzy systems subject to transmission delays, external disturbances, and redundant channels. Multiple redundant communication channels are utilized between the sensors and the remote estimator to enhance the reliability of data transmissions. In order to characterize the transmission delays in network-based communication, a family of random variables with partly known probabilities, which are independent and identically distributed, is adopted to describe the random behavior of the transmission delays with the redundant channels. The objective of this work is to put forward a PI state estimator such that the dynamics of the estimation error is exponentially mean-square stable and satisfies the prescribed$H_{\infty }$performance index of the disturbance attenuation/rejection. By employing the stochastic analysis approach, the error dynamics of the SE under the proposed state estimator is analyzed and sufficient conditions are obtained to ensure the existence of the required PI state estimator. Furthermore, the desired estimator parameters are derived by solving a nonlinear optimization problem. Finally, two simulation examples are exploited to demonstrate the validity of the proposed SE scheme.

In this article, a dynamic-neighborhood-based switching PSO (DNSPSO) algorithm is proposed, where a new velocity updating mechanism is designed to adjust the personal best position and the global best position according to a distance-based dynamic neighborhood to make full use of the population evolution information among the entire swarm. In addition, a novel switching learning strategy is introduced to adaptively select the acceleration coefficients and update the velocity model according to the searching state at each iteration, thereby contributing to a thorough search of the problem space. Furthermore, the differential evolution algorithm is successfully hybridized with the particle swarm optimization (PSO) algorithm to alleviate premature convergence. A series of commonly used benchmark functions (including unimodal, multimodal, and rotated multimodal cases) is utilized to comprehensively evaluate the performance of the DNSPSO algorithm. The experimental results demonstrate that the developed DNSPSO algorithm outperforms a number of existing PSO algorithms in terms of the solution accuracy and convergence performance, especially for complicated multimodal optimization problems.

This article is concerned with the energy-to-peak state estimation problem for a class of linear discrete-time systems with energy-bounded noises and intermittent measurement outliers (IMOs). In order to capture the intermittent nature, two sequences of step functions are introduced to model the occurrence of the IMOs. Furthermore, two special indices (i.e., minimum and maximum interval lengths) are adopted to describe the "occurrence frequency" of IMOs. Different from the considered energy-bounded noises, the outliers are assumed to have their magnitudes larger than certain thresholds. In order to achieve a satisfactory performance constraint on the energy-to-peak state estimation under the addressed kind of measurement outliers, a novel parameter-dependent (PD) state estimation strategy is developed to guarantee that the measurements contaminated by outliers would be removed in the estimation process. The proposed PD state estimation method is essentially a two-step process, where the first step is to examine the appearing and disappearing moments for each IMO by using a dedicatedly constructed outlier detection scheme, and the second step is to implement the state estimation task according to the outlier detection results. Sufficient conditions are obtained to ensure the existence of the desired estimator, and the gain matrix of the desired estimator is then derived by solving a constrained optimization problem. Finally, a simulation example is presented to illustrate the effectiveness of our developed PD state estimation strategy.

This article is concerned with the$H_{\infty }$proportional–integral–derivative (PID) control problem for class of discrete-time Takagi–Sugeno fuzzy systems subject to infinite-distributed time delays and round-robin (RR) protocol scheduling effects. The information exchange between the sensors and the controller is conducted through a shared communication network. For the purpose of alleviating possible data collision, the well-known RR communication protocol is deployed to schedule the data transmissions. To stabilize the target system with guaranteed$H_{\infty }$performance index, a novel yet easy-to-implement fuzzy PID controller is developed whose integral term is calculated based on the past measurements defined in a limited time window with hope to improve computational efficiency and reduce accumulation error. Based on the Lyapunov stability theory and the convex optimization technique, sufficient conditions are derived to ensure the exponential stability as well as the$H_{\infty }$disturbance attenuation/rejection capacity of the underlying system. Furthermore, by utilizing the cone complementarity linearization algorithm, the nonconvex controller design problem is transformed into an iterative optimization one that facilitates the controller implementation. Finally, simulation examples are given to show the effectiveness and correctness of the developed control method.

This article is concerned with the extended dissipative fuzzy proportional–integral–derivative (PID) control problem for nonlinear systems subject to controller parameter perturbations over a class of mixed fading channels. The sensors of plant are divided into two groups according to engineering practice, where the individual sensor group transmits the measurements to the controller via a respective communication channel undergoing specific fading effects. Considering the complicated nature of the signal fading with the transmission channels, two stochastic models (i.e., the independent and identically distributed fading model and the Markov fading model) are simultaneously employed to describe the mixed fading effects of the two communication channels corresponding to the two sensor groups. The objective of this article is to design a nonfragile PID controller such that the closed-loop system is exponentially stable in mean square and extended stochastically dissipative. With the assistance of the Lyapunov stability theory and stochastic analysis method, sufficient conditions are obtained to analyze the system performance. Then, within the established theoretical framework, an iterative optimization algorithm is proposed to design the desired controller parameters by using the convex optimization technique. Finally, two simulation examples are given to verify the effectiveness of the proposed control schemes.

High-dimensional and sparse (HiDS) matrices are frequently found in various industrial applications. A latent factor analysis (LFA) model is commonly adopted to extract useful knowledge from an HiDS matrix, whose parameter training mostly relies on a stochastic gradient descent (SGD) algorithm. However, an SGD-based LFA model's learning rate is hard to tune in real applications, making it vital to implement its self-adaptation. To address this critical issue, this study firstly investigates the evolution process of a particle swarm optimization algorithm with care, and then proposes to incorporate more dynamic information into it for avoiding accuracy loss caused by premature convergence without extra computation burden, thereby innovatively achieving a novel position-transitional particle swarm optimization (P2SO) algorithm. It is subsequently adopted to implement a P2SO-based LFA (PLFA) model that builds a learning rate swarm applied to the same group of LFs. Thus, a PLFA model implements highly efficient learning rate adaptation as well as represents an HiDS matrix precisely. Experimental results on four HiDS matrices emerging from real applications demonstrate that compared with an SGD-based LFA model, a PLFA model no longer suffers from a tedious and expensive tuning process of its learning rate, and it can achieve even higher prediction accuracy for missing data of an HiDS matrix. On the other hand, compared with state-of-the-art adaptive LFA models, a PLFA model's prediction accuracy and computational efficiency are highly competitive. Hence, it has high potential in addressing real industrial issues.

Online multi-object tracking (MOT) is an active research topic in the domain of computer vision. Although many previously proposed algorithms have exhibited decent results, the issue of tracklet inactivation has not been sufficiently studied. Simple strategies such as using a fixed threshold on classification scores are adopted, yielding undesirable tracking mistakes and limiting the overall performance. In this paper, a conditional random field (CRF) based framework is put forward to tackle the tracklet inactivation issue in online MOT problems. A discrete CRF which exploits the intra-frame relationship between tracking hypotheses is developed to improve the robustness of tracklet inactivation. Separate sets of feature functions are designed for the unary and binary terms in the CRF, which take into account various tracking challenges in practical scenarios. To handle the problem of varying CRF nodes in the MOT context, two strategies named as hypothesis filtering and dummy nodes are employed. In the proposed framework, the inference stage is conducted by using the loopy belief propagation algorithm, and the CRF parameters are determined by utilizing the maximum likelihood estimation method followed by slight manual adjustment. Experimental results show that the tracker combined with the CRF-based framework outperforms the baseline on the MOT16 and MOT17 benchmarks. The extensibility of the proposed framework is further validated by an extensive experiment.

This article is concerned with the$H_{\infty }$state estimation problem for a class of bidirectional associative memory (BAM) neural networks with binary mode switching, where the distributed delays are included in the leakage terms. A couple of stochastic variables taking values of 1 or 0 are introduced to characterize the switching behavior between the redundant models of the BAM neural network, and a general type of neuron activation function (i.e., the sector-bounded nonlinearity) is considered. In order to prevent the data transmissions from collisions, a periodic scheduling protocol (i.e., round-robin protocol) is adopted to orchestrate the transmission order of sensors. The purpose of this work is to develop a full-order estimator such that the error dynamics of the state estimation is exponentially mean-square stable and the$H_{\infty }$performance requirement of the output estimation error is also achieved. Sufficient conditions are established to ensure the existence of the required estimator by constructing a mode-dependent Lyapunov-Krasovskii functional. Then, the desired estimator parameters are obtained by solving a set of matrix inequalities. Finally, a numerical example is provided to show the effectiveness of the proposed estimator design method.

This article addresses the simultaneous state and unknown input estimation problem for a class of discrete time-varying complex networks (CNs) under redundant channels and dynamic event-triggered mechanisms (ETMs). The redundant channels, modeled by an array of mutually independent Bernoulli distributed stochastic variables, are exploited to enhance transmission reliability. For energy-saving purposes, a dynamic event-triggered transmission scheme is enforced to ensure that every sensor node sends its measurement to the corresponding estimator only when a certain condition holds. The primary objective of the investigation carried out is to construct a recursive estimator for both the state and the unknown input such that certain upper bounds on the estimation error covariances are first guaranteed and then minimized at each time instant in the presence of dynamic event-triggered strategies and redundant channels. By solving two series of recursive difference equations, the desired estimator gains are computed. Finally, an illustrative example is presented to show the usefulness of the developed estimator design method.

A recommender system (RS) is highly efficient in filtering people’s desired information from high-dimensional and sparse (HiDS) data. To date, a latent factor (LF)-based approach becomes highly popular when implementing a RS. However, current LF models mostly adopt single distance-oriented Loss like an$L_{2}$norm-oriented one, which ignores target data’s characteristics described by other metrics like an$L_{1}$norm-oriented one. To investigate this issue, this article proposes an$L_{1}$-and-$L_{2}$-norm-oriented LF ($\text{L}^{3}\text{F}$) model. It adopts twofold ideas: 1) aggregating$L_{1}$norm’s robustness and$L_{2}$norm’s stability to form its Loss and 2) adaptively adjusting weights of$L_{1}$and$L_{2}$norms in its Loss. By doing so, it achieves fine aggregation effects with$L_{1}$norm-oriented Loss ’s robustness and$L_{2}$norm-oriented Loss ’s stability to precisely describe HiDS data with outliers. Experimental results on nine HiDS datasets generated by real systems show that an$\text{L}^{3}\text{F}$model significantly outperforms state-of-the-art models in prediction accuracy for missing data of an HiDS dataset. Its computational efficiency is also comparable with the most efficient LF models. Hence, it has good potential for addressing HiDS data from real applications.

This article is concerned with the consensus control problem for a general class of linear multiagent systems (MASs) subject to actuator imperfection consisting of both actuator saturations and actuator faults. A novel two-step saturation-resistant approach is proposed to attenuate the side effects resulting from the actuator imperfection. In the first step of controller design, the state information received from the neighboring agents is used to design a consensus controller capable of tolerating the actuator fault. Then, in the second step of controller optimization, the domain of attraction (DOA) is introduced for MASs to evaluate the performance of the controller and, subsequently, optimize the controller parameters to enlarge the DOA in terms of solutions to a certain set of matrix inequalities. Finally, simulation examples are provided to demonstrate the effectiveness of the developed saturation-resistant approach.

A non-negative latent factor analysis (NLFA)-based recommender can make precise recommendations by correctly representing the non-negative characteristic of industrial data. It commonly relies on a nonconvex and bilinear optimization process, where the effects of first-order solvers maybe significantly reduced. Higher order solvers like a Newton-type method are expected to make a breakthrough; however, its computation efficiency and scalability are greatly limited due to the numerous parameters involved in a Hessian matrix. To address this issue, this article proposes an approach for assimilating second-order information for building NLFA-based recommenders. The key idea is an inner second-order solver that employs a Hessian-free method for avoiding the highly expensive manipulations of a Hessian matrix. Empirical studies on eight data cases emerging from real industrial applications indicate that the proposed approach outperforms state-of-the-art models in prediction accuracy with affordable computational burden.

In this article, a resilient set-membership approach is put forward to deal with the state estimation problem for a sort of discrete-time memristive neural networks (DMNNs) with hybrid time delays under the weighted try-once-discard protocol (WTODP). The WTODP is utilized to mitigate unnecessary network congestion occurring in the channel between DMNNs and the state estimator. In order to ensure resilience against possible realization errors, the estimator gain is permitted to undergo some norm-bounded parameter drifts. Our objective is to design a resilient set-membership estimator (RSME) that is capable of resisting gain variations and unknown-but-bounded noises by confining the estimation error to certain ellipsoidal regions. By resorting to the recursive matrix inequality technique, sufficient conditions are acquired for the existence of the expected RSME and, subsequently, an optimization problem is formalized by minimizing the constraint ellipsoid (with respect to the estimation error) under WTODP. Finally, numerical simulation is carried out to validate the usefulness of RSME.

In this article, we investigate the set-membership state estimation problem for a class of time-varying systems with non-Gaussian noises over a FlexRay network. To mitigate the communication load and improve the flexibility of the data scheduling, the FlexRay protocol (FRP) governed by both the time-triggered and event-triggered rules is exploited to regulate the signal transmission in a cyclic fashion. A new expression of the input signal to the state estimator is formulated with intention to account for the effect of the FRP. Accordingly, a multirate model (orchestrating the sampling/updating rates of the target plant, sensors, and state estimator) is proposed and then transformed into a single-rate one with the help of the lifting technique and the vector augmentation method. Subsequently, sufficient conditions are provided for the true states to always reside in an ellipsoid at each time instant in the presence of the non-Gaussian noises, and such an ellipsoid is then minimized in the matrix-trace sense. An online optimization algorithm is developed to parameterize the estimator gains by means of the solution to certain recursive matrix inequalities. Numerical results demonstrate the validity of the proposed protocol-based set-membership state estimator design scheme.

This article deals with the proportional–integral observer (PIO) design problem for a class of linear systems with distributed time delays and randomly occurring parameter uncertainties. The measurement signals, transmitted from the sensors to the observer, might suffer from the randomly occurring deception attacks. The random occurrences of parameter uncertainties and deception attacks are governed by two series of Bernoulli random variables with known probability distributions. An outlier-resistant PIO is developed by introducing an innovation saturation mechanism for the sake of alleviating the adverse effects induced by the deception attacks on the estimation performance. The purpose of the addressed problem is to design a PIO that is capable of guaranteeing the mean-square boundedness of the estimation errors while achieving the desired security level. The desired PIO gain is designed by solving a matrix inequality and the validity of the results obtained is shown by a numerical simulation example.

In this chapter, the partial-neurons-based state estimation problem is studied for a class of delayed neural networks with state-dependent noises under redundant channels. For the purpose of improving the success rate of the data transmission from the sensor to the estimator, the redundant-channel-based transmission mechanism is considered. The main aim of the addressed problem is to design a state estimator to estimate the neurons&s; state by use of a small fraction of the sensor measurements. With the help of the Lyapunov stability theory, a sufficient condition is provided to ensure that the estimation error dynamics is exponentially mean-square bounded. The desired estimator gain is acquired by minimizing an asymptotic upper bound of the estimation error. Finally, a numerical simulation is carried out to demonstrate the usefulness of the presented estimator design scheme.

This article is concerned with the problem of finite-horizon H∞state estimation for time-varying coupled stochastic networks through the round-robin scheduling protocol. The inner coupling strengths of the considered coupled networks are governed by a random sequence with known expectations and variances. For the sake of mitigating the occurrence probability of the network-induced phenomena, the communication network is equipped with the round-robin protocol that schedules the signal transmissions of the sensors' measurement outputs. By using some dedicated approximation techniques, an uncertain auxiliary system with stochastic parameters is established where the multiplicative noises enter the coefficient matrix of the augmented disturbances. With the established auxiliary system, the desired finite-horizon H∞state estimator is acquired by solving coupled backward Riccati equations, and the corresponding recursive estimator design algorithm is presented that is suitable for online application. The effectiveness of the proposed estimator design method is validated via a numerical example.

In this paper, a novel particle swarm optimization (PSO) algorithm is put forward where a sigmoid-function-based weighting strategy is developed to adaptively adjust the acceleration coefficients. The newly proposed adaptive weighting strategy takes into account both the distances from the particle to the global best position and from the particle to its personal best position, thereby having the distinguishing feature of enhancing the convergence rate. Inspired by the activation function of neural networks, the new strategy is employed to update the acceleration coefficients by using the sigmoid function. The search capability of the developed adaptive weighting PSO (AWPSO) algorithm is comprehensively evaluated via eight well-known benchmark functions including both the unimodal and multimodal cases. The experimental results demonstrate that the designed AWPSO algorithm substantially improves the convergence rate of the particle swarm optimizer and also outperforms some currently popular PSO algorithms.

In this article, a new outlier-resistant recursive filtering problem (RF) is studied for a class of multisensor multirate networked systems under the weighted try-once-discard (WTOD) protocol. The sensors are sampled with a period that is different from the state updating period of the system. In order to lighten the communication burden and alleviate the network congestions, the WTOD protocol is implemented in the sensor-to-filter channel to schedule the order of the data transmission of the sensors. In the case of the measurement outliers, a saturation function is employed in the filter structure to constrain the innovations contaminated by the measurement outliers, thereby maintaining satisfactory filtering performance. By resorting to the solution to a matrix difference equation, an upper bound is first obtained on the covariance of the filtering error, and the gain matrix of the filter is then characterized to minimize the derived upper bound. Furthermore, the exponential boundedness of the filtering error dynamics is analyzed in the mean square sense. Finally, the usefulness of the proposed outlier-resistant RF scheme is verified by simulation examples.

The sliding-mode control (SMC) problem is studied in this article for state-saturated systems over a class of time-varying fading channels. The underlying fading channels, whose channel fading amplitudes (characterized by the expectation and variance) are allowed to be different, are modeled as a finite-state Markov process. A key feature of the problem addressed is to use a hidden Markov mode detector to estimate the actual network mode. The novel model of hidden Markov fading channels (HMFCs) is shown to be more general yet practical than the existing fading channel models. Based on a linear sliding surface, a switching-type SMC law is dedicatedly constructed by just using the estimated network mode. By exploiting the concept of stochastic Lyapunov stability and the approach of hidden Markov models, sufficient conditions are obtained for the resultant SMC systems that ensure both the mean-square stability and the reachability with a sliding region. With the aid of the Hadamard product, a binary genetic algorithm (GA) is developed to solve the proposed SMC design problem subject to some nonconvex constraints induced by the state saturations and the fading channels, where the proposed GA is based on the objective function for optimal reachability. Finally, a numerical example is employed to verify the proposed GA-assisted SMC scheme over the HMFCs.

This article is concerned with the containment control problem for a class of multiagent systems (MASs) based on event-triggered output feedback. For homogeneous MASs, a distributed observer-based containment control protocol and a distributed hybrid event-triggered scheme are proposed. The observers, containment controllers, and event-triggered conditions are designed simultaneously by means of the matrix transformation technique. The proposed event-triggered mechanism includes not only the traditional triggering function but also a running time upper bound, which saves the communication workload and improves system performance. Furthermore, based on the output regulation framework, a hybrid distributed event-triggered scheme and a distributed containment control protocol for heterogeneous MASs are developed, where the dynamics of the followers vary. Multiply unmanned aerial vehicles are modeled as a linear MAS to verify the effectiveness of the presented algorithm.

This article is concerned with the multitask multiagent allocation problem via many-objective optimization for multiagent systems (MASs). First, a novel layered MAS model is constructed to address the multitask multiagent allocation problem that includes both the original task simplification and the many-objective allocation. In the first layer of the model, the deep Q-learning method is introduced to simplify the prioritization of the original task set. In the second layer of the model, the modified shift-based density estimation (MSDE) method is put forward to improve the conventional strength Pareto evolutionary algorithm 2 (SPEA2) in order to achieve many-objective optimization on task assignments. Then, an MSDE-SPEA2-based method is proposed to tackle the many-objective optimization problem with objectives including task allocation, makespan, agent satisfaction, resource utilization, task completion, and task waiting time. As compared with the existing allocation methods, the developed method in this article exhibits an outstanding feature that the task assignment and the task scheduling are carried out simultaneously. Finally, extensive experiments are conducted to: 1) verify the validity of the proposed model and the effectiveness of two main algorithms and 2) illustrate the optimal solution for task allocation and efficient strategy for task scheduling under different scenarios.

Friedreich's ataxia (FRDA) is the most common inherited ataxia that causes progressive damage of nervous systems and performance deterioration of physical movements. FRDA baseline data analysis plays a crucial role in advancing the disease research, where the main obstacle comes from the baseline data collection primarily due to the degenerative symptoms of the FRDA patients. Inspired by the nowadays popular collaborative filtering (CF) method, a new FRDA baseline data collection algorithm is proposed in this article, with which the patients (or their families) are only required to provide certain reliable baseline data acquired from home and the uncertain/missing parts of the data can then be predicted with acceptable accuracy by utilizing existing patient information. The framework of the proposed algorithm is constructed based on a novel hybrid model combining the merits of model- and memory-based CF methods, thereby facilitating the baseline data collection with improved prediction accuracy. The proposed hybrid algorithm exhibits the following two main features: when a patient does not have neighbors sharing similar baseline data, the model-based CF component is activated to employ certain clustering method to find similar neighbors based on their attributes; and in the case that a patient does have neighbors, a novel similarity measure, which accounts for more statistical characteristics by integrating rating habits and degree of co-rated items, is developed in the memory-based component of the algorithm in order to adjust initial similarities between the patients. To evaluate the advantages of the proposed algorithm, the Scale for the Assessment and Rating of Ataxia is selected from the European FRDA Consortium for Translational Studies database. Experimental results demonstrate that our proposed hybrid CF approach is superior to other conventional approaches.

This article is concerned with the synchronization control problem for a class of discrete-time dynamical networks with mixed delays and switching topology. The saturation phenomenon of physical actuators is specifically considered in designing feedback controllers. By exploring the mixed-delay-dependent sector conditions in combination with the piecewise Lyapunov-like functional and the average-dwell-time switching, a sufficient condition is first established under which all trajectories of the error dynamics are bounded for admissible initial conditions and nonzero external disturbances, while the l2- l∞performance constraint is satisfied. Furthermore, the exponential stability of the error dynamics is ensured for admissible initial conditions in the absence of disturbances. Second, by using some congruence transformations, the explicit condition guaranteeing the existence of desired controller gains is obtained in terms of the feasibility of a set of linear matrix inequalities. Then, three convex optimization problems are formulated regarding the disturbance tolerance, the l2- l∞performance, and the initial condition set, respectively. Finally, two simulation examples are given to show the effectiveness and merits of the proposed results.

In this article, the robust finite-horizon state estimation problem is investigated for a class of time-varying complex networks (CNs) under the random access protocol (RAP) through available measurements from only a part of network nodes. The underlying CNs are subject to randomly occurring uncertainties, randomly occurring multiple delays, as well as sensor saturations. Several sequences of random variables are employed to characterize the random occurrences of parameter uncertainties and multiple delays. The RAP is adopted to orchestrate the data transmission at each time step based on a Markov chain. The aim of the addressed problem is to design a series of robust state estimators that make use of the available measurements from partial network nodes to estimate the network states, under the RAP and over a finite horizon, such that the estimation error dynamics achieves the prescribed$H_{\infty }$performance requirement. Sufficient conditions are provided for the existence of such time-varying partial-node-based$H_{\infty }$state estimators via stochastic analysis and matrix operations. The desired estimators are parameterized by solving certain recursive linear matrix inequalities. The effectiveness of the proposed state estimation algorithm is demonstrated via a simulation example.

In this brief, a new outlier-resistant state estimation (SE) problem is addressed for a class of recurrent neural networks (RNNs) with mixed time-delays. The mixed time delays comprise both discrete and distributed delays that occur frequently in signal transmissions among artificial neurons. Measurement outputs are sometimes subject to abnormal disturbances (resulting probably from sensor aging/outages/faults/failures and unpredictable environmental changes) leading to measurement outliers that would deteriorate the estimation performance if directly taken into the innovation in the estimator design. We propose to use a certain confidence-dependent saturation function to mitigate the side effects from the measurement outliers on the estimation error dynamics (EEDs). Through using a combination of Lyapunov-Krasovskii functional and inequality manipulations, a delay-dependent criterion is established for the existence of the outlier-resistant state estimator ensuring that the corresponding EED achieves the asymptotic stability with a prescribed H∞performance index. Then, the explicit characterization of the estimator gain is obtained by solving a convex optimization problem. Finally, numerical simulation is carried out to demonstrate the usefulness of the derived theoretical results.

A single latent factor (LF)-dependent, nonnegative, and multiplicative update (SLF-NMU) learning algorithm is highly efficient in building a nonnegative LF (NLF) model defined on a high-dimensional and sparse (HiDS) matrix. However, convergence characteristics of such NLF models are never justified in theory. To address this issue, this study conducts rigorous convergence analysis for an SLF-NMU-based NLF model. The main idea is twofold: 1) proving that its learning objective keeps nonincreasing with its SLF-NMU-based learning rules via constructing specific auxiliary functions; and 2) proving that it converges to a stable equilibrium point with its SLF-NMU-based learning rules via analyzing the Karush-Kuhn-Tucker (KKT) conditions of its learning objective. Experimental results on ten HiDS matrices from real applications provide numerical evidence that indicates the correctness of the achieved proof.

In this article, a novel proportional–integral observer (PIO) design approach is proposed for the nonfragile$H_{\infty }$state estimation problem for a class of discrete-time recurrent neural networks with time-varying delays. The developed PIO is equipped with more design freedom leading to better steady-state accuracy compared with the conventional Luenberger observer. The phenomena of randomly occurring gain variations, which are characterized by the Bernoulli distributed random variables with certain probabilities, are taken into consideration in the implementation of the addressed PIO. Attention is focused on the design of a nonfragile PIO such that the error dynamics of the state estimation is exponentially stable in a mean-square sense, and the prescribed$H_{\infty }$performance index is also achieved. Sufficient conditions for the existence of the desired PIO are established by virtue of the Lyapunov–Krasovskii functional approach and the matrix inequality technique. Finally, a simulation example is provided to demonstrate the effectiveness of the proposed PIO design scheme.

This article is concerned with the distributed recursive filtering of cyber-physical systems consisting of a set of spatially distributed subsystems. Due to the vulnerability of communication networks, the transmitted data among subsystems could be subject to deception attacks. In this article, attackers do not have enough knowledge of the full network topology and the system parameters and therefore cannot carry out stealth attacks. For this scenario, a defense strategy dependent on the received innovation is proposed to identify the occurring attacks as far as possible. In light of identified attacks, a novel distributed filter is constructed and its gain is designed via a set of recursive formulas on the upper bound of covariance of filtering errors. The utilization of upper bound is to avoid the calculational challenge of cross-covariance matrices and realize the requirement of distributed implementation, simultaneously. Furthermore, the developed scheme only depends on the neighboring information and the information from the subsystem itself, and thereby satisfying the requirement of the scalability. Finally, a standard IEEE 39-bus power system is utilized to verify the effectiveness of the proposed filtering scheme.

This paper deals with the scalable distributed H∞-consensus filtering problem for a class of discrete time-varying systems subject to multiplicative noises and censored measurements over sensor networks (SNs). For the underlying SN, it is assumed that only the measurement outputs from partial sensor nodes are available. Also, the phenomenon of censored measurements is taken into account to reflect the limited capability in measuring. A new H∞-consensus performance index is put forward to evaluate the disturbance rejection level of the filters against the simultaneous presence of external disturbances, initial conditions, as well as censoring effects. By utilizing the vector dissipativity theory and the recursive matrix inequality technique, sufficient conditions are established under which the prescribed H∞-consensus performance index is achieved. The parameters of the desired distributed filters are calculated via solving certain matrix inequalities, where such a calculation is conducted in a local sense so as to preserve the scalability of the filter design. Finally, a numerical simulation example is provided to demonstrate the validity and applicability of the proposed filtering strategy.

This article addresses the prediction-based distributed filtering problem for a class of time-varying nonlinear stochastic systems with communication delays and missing measurements through the sensor networks. The phenomenon of the missing measurements is depicted by a set of Bernoulli distributed random variables, where each sensor node possesses its own missing probability. The communication delays are taken into account, which commonly occur during the estimation exchanges among the sensor nodes with communication links. A new prediction-based suboptimal distributed filter is designed by taking the missing probabilities and the prediction estimation into account, which has the advantages on the active compensation of the impacts caused by the missing measurements and communication delays. That is, a new compensation filtering method within the time-varying framework is presented based on the predictive estimation and the innovation measurements. A locally minimum upper bound matrix for the estimation error covariance is obtained by properly designing the distributed filter gain at every sampling step. Furthermore, the performance analysis problem of the prediction-based distributed filtering algorithm is discussed by providing the desirable theoretical derivations. Finally, some comparative simulations are used to show the advantages of the presented prediction-based distributed filtering strategy under delay compensation mechanism.

Non-negative latent factor (NLF) models can efficiently acquire useful knowledge from high-dimensional and sparse (HiDS) matrices filled with non-negative data. Single latent factor-dependent, non-negative and multiplicative update (SLF-NMU) is an efficient algorithm for building an NLF model on an HiDS matrix, yet it suffers slow convergence. A momentum method is frequently adopted to accelerate a learning algorithm, but it is incompatible with those implicitly adopting gradients like SLF-NMU. To build a fast NLF (FNLF) model, we propose a generalized momentum method compatible with SLF-NMU. With it, we further propose a single latent factor-dependent non-negative, multiplicative and momentum-incorporated update algorithm, thereby achieving an FNLF model. Empirical studies on six HiDS matrices from industrial application indicate that an FNLF model outperforms an NLF model in terms of both convergence rate and prediction accuracy for missing data. Hence, compared with an NLF model, an FNLF model is more practical in industrial applications.

High-dimensional and sparse (HiDS) data with non-negativity constraints are commonly seen in industrial applications, such as recommender systems. They can be modeled into an HiDS matrix, from which non-negative latent factor analysis (NLFA) is highly effective in extracting useful features. Preforming NLFA on an HiDS matrix is ill-posed, desiring an effective regularization scheme for avoiding overfitting. Current models mostly adopt a standard L2scheme, which does not consider the imbalanced distribution of known data in an HiDS matrix. From this point of view, this paper proposes an instance-frequency-weighted regularization (IR) scheme for NLFA on HiDS data. It specifies the regularization effects on each latent factors with its relevant instance count, i.e., instance-frequency, which clearly describes the known data distribution of an HiDS matrix. By doing so, it achieves finely grained modeling of regularization effects. The experimental results on HiDS matrices from industrial applications demonstrate that compared with an L2scheme, an IR scheme enables a resultant model to achieve higher accuracy in missing data estimation of an HiDS matrix.

In this article, the$H_{\infty }$control problem is investigated for a class of discrete-time singularly perturbed systems (DTSPSs) with time-delays and sensor saturations. In order to save the network bandwidth with satisfactory communication reliability during the signal transmissions, a dynamic event-triggered mechanism (DETM) is put forward to regulate the data-packet transmissions from the saturated sensors to the proposed controller. By constructing a novel Lyapunov–Krasovskii functional dependent on the singular perturbation parameter (SPP), the dynamics of the DTSPSs under the DETM are rigorously analyzed, and an output-feedback controller design scheme is then developed to ensure that, for any SPP not exceeding a prescribed upper bound, the closed-loop system is asymptotically stable with a guaranteed$H_{\infty }$performance index. By resorting to the convex programming techniques, the desired controller gain is parameterized in light of the feasible solutions to a set of matrix inequalities. Finally, the effectiveness and merits of the proposed algorithm are demonstrated by a simulation example.

Optimization is an important research field, especially in engineering, physical sciences, and economics. The main purpose of optimization is to maximize the profit and minimize the cost of production as well as the loss of the system. Evolutionary computation algorithms, such as the genetic algorithm and the particle swarm optimization (PSO) algorithm have been successfully employed in solving various optimization problems. Owing to its application potential and promising performance in discovering the optimal solution, the PSO algorithm has been recognized as a powerful optimization technique and attracted an ever-increasing interest in the evolutionary computation community. In this article, a novel$N$-state Markovian jumping PSO (NS-MJPSO) algorithm is presented where the velocity updating equation is adjusted based on the state evolution governed by a Markov chain. The performance of the proposed NS-MJPSO algorithm is evaluated via some widely used mathematical benchmark functions. The experimental results demonstrate that the developed NS-MJPSO algorithm outperforms some currently popular PSO algorithms on the widely used benchmark functions.

This article deals with the observer-based proportional-integral-derivative (PID) security control problem for a kind of linear discrete time-delay systems subject to cyber-attacks. The cyber-attacks, which include both denial-of-service and deception attacks, are allowed to be randomly occurring as regulated by two sequences of Bernoulli distributed random variables with certain probabilities. A novel observer-based PID controller is proposed such that the closed-loop system achieves the desired security level and the quadratic cost criterion (QCC) has an upper bound. Sufficient conditions are derived under which the exponentially mean-square input-to-state stability is guaranteed and the desired security level is then achieved. Subsequently, an upper bound of the QCC is obtained and the explicit expression of the desired PID controller is also parameterized. Finally, the validity of the developed design approach is verified via an illustrative example.

The finite-time state estimation issue is addressed in this paper for discrete time-delayed neural networks (NNs). More than one communication channel is utilized to improve the communication performance. The transmission delays of each channel are modeled by a family of stochastic variables which are independent and identically distributed. The main purpose of this paper is to construct an appropriate state estimation scheme under which the corresponding state estimation error dynamics is finite-time bounded in the mean square. By employing the stochastic analysis approach and introducing a special Lyapunov-like functional, we have developed certain sufficient conditions to achieve the prescribed estimation performance. Furthermore, the exact expressions of the achieved estimator parameters are given by solving a special minimization problem subject to certain inequality constraints. Finally, we propose an illustrative simulation to examine the correctness, as well as the effectiveness, of our proposed state estimation method.

The full information estimation (FIE) problem is addressed for discrete time-varying systems (TVSs) subject to the effects of a round-Robin (RR) protocol. A shared communication network is adopted for data transmissions between sensor nodes and the state estimator. In order to avoid data collisions in signal transmission, only one sensor node could have access to the network and communicate with the state estimator per time instant. The so-called RR protocol, which is also known as the token ring protocol, is employed to orchestrate the access sequence of sensor nodes, under which the chosen sensor node communicating with the state estimator could be modeled by a periodic function. A novel recursive FIE scheme is developed by defining a modified cost function and using a so-called “backward-propagation-constraints.” The modified cost function represents a special global estimation performance. The solution of the proposed FIE scheme is achieved by solving a minimization problem. Then, the recursive manner of such a solution is studied for the purpose of online applications. For the purpose of ensuring the estimation performance, sufficient conditions are obtained to derive the upper bound of the norm of the state estimation error (SEE). Finally, two illustrative examples are proposed to demonstrate the effectiveness of the developed estimation algorithm.

This paper is concerned with the moving horizon (MH) estimation issue for a type of networked nonlinear systems (NNSs) with the so-called random access (RA) protocol scheduling effects. To handle the signal transmissions between sensor nodes and the MH estimator, a constrained communication channel is employed whose channel constraints implies that at each time instant, only one sensor node is permitted to access the communication channel and then send its measurement data. The RA protocol, whose scheduling behavior is characterized by a discrete-time Markov chain (DTMC), is utilized to orchestrate the access sequence of sensor nodes. By extending the robust MH estimation method, a novel nonlinear MH estimation scheme and the corresponding approximate MH estimation scheme are developed to cope with the state estimation task. Subsequently, some sufficient conditions are established to guarantee that the estimation error is exponentially ultimately bounded in mean square. Based on that the main results are further specialized to linear systems with the RA protocol scheduling. Finally, two numerical examples and the corresponding figures are provided to verify the effectiveness/correctness of the developed MH estimation scheme and approximate MH estimation scheme.

This study is concerned with the state estimation issue for a kind of delayed artificial neural networks with multiplicative noises. The occurrence of the time delay is in a random way that is modeled by a Bernoulli distributed stochastic variable whose occurrence probability is time-varying and confined within a given interval. A gain-scheduled approach is proposed for the estimator design to accommodate the time-varying nature of the occurrence probability. For the sake of utilizing the communication resource as efficiently as possible, a dynamic event triggering mechanism is put forward to orchestrate the data delivery from the sensor to the estimator. Sufficient conditions are established to ensure that, in the simultaneous presence of the external noises, the randomly occurring time delays with time-varying occurrence probability as well as the dynamic event triggering communication protocol, the estimation error is exponentially ultimately bounded in the mean square. Moreover, the estimator gain matrices are explicitly calculated in terms of the solution to certain easy-to-solve matrix inequalities. Simulation examples are provided to show the validity of the proposed state estimation method.

This paper is concerned with the problem of distributed H∞ filtering for switched stochastic time-delay systems with fading measurements over sensor networks. The underlying target plants are subject to fading measurements where the fading rates are described by continuous-time random variables with known statistical properties dependent on the system modes. The adjacency matrices characterizing the topology of the sensor networks are also allowed to be mode-dependent. Based on the multiple Lyapunov functional approach and average dwell-time concept, the distributed H∞filter is designed by means of the convex optimization scheme. A dedicated technique is developed via a simple algebraic equality in order to avoid solving a transcendental equation used in the existing results. With the designed filter, the error dynamics of the state estimation is guaranteed to have the mean-square exponential stability with a prescribed H∞disturbance attenuation level. Finally, a numerical example is used to demonstrate the effectiveness of the method.

In this paper, the neural-network (NN)-based consensus control problem is investigated for a class of discrete-time nonlinear multiagent systems (MASs) with a leader subject to input constraints. Relative measurements related to local tracking errors are collected via some smart sensors. A local nonquadratic cost function is first introduced to evaluate the control performance with input constraints. Then, in view of the relative measurements, an NN-based observer under the event-triggered mechanism is designed to reconstruct the dynamics of the local tracking errors, where the adopted event-triggered condition has a time-dependent threshold and the weight of NNs is updated via a new adaptive tuning law catering to the employed event-triggered mechanism. Furthermore, an ideal control policy is developed for the addressed consensus control problem while minimizing the prescribed local nonquadratic cost function. Moreover, an actor-critic NN scheme with online learning is employed to realize the obtained control policy, where the critic NN is a three-layer structure with powerful approximation capability. Through extensive mathematical analysis, the consensus condition is established for the underlying MAS, and the boundedness of the estimated errors is proven for actor and critic NN weights. In addition, the effect from the adopted event-triggered mechanism on the local cost is thoroughly discussed, and the upper bound of the corresponding increment is derived in comparison with time-triggered cases. Finally, a simulation example is utilized to illustrate the usefulness of the proposed controller design scheme.

An event-triggered distributed state estimation problem is investigated for a class of discrete-time nonlinear stochastic systems with unknown parameters over sensor networks (SNs) subject to switched topologies. An event-triggered communication strategy is employed to govern the information broadcast and reduce the unnecessary resource consumption. Based on the adopted communication strategy, a distributed state estimator is designed to estimate the plant states and also identify the unknown parameters. In the framework of input-to-state stability, sufficient conditions with an average dwell time are established to ensure the boundedness of estimation errors in mean-square sense. In addition, the gains of the designed estimators are dependent on the solution of a set of matrix inequalities whose dimensions are unrelated to the scale of underlying SNs, thereby fulfill the scalability requirement. Finally, an illustrative simulation is utilized to verify the feasibility of the proposed design scheme.

In this paper, the partial-nodes-based state estimators (PNBSEs) are designed for a class of uncertain complex networks subject to finite-distributed delays, stochastic disturbances, as well as randomly occurring deception attacks (RODAs). In consideration of the likely unavailability of the output signals in harsh environments from certain network nodes, only partial measurements are utilized to accomplish the state estimation task for the addressed complex network with norm-bounded uncertainties in both the network parameters and the inner couplings. The RODAs are taken into account to reflect the compromised data transmissions in cyber security. We aim to derive the gain parameters of the estimators such that the overall estimation error dynamics satisfies the specified security constraint in the simultaneous presence of stochastic disturbances and deception signals. Through intensive stochastic analysis, sufficient conditions are obtained to guarantee the desired security performance for the PNBSEs, based on which the estimator gains are acquired by solving certain matrix inequalities with nonlinear constraints. A simulation study is carried out to testify the security performance of the presented state estimation method.

This paper addresses the H∞state estimation issue for a sort of memristive neural networks in the discrete-time setting under randomly occurring mixed time-delays and fading measurements. The main purpose of the addressed issue is to propose a state estimator design algorithm that ensures the error dynamics of the state estimation to be stochastically stable with a prespecified H∞disturbance attenuation index. We put forward certain switching functions to account for the discrete-time yet state-dependent characteristics of the memristive connection weights. By resorting to the robust analysis theory and the Lyapunov-functional analysis theory, we derive some sufficient conditions to guarantee the desired estimation performance. The derived sufficient conditions rely not only on the size of discrete time-delays and the probability distribution law of the distributed time-delays but also on the statistics information of the coefficients of the adopted Rice fading model. Based on the established existence conditions, the gain matrices of the desired estimator are obtained by means of the feasibility of a set of matrix inequalities that can be checked efficiently via available software packages. Finally, the numerical simulation results are provided to show the validity of the main results.

In this paper, the distributed set-membership filtering problem is dealt with for a class of time-varying multirate systems in sensor networks with the communication protocol. For relieving the communication burden, the round-Robin (RR) protocol is exploited to orchestrate the transmission order, under which each sensor node only broadcasts partial information to both the corresponding local filter and its neighboring nodes. In order to meet the practical transmission requirements as well as reduce communication cost, the multirate strategy is proposed to govern the sampling/update rate of the plant, the sensors, and the filters. By means of the lifting technique, the augmented filtering error system is established with a unified sampling rate. The main purpose of the addressed filtering problem is to design a set of distributed filters such that, in the simultaneous presence of the RR transmission protocol, the multirate mechanism, and the bounded noises, there exists a certain ellipsoid that includes all possible error states at each time instant. Then, the desired distributed filter gains are obtained by minimizing such an ellipsoid in the sense of the minimum trace of the weighted matrix. The proposed resource-efficient filtering algorithm is of a recursive form, thereby facilitating the online implementation. A numerical simulation example is given to demonstrate the effectiveness of the proposed protocol-based distributed filter design method.

This article is concerned with the distributed recursive filtering issue for stochastic discrete time-varying systems subjected to both state saturations and round-robin (RR) protocols over sensor networks. The phenomenon of state saturation is considered to better describe practical engineering. The RR protocol is introduced to mitigate a network burden by determining which component of the sensor node has access to the network at each transmission instant. The purpose of the issue under consideration is to construct a distributed recursive filter such that a certain filtering error covariance's upper bound can be found and the corresponding filter parameters' explicit expression is given with both state saturations and RR protocols. By taking advantage of matrix difference equations, a filtering error covariance's upper bound can be presented and then be minimized by appropriately designing filter parameters. In particular, by using a matrix simplification technique, the sensor network topology's sparseness issue can be tackled. Finally, the feasibility for the addressed filtering scheme is demonstrated by an example.

This paper proposes a novel event-triggered (ET) adaptive neural control scheme for a class of discrete-time nonlinear systems in a strict-feedback form. In the proposed scheme, the ideal control input is derived in a recursive design process, which relies on system states only and is unrelated to virtual control laws. In this case, the high-order neural networks (NNs) are used to approximate the ideal control input (but not the virtual control laws), and then the corresponding adaptive neural controller is developed under the ET mechanism. A modified NN weight updating law, nonperiodically tuned at triggering instants, is designed to guarantee the uniformly ultimate boundedness (UUB) of NN weight estimates for all sampling times. In virtue of the bounded NN weight estimates and a dead-zone operator, the ET condition together with an adaptive ET threshold coefficient is constructed to guarantee the UUB of the closed-loop networked control system through the Lyapunov stability theory, thereby largely easing the network communication load. The proposed ET condition is easy to implement because of the avoidance of: 1) the use of the intermediate ET conditions in the backstepping procedure; 2) the computation of virtual control laws; and 3) the redundant triggering of events when the system states converge to a desired region. The validity of the presented scheme is demonstrated by simulation results.

This paper focuses on the observer-based output-feedback control (OBOFC) problem for a class of discrete-time strict-feedback nonlinear systems (DTSFNSs) with both multiplicative process noises and additive measurement noises. A state observer is first designed to estimate immeasurable system states, and then a novel observer-based backstepping control framework is proposed for DTSFNSs with known model information. To be specific, virtual control laws and the actual control law are derived using a variable substitution method that gets rid of the repeated accumulation of measurement noises in the recursive process. Furthermore, for technical derivation, the multiplicative noise is successively bounded by state estimation errors and controlled errors. Stability conditions are obtained to guarantee the exponential mean-square boundedness of the closed-loop system. Moreover, the nonlinear modeling uncertainties are taken into account to better reflect engineering practices. In virtue of the universal approximation property of fuzzy-logic systems, a fuzzy observer and the corresponding fuzzy output-feedback controller are simultaneously constructed to derive the stability criteria by using novel weight updated laws. Simulation studies are performed to test the validity of the proposed OBOFC scheme.

This paper addresses the recursive filtering problem for shift-varying linear repetitive processes (LRPs) with limited network resources. To reduce the resource occupancy, a novel event-triggered strategy is proposed where the concern is to broadcast those necessary measurements to update the innovation information only when certain events appear. The primary goal of this paper is to design a recursive filter rendering that, under the event-triggered communication mechanism, an upper bound (UB) on the filtering error variance is ensured and then optimized by properly determining the filter gains. As a distinct kind of 2-D systems, the LRPs are cast into a general Fornasini-Marchesini model by using the lifting technique. A new definition of the triggering-shift sequence is introduced and an event-triggered rule is then constructed for the transformed system. With the aid of mathematical induction, the filtering error variance is guaranteed to have a UB which is subsequently optimized with appropriate filter parameters via solving two series of Riccati-like difference equations. Theoretical analysis further reveals the monotonicity of the filtering performance with regard to the event-triggering threshold. Finally, an illustrative simulation is given to show the feasibility of the designed filtering scheme.

In this article, the design problem of the proportional-integral observer (PIO) is investigated for a class of discrete-time multidelayed recurrent neural networks (RNNs). In the addressed RNN model, the delays occurring in the information interconnections are allowed to be different, and the phenomenon of sensor saturation is taken into consideration in the measurement model. A novel dynamic event-triggered protocol is employed in the data transmission from sensors to the observer with hope to improve the efficiency of resource utilization, where the threshold parameters are adaptive to the dynamical environment. By virtue of the Lyapunov-like approach, a general framework is established for examining the boundedness of the estimation errors in mean-square sense, and the ultimate bound of the error dynamics is also acquired. Subsequently, the explicit expression of the desired PIO is parameterized by using the matrix inequality techniques. Finally, a simulation example is utilized to verify the effectiveness and superiority of the proposed PIO design scheme.

This article investigates the mixed H2/H∞state estimation problem for a class of discrete-time switched complex networks with random coupling strengths through redundant communication channels. A sequence of random variables satisfying certain probability distributions is employed to describe the stochasticity of the coupling strengths. A redundant-channel-based data transmission mechanism is adopted to enhance the reliability of the transmission channel from the sensor to the estimator. The purpose of the addressed problem is to design a state estimator for each node, such that the error dynamics achieves both the stochastic stability (with probability 1) and the prespecified mixed H2/H∞performance requirement. By using the switched system theory, an extensive stochastic analysis is carried out to derive the sufficient conditions ensuring the stochastic stability as well as the mixed H2/H∞performance index. The desired state estimator is also parameterized by resorting to the solutions to certain convex optimization problems. A numerical example is provided to illustrate the validity of the proposed estimation scheme.

In this article, the recursive filtering problem is investigated for a class of time-varying complex networks with state saturations and random coupling strengths under an event-triggering transmission mechanism. The coupled strengths among nodes are characterized by a set of random variables obeying the uniform distribution. The event-triggering scheme is employed to mitigate the network data transmission burden. The purpose of the problem addressed is to design a recursive filter such that in the presence of the state saturations, event-triggering communication mechanism, and random coupling strengths, certain locally optimized upper bound is guaranteed on the filtering error covariance. By using the stochastic analysis technique, an upper bound on the filtering error covariance is first derived via the solution to a set of matrix difference equations. Next, the obtained upper bound is minimized by properly parameterizing the filter parameters. Subsequently, the boundedness issue of the filtering error covariance is studied. Finally, two numerical simulation examples are provided to illustrate the effectiveness of the proposed algorithm.

In this paper, a new recursive state estimation problem is discussed for a class of discrete time-varying stochastic complex networks with uncertain inner coupling and signal quantization under the error-variance constraints. The coupling strengths are allowed to be varying within certain intervals, and the measurement signals are subject to the quantization effects before being transmitted to the remote estimator. The focus of the conducted topic is on the design of a variance-constrained state estimation algorithm with the aim to ensure a locally minimized upper bound on the estimation error covariance at every sampling instant. Furthermore, the boundedness of the resulting estimation error is analyzed, and a sufficient criterion is established to ensure the desired exponential boundedness of the state estimation error in the mean square sense. Finally, some simulations are proposed with comparisons to illustrate the validity of the newly developed variance-constrained estimation method.

In this brief, the partial-neurons-based passivity-guaranteed state estimation (SE) problem is examined for a class of discrete-time artificial neural networks with randomly occurring time delays. The measurement outputs available utilized for the SE are allowed to be available only at a fraction of neurons in the networks. A Bernoulli-distributed random variable is employed to characterize the random nature of the occurrence of time delays. By resorting to the Lyapunov-Krasovskii functional method as well as the stochastic analysis technique, sufficient criteria are provided for the existence of the desired state estimators ensuring the estimation error dynamics to achieve the asymptotic stability in the mean square with a guaranteed passivity performance level. In addition, the parameterization of the estimator gain is acquired by solving a convex optimization problem. Finally, the validity of the obtained theoretical results is illustrated via a numerical simulation example.

This article deals with the recursive filtering issue for a class of nonlinear complex networks (CNs) with switching topologies, random sensor failures and dynamic event-triggered mechanisms. A Markov chain is utilized to characterize the switching behavior of the network topology. The phenomenon of sensor failures occurs in a random way governed by a set of stochastic variables obeying certain probability distributions. In order to save communication cost, a dynamic event-triggered transmission protocol is introduced into the transmission channel from the sensors to the recursive filters. The objective of the addressed problem is to design a set of dynamic event-triggered filters for the underlying CN with a certain guaranteed upper bound (on the filtering error covariance) that is then locally minimized. By employing the induction method, an upper bound is first obtained on the filtering error covariance and subsequently minimized by properly designing the filter parameters. Finally, a simulation example is provided to demonstrate the effectiveness of the proposed filtering scheme.

This article is concerned with the distributed filtering problem for a class of discrete complex networks over time-varying topology described by a sequence of variables. In the developed scalable filtering algorithm, only the local information and the information from the neighboring nodes are used. As such, the proposed filter can be implemented in a truly distributed manner at each node, and it is no longer necessary to have a certain center node collecting information from all the nodes. The aim of the addressed filtering problem is to design a time-varying filter for each node such that an upper bound of the filtering error covariance is ensured and the desired filter gain is then calculated by minimizing the obtained upper bound. The filter is established by solving two sets of recursive matrix equations, and thus, the algorithm is suitable for online application. Sufficient conditions are provided under which the filtering error is exponentially bounded in mean square. The monotonicity of the filtering error with respect to the coupling strength is discussed as well. Finally, an illustrative example is presented to demonstrate the feasibility and effectiveness of our distributed filtering strategy.

This article tackles the recursive filtering problem for a class of stochastic nonlinear time-varying complex networks (CNs) suffering from both the state saturations and the deception attacks. The nonlinear inner coupling and the state saturations are taken into account to characterize the nonlinear nature of CNs. From the defender's perspective, the randomly occurring deception attack is governed by a set of Bernoulli binary distributed white sequence with a given probability. The objective of the addressed problem is to design a state-saturated recursive filter such that, in the simultaneous presence of the state saturations and the randomly occurring deception attacks, a certain upper bound is guaranteed on the filtering error covariance, and such an upper bound is then minimized at each time instant. By employing the induction method, an upper bound on the filtering error variance is first constructed in terms of the solutions to a set of matrix difference equations. Subsequently, the filter parameters are appropriately designed to minimize such an upper bound. Finally, a numerical simulation example is provided to demonstrate the feasibility and usefulness of the proposed filtering scheme.

This paper investigates the event-triggered (ET) tracking control problem for a class of discrete-time strict-feedback nonlinear systems subject to both stochastic noises and limited controller-to-actuator communication capacities. The ET mechanism with fixed triggering threshold is designed to decide whether the current control signal should be transmitted to the actuator. A systematic framework is developed to construct a novel adaptive neural controller by directly applying the backstepping procedure to the underlying system. The proposed framework overcomes the noncausality problem, avoids the possible controller-related singularity problem, and gets rid of the neural approximation of the virtual control laws. Under the ET mechanism, the corresponding ET-based actuator is put forward by introducing an ET threshold compensation operator. Such a compensation operator (with an adjustable design parameter) is subtly designed based on a hyperbolic tangent function and a sign function. The threshold compensation error is analytically characterized in terms of a time-varying parameter, and the error bound is shown to be relatively small that is dependent on the adjustable design parameter. Compared with the traditional ET-based actuator without the compensation operator, the proposed ET-based actuator exhibits several distinguished features including: 1) improvement of the tracking accuracy (especially at the triggering instants); 2) further mitigation of the communication load; and 3) enlargement of the allowable range of the ET threshold. These features are illustrated by numerical and practical examples.

The distributed state estimation problem is examined for a kind of nonlinear time-varying stochastic systems through sensor networks (SNs) with randomly switching topologies as well as redundant channels. The random switches of the topologies for SNs are governed by Markovian jumping parameters and the redundant channels are introduced to help improve the capability of the network communication. We are interested in designing distributed state estimators so that the estimation error dynamics is confirmed to reach a prescribed level of average H∞performance in terms of a finite horizon. Through intensive stochastic analysis, we acquire some sufficient conditions that guarantee the existence of the expected state estimators whose gain parameters are obtained recursively by means of the solution to a series of matrix inequalities. A numerical simulation is carried out to illustrate the validity of the developed state estimation algorithm.

This paper addresses the nonfragile sensor fault estimation problem for a class of two-dimensional (2-D) nonlinear systems. The underlying system is described by the well-known Fornasini-Marchesini model. The system parameters are subject to abrupt changes regulated by the Markovian process for which the entries of the mode transition probability matrix are partially accessible. A novel 2-D nonfragile state estimator is constructed to achieve the sensor fault estimation where the estimator gains are allowed to be randomly perturbed. Then, together with the Lyapunov stability theory, the stochastic analysis techniques are employed to derive the sufficient conditions that guarantee the following three performance requirements: 1) the exponential stability of the estimation error dynamics; 2) the prespecified constraint on the energy-to-peak gain; and 3) the prespecified restriction on the prescribed power bound. Moreover, the estimator gains are parameterized by using the convex optimization method. Finally, a numerical example is provided to illustrate the effectiveness of the addressed estimation algorithm.

This paper is concerned with the state estimation problem for a class of discrete-time singularly perturbed systems with distributed time-delays. During the data transmission through a network channel of limited bandwidth, for the sake of collision avoidance and energy saving, a dynamic event-triggered scheme is employed to schedule the data communication from the sensors to the designed estimator. First, for a given singular perturbation parameter (SPP), by constructing a novel Lyapunov-Krasovskii SPP-dependent functional, sufficient conditions are obtained to guarantee the exponentially mean-square ultimate boundedness of the error dynamics of the state estimation. Furthermore, in the case that the SPP does not exceed a predefined upper bound, a design algorithm is developed for the desired state estimator ensuring that the error dynamics is exponentially mean-square ultimately bounded. In this case, by solving certain matrix inequalities, the estimator gain is characterized without needing to know the exact SPP (as long as it stays below the given upper bound). Moreover, the ultimate bound of the error dynamics is estimated. Finally, simulation results are given to confirm the validity and advantages of the proposed design scheme of the state estimator.

This paper is concerned with the N-step model predictive control (MPC) problem for a class of constrained systems with persistent bounded disturbances under the stochastic communication protocol (SCP). The control signals are transmitted to the plant via a shared network subject to a prescribed SCP for the purpose of avoiding data collisions. The SCP scheduling, which is governed by a Markov chain, is applied to orchestrate the transmission order of the controller nodes. Under the SCP, only one control node is allowed to update the control signal sent to the plant at each communication instant. Our aim is to design a set of desired controllers in the framework of N-step MPC such that the mean-square input-to-state stability of the closed-loop system is guaranteed. An optimization algorithm consisting of both off-line and online parts is developed to cope with the design problem of the N-step controller. Finally, a numerical example is utilized to illustrate the validity of the proposed N-step MPC strategy.

This paper is concerned with the robust finite-horizon filter design problem for a class of two-dimensional (2-D) time-varying systems with norm-bounded parameter uncertainties and incomplete measurements. The incomplete measurements cover randomly occurring sensor delays and missing measurements that are presented in a unified form by resorting to a stochastic Kronecker delta function. The occurrences of the sensor delays and missing measurements are governed by stochastic variables with known probability distributions. The main aim of the addressed problem is to design a recursive filter with appropriate gain parameters that ensure the local minimum of certain upper bound on the estimation error variance at each time instant. With the aid of the inductive approach and the 2-D Riccati-like difference equations, one of the first few attempts is made to tackle the robust filter design problem for 2-D uncertain systems with random sensor delays over a finite horizon. Sufficient conditions are provided for the existence of an upper bound on the estimation error variance, an algorithm is then developed to derive such an upper bound, and finally the desired filter is designed to minimize the obtained upper bound. The filter design procedure is of a recursive form that facilitates the online calculation. A numerical simulation is carried out to show the effectiveness of the developed filtering scheme.

This paper is concerned with the H∞proportional-integral-derivative (PID) control problem for a class of linear discrete-time systems with fading measurements. The fading measurements are governed by the Rice fading model whose coefficients are hypothesized to be a series of independent and identically distributed Gaussian variables. By utilizing the received measurements subject to fading phenomena, a novel output-feedback PID controller is proposed where the integral-loop (accumulation sum-loop for the discrete-time case) is equipped with the limited time-window in order to reduce the computational burden. The main objective of the addressed problem is to design a desired PID controller such that both the exponentially mean-square stability and the prescribed H∞performance are guaranteed for the closed-loop system in the presence of fading measurements. With the help of Lyapunov stability theory, a sufficient condition is obtained to guarantee the desired performance and, on the basis of such a condition, the synthesis issue of the PID controller is subsequently discussed, where the orthogonal decomposition combined with a free matrix is introduced to facilitate the controller design. Finally, a numerical example is exploited to demonstrate the usefulness and effectiveness of the presented control scheme.

The neural-network (NN)-based output-feedback control is considered for a class of stochastic nonlinear systems under round-Robin (RR) scheduling protocols. For the purpose of effectively mitigating data congestions and saving energies, the RR protocols are implemented and the resulting nonlinear systems become the so-called protocol-induced periodic ones. Taking such a periodic characteristic into account, an NN-based observer is first proposed to reconstruct the system states where a novel adaptive tuning law on NN weights is adopted to cater to the requirement of performance analysis. In addition, with the established boundedness of the periodic systems in the mean-square sense, the desired observer gain is obtained by solving a set of matrix inequalities. Then, an actor-critic NN scheme with a time-varying step length in adaptive law is developed to handle the considered control problem with terminal constraints over finite-horizon. Some sufficient conditions are derived to guarantee the boundedness of estimation errors of critic and actor NN weights. In view of these conditions, some key parameters in adaptive tuning laws are easily determined via elementary algebraic operations. Furthermore, the stability in the mean-square sense is investigated for the discussed issue in infinite horizon. Finally, a simulation example is utilized to illustrate the applicability of the proposed control scheme.

This paper is concerned with the distributed set-membership estimation for a discrete-time linear time-varying system over a resource-constrained wireless sensor network under the influence of unknown-but-bounded (UBB) process and measurement noise. Sensors collaborate among themselves by exchanging local measurements with only neighboring sensors in their sensing ranges. First, a new dynamic event-triggered transmission scheme (ETS) is developed to schedule the transmission of each sensor's local measurement. In contrast with the majority of existing static ETSs, the newly proposed dynamic ETS can result in larger average interevent times and thus less totally released data packets. Second, a criterion for designing desired event-triggered set-membership estimators is derived such that the system's true state always resides in each sensor's bounding ellipsoidal estimation set regardless of the simultaneous presence of UBB process and measurement noise. Third, a recursive convex optimization algorithm is presented to determine optimal ellipsoids as well as the estimator gain parameters and the event triggering weighting matrix parameter. Furthermore, the proposed dynamic ETS is applied to address the distributed set-membership estimation problem for a discrete-time linear time-varying system with a nonlinearity satisfying a sector constraint. Finally, an illustrative example is given to show the effectiveness and advantage of the developed approach.

This paper is concerned with distributed event-triggered H∞consensus filtering for a discrete-time linear system over a sensor network. Different from some existing event-triggered communication schemes (ETCSs), a new distributed ETCS is first developed to reduce the communication frequency of neighboring sensors, where the threshold parameter in an event triggering condition is time-varying with attainable upper and lower bounds. Then a threshold-parameter-dependent approach is proposed to derive criteria for designing the desired H∞consensus filters and the ETCS such that the resultant filtering error system is asymptotically stable with the prescribed H∞performance while maintaining satisfactory resource efficiency. Furthermore, a polytope-like transformation with regard to time-varying threshold parameters is performed and a recursive algorithm is presented to determine the threshold-parameter-dependent filter matrix sequences and event triggering weighting matrix sequence. Two illustrative examples are employed to show the effectiveness of the developed approach.

This paper is concerned with the synchronization control problem for a class of discrete time-delay complex dynamical networks under a dynamic event-triggered mechanism. For the efficiency of energy utilization, we make the first attempt to introduce a dynamic event-triggering strategy into the design of synchronization controllers for complex dynamical networks. A new discrete-time version of the dynamic event-triggering mechanism is proposed in terms of the absolute errors between control input updates. By constructing an appropriate Lyapunov functional, the dynamics of each network node combined with the introduced event-triggering mechanism are first analyzed, and a sufficient condition is then provided under which the synchronization error dynamics is exponentially ultimately bounded. Subsequently, a set of the desired synchronization controllers is designed by solving a matrix inequality. Finally, a simulation example is provided to verify the effectiveness of the proposed dynamic event-triggered synchronization control scheme.

In this paper, the observer-based stabilization problem is investigated for a class of discrete-time nonlinear stochastic networked control systems (NCSs) with exogenous disturbances. The signal transmission from the sensors to the observer is implemented via a shared digital network, in which both uniform quantization effect and stochastic communication protocol (SCP) are taken into account to reflect several network-induced constraints. The notion of input-to-state stability in probability is introduced to describe the dynamical behaviors of the closed-loop stochastic NCS that is effectively characterized by a general nonlinear stochastic difference equation with Markovian jumping parameters. A theoretical framework is first established to felicitate the dynamics analysis of the closed-loop system in virtue of the switched Lyapunov function method and the stochastic analysis techniques. By making full use of the quantized measurement output under the scheduling of the SCP, the existence conditions for an observer-based controller are established under which the closed-loop system is input-to-state stable in probability. Then, the explicit expression of the gain matrices of the desired controller is given by resorting to a set of feasible solutions of certain matrix inequalities. The effectiveness of the theoretical results is demonstrated by a numerical simulation example.

In this paper, the input-to-state stabilization problem is investigated for a class of nonlinear delayed systems with exogenous disturbances. The model under consideration is general that covers for both mixed time-delays and Lipschitz-type nonlinearities. An observer-based controller is designed such that the closed-loop system is stable under an event-triggered mechanism. Two separate event-triggered strategies are proposed in sensor-to-observer (S/O) and controller-to-actuator (C/A) channels, respectively, in order to reduce the updating frequencies of the sensor and the controller with guaranteed performance requirements. The notion of input-to-state practical stability is introduced to characterize the performance of the controlled system that caters for the influence from both disturbances and event-triggered schemes. The estimates of the upper bounds of the delayed states and two measurement errors are employed to analyze and further exclude the Zeno behavior resulting from the proposed event-triggered schemes in S/O and C/A channels. The controller gain matrices and the event-trigger parameters are co-designed in terms of the feasibility of certain matrix inequalities. A numerical simulation example is provided to illustrate the effectiveness of theoretical results.

This paper investigates the state estimation problem for a class of 2-D time-varying systems with error variance constraints, where the implemented estimator gain is subject to stochastic perturbations. Redundant channels are utilized as a protocol to strengthen the transmission reliability and the channels' packet dropout rates are described by mutually uncorrelated Bernoulli distributions. The objective of the addressed problem is to design a resilient estimator such that an upper bound on the estimation error variance is first guaranteed and then minimized at each time step, where the considered gain perturbations are characterized by their statistical properties. By employing the induction method and the variance-constrained approach, an upper bound on the estimation error variance is first constructed by means of the solutions to two Riccati-like difference equations and, subsequently, a locally minimal upper bound is achieved by appropriately designing the gain parameter. Then, an effective algorithm is proposed for designing the desired estimator, which is in a recursive form suitable for online applications. Finally, a numerical simulation is provided to demonstrate the usefulness of the proposed estimation scheme.

In this paper, the consensus control problem is investigated for a class of discrete-time networked multiagent systems (MASs) with the coding-decoding communication protocol (CDCP). Under a directed communication topology, an observer-based control scheme is proposed for each agent by utilizing the relative measurement outputs between the agent itself and its neighboring ones. The signal delivery is in a digital manner, which means that only the sequence of finite coded signals is sent from the observer to the controller. To be specific, the observed data is encoded to certain codewords by a designed coder via the CDCP, and the received codewords are then decoded by the corresponding decoder at the controller side. The purpose of the addressed problem is to design an observer-based controller such that the close-loop MAS achieves the expected consensus performance. First, with the help of the input-to-state stability theory, a theoretical framework for the detectability is established for analyzing and designing the CDCP. Then, under such a communication protocol, some sufficient conditions for the existence of the proposed observer-based controller are derived to guarantee the asymptotic consensus of the MASs. In addition, the controller parameter is explicitly determined in terms of the solution to certain matrix inequalities associated with the information of the communication topology. Finally, a simulation example is given to demonstrate the effectiveness of the developed control strategy.

In this paper, the near-optimal nonfragile consensus control design problem is investigated for a class of discrete time-varying multiagent systems (MASs) with control- and state-dependent noises. A decentralized observer-based control protocol is proposed by using the relative output measurements. The gain perturbations/variations of the controller as well as the state- and control-dependent noises are simultaneously taken into consideration, which could better reflect the complexities in reality. The corresponding time-varying observer-based nonfragile near-optimal consensus protocol is designed for the underlying MASs over a finite horizon. To be specific, a certain upper bound is first derived for the associate cost function for the MASs. Then, such an upper bound is minimized by using the completing-the-square technique and Moore-Penrose pseudo inverse. The parameters of the time-varying observer/controller are obtained in terms of the solutions to the Riccati-like recursion. In virtue of the matrix partitioning technique, the explicit expressions of the control/observer parameters are presented. Finally, based on the derived consensus protocol, an upper bound of the associate cost function is provided as time goes to infinity. Some numerical simulations are conducted to demonstrate the validity of the proposed methodology.

In this paper, the near-optimal resilient control strategy design problem is investigated for a class of discrete time-varying system in simultaneous presence of stochastic communication protocols (SCPs), gain perturbations, state saturations, and additive nonlinearities. In the sensor-to-controller network, only one sensor is permitted to get access to the communication media so as to avoid possible data collisions. Described by a Markov chain, the SCP is employed to determine which sensor should obtain the access to the network at a certain time. Furthermore, two kinds of well-recognized complexities (i.e., state saturations and additive nonlinearities) are considered in the system model and the phenomenon of controller gain perturbation is also taken into special consideration. Accordingly, the resilient control strategy is designed by: 1) deriving a certain upper bound on the associate cost function of underlying systems and 2) minimizing such an upper bound through the utilization of the completing-the-square technique and the Moore-Penrose pseudo inverse. The resilient control strategy is obtained in an iterative manner by solving a set of coupled backward Riccati-like recursions. Furthermore, based on the proposed control strategies, the infinite horizon case is considered and the corresponding upper bound of the cost function is explicitly provided. Finally, numerical simulations are carried out on power systems in order to verify the validity of the proposed resilient control algorithms.

In this paper, a novel particle swarm optimization (PSO) algorithm is proposed in order to improve the accuracy of traditional clustering approaches with applications in analyzing real-time patient attendance data from an accident & emergency (A&E) department in a local U.K. hospital. In the proposed randomly occurring distributedly delayed PSO (RODDPSO) algorithm, the evolutionary state is determined by evaluating the evolutionary factor in each iteration, based on whether the velocity updating model switches from one mode to another. With the purpose of reducing the possibility of getting trapped in the local optima and also expanding the search space, randomly occurring time-delays that reflect the history of previous personal best and global best particles are introduced in the velocity updating model in a distributed manner. Eight well-known benchmark functions are employed to evaluate the proposed RODDPSO algorithm which is shown via extensive comparisons to outperform some currently popular PSO algorithms. To further illustrate the application potential, the RODDPSO algorithm is successfully exploited in the patient clustering problem for data analysis with respect to a local A&E department in West London. Experiment results demonstrate that the RODDPSO-based clustering method is superior over two other well-known clustering algorithms.

Industrial cyber-physical systems (CPSs) are large-scale, geographically dispersed, and life-critical systems, in which lots of sensors and actuators are embedded and networked together to facilitate real-time monitoring and closed-loop control. Their intrinsic features in geographic space and resources put forward to urgent requirements of reliability and scalability for designed filtering or control schemes. This paper presents a review of the state-of-the-art of distributed filtering and control of industrial CPSs described by differential dynamics models. Special attention is paid to sensor networks, manipulators, and power systems. For real-time monitoring, some typical Kalman-based distributed algorithms are summarized and their performances on calculation burden and communication burden, as well as scalability, are discussed in depth. Then, the characteristics of non-Kalman cases are further disclosed in light of constructed filter structures. Furthermore, the latest development is surveyed for distributed cooperative control of mobile manipulators and distributed model predictive control in industrial automation systems. By resorting to droop characteristics, representative distributed control strategies classified by controller structures are systematically summarized for power systems with the requirements of power sharing and voltage and frequency regulation. In addition, distributed security control of industrial CPSs is reviewed when cyber-attacks are taken into consideration. Finally, some challenges are raised to guide the future research.

Train positioning is of critical importance for communication-based train control (CBTC) systems. The objective of this paper is to provide an algorithm to generate the precise estimates of the train position and velocity for CBTC systems with carrier-sense multiple access (CSMA) protocol scheduling, thereby improving the accuracy of train positioning as well as the availability of CBTC systems. First, the dynamics of a train with N cars linked by couplers is described based on Newton's motion equations. Then, the transmission model reflecting the behaviors of p-persistent CSMA protocol is presented by using a Bernoulli distributed sequence whose probability distribution is dependent on the number of trains sharing with one communication channel [i.e., N(k)]. Furthermore, the value of N(k) is assumed to be unknown but bounded by two known positive integers. The purpose of the problem addressed is to design an estimator, such that the estimation error is exponentially ultimately bounded (with a certain asymptotic upper bound) in mean square subject to the external resistive force. By utilizing the stochastic analysis approach, sufficient conditions are established to guarantee the ultimate boundedness of the estimation error in mean square. For the purpose of designing the desired estimator gains under different requirements (e.g., smallest ultimate bound and fastest decay rate), two optimization problems are solved in terms of linear matrix inequalities. Finally, a simulation example is given to illustrate the effectiveness of the estimator design scheme.

Over the past two decades, the synchronization problem for dynamical networks has drawn significant attention due to its clear practical insight in biological systems, social networks, and neuroscience. In the case where a dynamical network cannot achieve the synchronization by itself, the feedback controller should be added to drive the network toward a desired orbit. On the other hand, the time delays may often occur in the nodes or the couplings of a dynamical network, and the existence of time delays may induce some undesirable dynamics or even instability. Moreover, in the course of implementing a feedback controller, the inevitable actuator limitations could downgrade the system performance and, in the worst case, destabilize the closed-loop dynamics. The main purpose of this paper is to consider the synchronization problem for a class of delayed dynamical networks with actuator saturations. Each node of the dynamical network is described by a nonlinear system with a time-varying delay and the intermittent control strategy is proposed. By using a combination of novel sector conditions, piecewise Lyapunov-like functionals and the switched system approach, delay-dependent sufficient conditions are first obtained under which the dynamical network is locally exponentially synchronized. Then, the explicit characterization of the controller gains is established by means of the feasibility of certain matrix inequalities. Furthermore, optimization problems are formulated in order to acquire a larger estimate of the set of initial conditions for the evolution of the error dynamics when designing the intermittent controller. Finally, two examples are given to show the benefits and effectiveness of the developed theoretical results.

This paper deals with the finite-horizon quantized H∞state estimation problem for a class of discrete timevarying genetic regulatory networks with quantization effects under stochastic communication protocols (SCPs). To better reflect the data-driven flavor of today's biological research, the network measurements (typically gigabytes in size by highthroughput sequencing technologies) are transmitted to a remote state estimator via two independent communication networks of limited bandwidths. To lighten the communication loads and avoid undesired data collisions, the measurement outputs are quantized and then transmitted under two SCPs introduced to schedule the large-scale data transmissions. The purpose of this paper is to design a time-varying state estimator such that the error dynamics of the state estimation satisfies a prescribed H∞performance requirement over a finite horizon in the presence of nonlinearities, quantization effects, and SCPs. By utilizing the completing-the-square technique, sufficient conditions are derived to ensure the H∞estimation performance and the parameters of the state estimator are designed by solving coupled backward recursive Riccati difference equations. A numerical example is given to illustrate the effectiveness of the design scheme of the proposed state estimator.

This paper investigates the H∞state estimation problem for a class of discrete-time nonlinear singularly perturbed complex networks (SPCNs) under the Round-Robin (RR) protocol. A discrete-time nonlinear SPCN model is first devised on two time scales with their discrepancies reflected by a singular perturbation parameter (SPP). The network measurement outputs are transmitted via a communication network where the data transmissions are scheduled by the RR protocol with hope to avoid the undesired data collision. The error dynamics of the state estimation is governed by a switched system with a periodic switching parameter. A novel Lyapunov function is constructed that is dependent on both the transmission order and the SPP. By establishing a key lemma specifically tackling the SPP, sufficient conditions are obtained such that, for any SPP less than or equal to a predefined upper bound, the error dynamics of the state estimation is asymptotically stable and satisfies a prescribed H∞performance requirement. Furthermore, the explicit parameterization of the desired state estimator is given by means of the solution to a set of matrix inequalities, and the upper bound of the SPP is then evaluated in the feasibility of these matrix inequalities. Moreover, the corresponding results for linear discrete-time SPCNs are derived as corollaries. A numerical example is given to illustrate the effectiveness of the proposed state estimator design scheme.

Accurately estimating unknown quality-of-service (QoS) data based on historical records of Web-service invocations is vital for automatic service selection. This work presents an effective scheme for addressing this issue via alternating direction method-based matrix factorization. Its main idea consists of a) adopting the principle of the alternating direction method to decompose the task of building a matrix factorization-based QoS-estimator into small subtasks, where each one trains a subset of desired parameters based on the latest status of the whole parameter set; b) building an ensemble of diversified single models with sophisticated diversifying and aggregating mechanism; and c) parallelizing the construction process of the ensemble to drastically reduce the time cost. Experimental results on two industrial QoS datasets demonstrate that with the proposed scheme, more accurate QoS estimates can be achieved than its peers with comparable computing time with the help of its practical parallelization.

This paper is concerned with the input-to-state stabilization problem for a class of delayed differential systems. Both time-delay in state and bounded exogenous disturbances are taken into account in the model. An event-triggered strategy, which depends simultaneously on the latest sampled state and a non-negative threshold, is proposed to reduce the transmission frequency of the feedback control signals with guaranteed performance requirements. The notion of input-to-state practical stability is introduced to evaluate the dynamical performance of the controlled systems with considering the effects from both exogenous disturbances and event-triggered scheme. The estimations of the upper bounds for the system state and the measurement error are employed to analyze and further exclude the Zeno behavior for the proposed event-triggered scheme. The controller gain and the event-trigger parameters are co-designed in terms of the feasibility of certain matrix inequalities. A numerical simulation example is provided to illustrate the effectiveness of theoretical results.

In this paper, the state estimation problem is investigated for a class of continuous-time complex networks with bounded distributed delay. For the network under consideration, only the outputs from a fraction of nodes are available and this put forward the new challenge, that is, the so-called partial-nodes-based state estimation problem. In order to reduce the usage of the communication resources, a general event-triggering rule is considered in the design of the estimator. A novel estimator is constructed and, by constructing novel Lyapunov-Krasovskii functionals, some easy-to-check conditions are derived such that the error dynamics is exponentially ultimately bounded. Furthermore, it is shown that the Zeno behavior can be excluded from the event-triggering rules. Numerical simulations are presented to further illustrate the effectiveness of the theoretical results.

This paper is concerned with the robust H2/H∞model predictive control problem for a class of linear systems with polytopic uncertainties under weighted maximum-error-first and try-once-discard (MEF-TOD) protocol. To prevent data from collision, the weighted MEF-TOD protocol is employed during the data transmission from sensors to controller, where only one sensor is allowed to transmit the measurement at every time instant. A switched linear system is established with respect to the switching signals generated by the adopted weighted MEF-TOD protocol. By taking the influence of the exogenous disturbance and the weighted MEF-TOD protocol into consideration, an online optimization problem with nonconvex conditions is formulated. Then, to deal with the couplings of unknown variables, singular value decomposition technique is utilized to transform the nonconvex conditions into solvable ones. Subsequently, a set of dynamic output-feedback controllers is designed to guarantee the stability of the closed-loop system with guaranteed robust H2/H∞performance. Finally, a direct current motor example is used to illustrate the validity and effectiveness of the proposed methods.

This paper is concerned with the recursive filtering problem for a class of nonlinear 2-D time-varying systems with degraded measurements over a finite horizon. The phenomenon of measurement degradation occurs in a random way depicted by stochastic variables satisfying certain probabilities distributions. The nonlinearities under consideration are dealt with through the Taylor expansion, where the high-order terms of the linearization errors are characterized by norm-bounded parameter uncertainties. The objective of the addressed problem is to design a filter which guarantees an upper bound of the estimation error variance and subsequently minimizes such a bound with the desired gain parameters. By means of mathematical induction, an upper bound is first derived for the estimation error variance by constructing two sets of Riccati-like difference equations, and then the obtained bound is minimized by properly selecting the filter parameter at each time step. Both the minimal upper bound and the desired filter parameter are suitable for recursive online computation. Furthermore, the effect of the stochastic measurement degradation on the filtering performance is discussed. Finally, a simulation example is presented to demonstrate the effectiveness of the designed filter.

This paper is concerned with the distributed filtering problem for a class of discrete time-varying stochastic parameter systems with error variance constraints over a sensor network where the sensor outputs are subject to successive missing measurements. The phenomenon of the successive missing measurements for each sensor is modeled via a sequence of mutually independent random variables obeying the Bernoulli binary distribution law. To reduce the frequency of unnecessary data transmission and alleviate the communication burden, an event-triggered mechanism is introduced for the sensor node such that only some vitally important data is transmitted to its neighboring sensors when specific events occur. The objective of the problem addressed is to design a time-varying filter such that both the requirements and the variance constraints are guaranteed over a given finite-horizon against the random parameter matrices, successive missing measurements, and stochastic noises. By recurring to stochastic analysis techniques, sufficient conditions are established to ensure the existence of the time-varying filters whose gain matrices are then explicitly characterized in term of the solutions to a series of recursive matrix inequalities. A numerical simulation example is provided to illustrate the effectiveness of the developed event-triggered distributed filter design strategy.

The synchronization control problem is investigated for a class of discrete-time dynamical networks with packet dropouts via a coding-decoding-based approach. The data is transmitted through digital communication channels and only the sequence of finite coded signals is sent to the controller. A series of mutually independent Bernoulli distributed random variables is utilized to model the packet dropout phenomenon occurring in the transmissions of coded signals. The purpose of the addressed synchronization control problem is to design a suitable coding-decoding procedure for each node, based on which an efficient decoder-based control protocol is developed to guarantee that the closed-loop network achieves the desired synchronization performance. By applying a modified uniform quantization approach and the Kronecker product technique, criteria for ensuring the detectability of the dynamical network are established by means of the size of the coding alphabet, the coding period and the probability information of packet dropouts. Subsequently, by resorting to the input-to-state stability theory, the desired controller parameter is obtained in terms of the solutions to a certain set of inequality constraints which can be solved effectively via available software packages. Finally, two simulation examples are provided to demonstrate the effectiveness of the obtained results.

This paper is concerned with the finite-horizon consensus problem for a class of discrete time-varying multiagent systems with external disturbances and missing measurements. To improve the communication reliability, redundant channels are introduced and the corresponding protocol is constructed for the information transmission over redundant channels. An event-triggered scheme is adopted to determine whether the information of agents should be transmitted to their neighbors. Subsequently, an observer-type event-triggered control protocol is proposed based on the latest received neighbors' information. The purpose of the addressed problem is to design a time-varying controller based on the observed information to achieve the consensus performance in a finite horizon. By utilizing a constrained recursive Riccati difference equation approach, some sufficient conditions are obtained to guarantee the consensus performance, and the controller parameters are also designed. Finally, a numerical example is provided to demonstrate the desired reliability of redundant channels and the effectiveness of the event-triggered control protocol.

This paper is concerned with the fault detection problem for a class of networked fuzzy systems with multiplicative noises on both system states and measurement outputs. In view of the limited communication capacity, a periodical communication protocol (i.e., the round-robin protocol) is adopted to undertake the transmission task between the sensors and the fault detection filter, which leads to periodical delays in the overall system. A Takagi-Sugeno fuzzy-model-based fault detection filter is constructed to produce the residual signal and an auxiliary error system is established to facilitate the stability analysis of the error dynamics. With the aid of Lyapunov stability theory, sufficient conditions are obtained that ensure the exponentially mean-square stability of the error dynamics with prescribedH∞performance constraint. The desired fault detection filter is designed by solving a convex optimization problem via the semidefinite programme method. A finite-time evaluation function and an adjustable threshold are introduced in order to detect the possible faults effectively. The effectiveness of the proposed fault detection scheme is validated by a numerical simulation example.

This paper deals with the nonfragile H∞ control problem for a class of discrete-time Takagi-Sugeno fuzzy systems with both randomly occurring gain variations (ROGVs) and channel fadings. The system measurement is subject to fading channels described by Rice fading model where the channel coefficients are random variables taking values within given intervals. The gain matrices of the output feedback controllers are subject to random fluctuations referred to as the ROGVs. The purpose of the addressed problem is to design a parameter-dependent nonfragile output-feedback controller such that, in the presence of both ROGVs and channel fadings, the closed-loop system is exponentially mean-square stable while achieving the guaranteed H∞ disturbance attenuation level. A gain-scheduling approach is developed to tackle the addressed problem where the designed controller gains are dependent on certain parameters of practical significance (e.g., packet dropout rate). Through stochastic analysis and Lyapunov functional approach, sufficient conditions are derived for the existence of the desired output feedback controller ensuring both the exponential mean-square stability and the prescribed H∞performance. The explicit expression of the feedback controller is also characterized by using a semidefinite programming method. Finally, an illustrative example is given to show the usefulness and effectiveness of the proposed design method.

This paper investigates the variance-constrained state estimation problem for a class of nonlinear time-varying complex networks with randomly varying topologies, stochastic inner coupling, and measurement quantization. A Kronecker delta function and Markovian jumping parameters are utilized to describe the random changes of network topologies. A Gaussian random variable is introduced to model the stochastic disturbances in the inner coupling of complex networks. As a kind of incomplete measurements, measurement quantization is taken into consideration so as to account for the signal distortion phenomenon in the transmission process. Stochastic nonlinearities with known statistical characteristics are utilized to describe the stochastic evolution of the complex networks. We aim to design a finite-horizon estimator, such that in the simultaneous presence of quantized measurements and stochastic inner coupling, the prescribed variance constraints on the estimation error and the desired performance requirements are guaranteed over a finite horizon. Sufficient conditions are established by means of a series of recursive linear matrix inequalities, and subsequently, the estimator gain parameters are derived. A simulation example is presented to illustrate the effectiveness and applicability of the proposed estimator design algorithm.

This paper is concerned with the synchronization analysis and control problems for a class of nonlinear discrete-time stochastic complex dynamical networks (CDNs) consisting of identical nodes. The discrete-time stochastic dynamical networks under consideration are quite general that account for asymmetric coupling configuration, nonlinear inner coupling structures as well as nonidentical exogenous disturbances. By resorting to both the error bound and the synchronization probability, a notion of quasi-synchronization in probability is first introduced to assess the synchronization performance of the addressed CDNs. An event-triggered pinning feedback control strategy is adopted to control a small fraction of the network nodes with hope to reduce the frequency of updating and communication in the control process while preserving the desired dynamical behaviors of the controlled networks. By using the Lyapunov function method and the stochastic analysis techniques, a general framework is established within which the problems of dynamics analysis and controller synthesis are solved for the closed-loop stochastic dynamical networks. Two numerical examples and their simulations are presented to illustrate the effectiveness and the usefulness of our theoretical results.

In this paper, the event-triggered state estimation problem is investigated for a class of discrete-time stochastic memristive neural networks (DSMNNs) with time-varying delays and missing measurements. The DSMNN is subject to both the additive deterministic disturbances and the multiplicative stochastic noises. The missing measurements are governed by a sequence of random variables obeying the Bernoulli distribution. For the purpose of energy saving, an event-triggered communication scheme is used for DSMNNs to determine whether the measurement output is transmitted to the estimator or not. The problem addressed is to design an event-triggered estimator such that the dynamics of the estimation error is exponentially mean-square stable and the prespecified disturbance rejection attenuation level is also guaranteed. By utilizing a Lyapunov-Krasovskii functional and stochastic analysis techniques, sufficient conditions are derived to guarantee the existence of the desired estimator, and then, the estimator gains are characterized in terms of the solution to certain matrix inequalities. Finally, a numerical example is used to demonstrate the usefulness of the proposed event-triggered state estimation scheme.

In this brief, the new problem of partial-nodes-based (PNB) state estimation problem is investigated for a class of complex network with unbounded distributed delays and energy-bounded measurement noises. The main novelty lies in that the states of the complex network are estimated through measurement outputs of a fraction of the network nodes. Such fraction of the nodes is determined by either the practical availability or the computational necessity. The PNB state estimator is designed such that the error dynamics of the network state estimation is exponentially ultimately bounded in the presence of measurement errors. Sufficient conditions are established to ensure the existence of the PNB state estimators and then the explicit expression of the gain matrices of such estimators is characterized. When the network measurements are free of noises, the main results specialize to the case of exponential stability for error dynamics. Numerical examples are presented to verify the theoretical results.

This paper deals with the event-based finite-time state estimation problem for a class of discrete-time stochastic neural networks with mixed discrete and distributed time delays. In order to mitigate the burden of data communication, a general component-based event-triggered transmission mechanism is proposed to determine whether the measurement output should be released to the estimator at certain time-point according to a specific triggering condition. A new concept of finite-time boundedness in the mean square is put forward to quantify the estimation performance by introducing a settling-like time function. The objective of the addressed problem is to construct an event-based state estimator to estimate the neuron states such that, in the presence of both mixed time delays and external noise disturbances, the dynamics of the estimation error is finite-time bounded in the mean square with a prescribed error upper bound. Sufficient conditions are established, via stochastic analysis techniques, to guarantee the desired estimation performance. By solving an optimization problem with some inequality constraints, the explicit expression of the estimator gain matrix is characterized to minimize the settling-like time. Finally, a numerical simulation example is exploited to demonstrate the effectiveness of the proposed estimator design scheme.

This paper is concerned with the security control problem with quadratic cost criterion for a class of discrete-time stochastic nonlinear systems subject to deception attacks. A definition of security in probability is adopted to account for the transient dynamics of controlled systems. The purpose of the problem under consideration is to design a dynamic output feedback controller such that the prescribed security in probability is guaranteed while obtaining an upper bound of the quadratic cost criterion. First of all, some sufficient conditions with the form of matrix inequalities are established in the framework of the input-to-state stability in probability. Then, an easy-solution version on above inequalities is proposed by carrying out the well-known matrix inverse lemma to obtain both the controller gain and the upper bound. Furthermore, the main results are shown to be extendable to the case of discrete-time stochastic linear systems. Finally, two simulation examples are utilized to illustrate the usefulness of the proposed controller design scheme.

This paper is concerned with the distributed filtering problem over wireless sensor networks for a class of state-saturated systems subject to fading measurements and quantization effects. Each sensor node in the network communicates with its neighbors according to the network topology described by a directed graph. The fading phenomena of measurements are assumed to occur in a random way and the attenuation coefficients of the fading measurements are described by a set of random variables with known stochastic properties. By solving two sets of matrix difference equations, an upper bound for the filtering error covariance is presented. Subsequently, with the topology information of the sensor network, such an upper bound is minimized by properly designing the filter parameters. Moreover, the performance of the proposed filter is investigated through establishing sufficient conditions ensuring that the trace of the upper bound is bounded. The relationship between the filter performance and the mean of attenuation coefficient is also discussed. A numerical simulation is exploited to demonstrate the effectiveness of the proposed filtering method.

In this paper, the event-triggered strategy design problem is addressed for a class of discrete-time nonlinear quadratic noncooperative games subject to matched disturbances. The event-triggered scheme is proposed based on the relative error of the input signals with aim to determine whether such signals should be transmitted to the actuator or not. The disturbance-observer-based game strategy is put forward to compensate the matched disturbance and also optimize the individual cost function for each player. The main purpose of the addressed problem is to design the time-varying strategy parameters such that the upper bound of the individual cost function of each player is minimized unilaterally over a finite horizon [0,N]. Sufficient conditions are first established for the existence and uniqueness of the game strategies through backward Riccati-like recursions and then the desired strategy parameters are computed iteratively by utilizing the Moore-Penrose pseudo inverse. Finally, a simulation example is provided to verify the effectiveness of the proposed design method.

In this paper, the observer-based event-triggering consensus control problem is investigated for a class of discrete-time multiagent systems with lossy sensors and cyber-attacks. A novel distributed observer is proposed to estimate the relative full states and the estimated states are then used in the feedback protocol in order to achieve the overall consensus. An event-triggered mechanism with state-independent threshold is adopted to update the control input signals so as to reduce unnecessary data communications. The success ratio of the launched attacks is taken into account to reflect the probabilistic failures of the attacks passing through the protection devices subject to limited resources and network fluctuations. The purpose of the address problem is to design an observer-based distributed controller such that the closed-loop multiagent system achieves the prescribed consensus in spite of the lossy sensors and cyber-attacks. By making use of eigenvalues and eigenvectors of the Laplacian matrix, the closed-loop system is transformed into an easy-to-analyze setting and then a sufficient condition is derived to guarantee the desired consensus. Furthermore, the controller gain is obtained in terms of the solution to certain matrix inequality which is independent of the number of agents. An algorithm is provided to optimize the consensus bound. Finally, a simulation example is utilized to illustrate the usefulness of the proposed controller design scheme.

In this paper, we consider the H∞control problem for a class of 2-D Takagi-Sugeno fuzzy described by the second Fornasini-Machesini local state-space model with time delays and missing measurements. The state delays are allowed to be time-varying within a known interval. The measurement output is subject to randomly intermittent packet dropouts governed by a random sequence satisfying the Bernoulli distribution. The purpose of the addressed problem is to design an output feedback controller such that the closed-loop system is globally asymptotically stable in the mean square and the prescribed H∞performance index is satisfied. By employing a combination of the intensive stochastic analysis and the free weighting matrix method, several delay-range-dependent sufficient conditions are presented that guarantee the existence of the desired controllers for all possible time-delays and missing measurements. The explicit expressions of such controllers are derived by means of the solution to a class of convex optimization problems that can be solved via standard software packages. Finally, a numerical simulation example is given to demonstrate the applicability of the proposed control scheme.

In this paper, the distributed set-membership filtering problem is investigated for a class of discrete time-varying system with an event-based communication mechanism over sensor networks. The system under consideration is subject to sector-bounded nonlinearity, unknown but bounded noises and sensor saturations. Each intelligent sensing node transmits the data to its neighbors only when certain triggering condition is violated. By means of a set of recursive matrix inequalities, sufficient conditions are derived for the existence of the desired distributed event-based filter which is capable of confining the system state in certain ellipsoidal regions centered at the estimates. Within the established theoretical framework, two additional optimization problems are formulated: one is to seek the minimal ellipsoids (in the sense of matrix trace) for the best filtering performance, and the other is to maximize the triggering threshold so as to reduce the triggering frequency with satisfactory filtering performance. A numerically attractive chaos algorithm is employed to solve the optimization problems. Finally, an illustrative example is presented to demonstrate the effectiveness and applicability of the proposed algorithm.

This paper is concerned with the state estimation for neural networks with two additive time-varying delay components. Three cases of these two time-varying delays are fully considered: 1) both delays are differentiable uniformly bounded with delay-derivative bounded by some constants; 2) one delay is continuous uniformly bounded while the other is differentiable uniformly bounded with delay-derivative bounded by certain constants; and 3) both delays are continuous uniformly bounded. First, an extended reciprocally convex inequality is introduced to bound reciprocally convex combinations appearing in the derivative of some Lyapunov-Krasovskii functional. Second, sufficient conditions are derived based on the extended inequality for three cases of time-varying delays, respectively. Third, a linear-matrix-inequality-based approach with two tuning parameters is proposed to design desired Luenberger estimators such that the error system is globally asymptotically stable. This approach is then applied to state estimation on neural networks with a single interval time-varying delay. Finally, two numerical examples are given to illustrate the effectiveness of the proposed method.

This paper is concerned with the distributed ℋ∞ consensus control problem for a discrete time-varying multiagent system with the stochastic communication protocol (SCP). A directed graph is used to characterize the communication topology of the multiagent network. The data transmission between each agent and the neighboring ones is implemented via a constrained communication channel where only one neighboring agent is allowed to transmit data at each time instant. The SCP is applied to schedule the signal transmission of the multiagent system. A sequence of random variables is utilized to capture the scheduling behavior of the SCP. By using the mapping technology combined with the Hadamard product, the closed-loop multiagent system is modeled as a time-varying system with a stochastic parameter matrix. The purpose of the addressed problem is to design a cooperative controller for each agent such that, for all probabilistic scheduling behaviors, the ℋ∞ consensus performance is achieved over a given finite horizon for the closed-loop multiagent system. A necessary and sufficient condition is derived to ensure the ℋ∞ consensus performance based on the completing squares approach and the stochastic analysis technique. Then, the controller parameters are obtained by solving two coupled backward recursive Riccati difference equations. Finally, a numerical example is given to illustrate the effectiveness of the proposed controller design scheme.

This paper is concerned with the robust H∞filtering problem for a class of 2-D uncertain fuzzy systems with randomly occurring mixed delays (ROMDs). The underlying 2-D systems are described by the Fornasini-Marchesini model, and the uncertainty is expressed in a linear fraction form. An improved Takagi-Sugeno (T-S) fuzzy model corresponding to the spatial promise variables is adopted to represent the complicated 2-D nonlinear system. The mixed delays consisting of both discrete and distributed delays are allowed to appear in a random manner governed by two sets of Bernoulli distributed white sequences with known probability. A full-order fuzzy filter is constructed to estimate the output signal such that, in the presence of parameter uncertainties and ROMDs, the dynamics of the estimation errors is asymptotically stable with a prescribed H∞disturbance attenuation level. Based on the stochastic analysis technique and the Lyapunov-like functional, sufficient conditions are established to ensure the existence of the desired filters, and the explicit expressions of such filters are derived by means of the solution to a class of convex optimization problems that can be solved via standard software packages. A numerical example is provided to demonstrate the effectiveness of the developed filter design algorithms, and the filter performances with and without fuzzy rules are also compared.

Undirected, high-dimensional, and sparse (HiDS) networks are frequently encountered in industrial applications. They contain rich knowledge regarding various useful patterns. Nonnegative latent factor (NLF) models are effective and efficient in extracting useful knowledge from directed networks. However, they cannot describe the symmetry of an undirected network. For addressing this issue, this paper analyzes the extraction process of NLFs on asymmetric and symmetric matrices, respectively, thereby innovatively achieving the symmetric and nonnegative latent factor (SNLF) models for undirected, HiDS networks. The proposed SNLF models are equipped with: 1) high efficiency; 2) nonnegativity; and 3) symmetry. Experimental results on real networks show that the SNLF models are able to: 1) describe the symmetry of the target network rigorously; 2) ensure the nonnegativity of resultant latent factors; and 3) achieve high computational efficiency when addressing data analysis tasks like missing data estimation.

In this paper, the event-triggered state estimation problem is investigated for a class of discrete-time multidelayed neural networks with stochastic parameters and incomplete measurements. In order to cater for more realistic transmission process of the neural signals, we make the first attempt to introduce a set of stochastic variables to characterize the random fluctuations of system parameters. In the addressed neural network model, the delays among the interconnections are allowed to be different, which are more general than those in the existing literature. The incomplete information under consideration includes randomly occurring sensor saturations and quantizations. For the purpose of energy saving, an event-triggered state estimator is constructed and a sufficient condition is given under which the estimation error dynamics is exponentially ultimately bounded in the mean square. It is worth noting that the ultimate boundedness of the error dynamics is explicitly estimated. The characterization of the desired estimator gain is designed in terms of the solution to a certain matrix inequality. Finally, a numerical simulation example is presented to illustrate the effectiveness of the proposed event-triggered state estimation scheme.

In this paper, the event-based finite-horizon H∞state estimation problem is investigated for a class of discrete time-varying stochastic dynamical networks with stateand disturbance-dependent noises [also called (x, v)-dependent noises]. An event-triggered scheme is proposed to decrease the frequency of the data transmission between the sensors and the estimator, where the signal is transmitted only when certain conditions are satisfied. The purpose of the problem addressed is to design a time-varying state estimator in order to estimate the network states through available output measurements. By employing the completing-the-square technique and the stochastic analysis approach, sufficient conditions are established to ensure that the error dynamics of the state estimation satisfies a prescribed H∞performance constraint over a finite horizon. The desired estimator parameters can be designed via solving coupled backward recursive Riccati difference equations. Finally, a numerical example is exploited to demonstrate the effectiveness of the developed state estimation scheme.

This paper is concerned with the state estimation problem for a class of nonlinear dynamical networks with time-varying delays subject to the round-robin protocol. The communication between the state estimator and the nodes of the dynamical networks is implemented through a shared constrained network, in which only one node is allowed to send data at each time instant. The round-robin protocol is utilized to orchestrate the transmission order of nodes. By using a switch-based approach, the dynamics of the estimation error is modeled by a periodic parameter-switching system with time-varying delays. The purpose of the problem addressed is to design an estimator, such that the estimation error is exponentially ultimately bounded with a certain asymptotic upper bound in mean square subject to the process noise and exogenous disturbance. Furthermore, such a bound is subsequently minimized by the designed estimator parameters. A novel Lyapunov-like functional is employed to deal with the dynamics analysis issue of the estimation error. Sufficient conditions are established to guarantee the ultimate boundedness of the estimation error in mean square by applying the stochastic analysis approach. Then, the desired estimator gains are characterized by solving a convex problem. Finally, a numerical example is given to illustrate the effectiveness of the estimator design scheme.

This paper is concerned with the robust H∞fault detection problem for a class of uncertain discrete-time nonlinear 2-D systems subject to Round-Robin scheduling protocol. The Takagi-Sugeno fuzzy model is used to approximate the nonlinearities, where the linear fractional uncertainties enter the system in a random way. A kind of widely used communication mechanism, namely, Round-Robin communication protocol, is adopted to periodically schedule the sensors and the fault detectors to realize the information exchange in order to reduce the bandwidth usage in a networked environment with limit resource. An improved 2-D fuzzy residual generator is constructed to detect the possible fault, where the stability analysis of the resulting augmented 2-D system is discussed. It is accomplished by using a combination of the basis-dependent Lyapunov-like function and the stochastic analysis techniques. Sufficient conditions are first established to guarantee the globally asymptotic stability of the error dynamics of the state estimation with prescribed H∞performance constraints. Then, a residual generator is proposed to detect the possible faults. The effectiveness of the developed algorithm is demonstrated via application to the fault detection problem for a thermal process.

This paper deals with the consensus control problem for a class of nonlinear discrete time-varying stochastic multiagent systems (MASs) over a finite horizon via static output feedback. The measurement output available for the controller is not only from the individual agent itself but also from its neighboring ones according to the given topology. The nonlinearities described by statistical means can encompass several classes of well-studied nonlinearities in the literature. A new index of mean-square consensus performance, which quantifies the deviation level from the state of individual agent to the average value of all agents' states, is proposed to reflect the transient consensus behavior of the MAS. The purpose of the addressed problem is to design a time-varying output feedback controller such that: 1) the H∞consensus performance defined over a given finite horizon is guaranteed with respect to the additive noises and 2) at each time step, the mean-square consensus performance satisfies the prespecified upper bound constraint. By using a set of recursive matrix inequalities, sufficient conditions are derived for the existence of the desired control scheme for achieving both H∞and mean-square consensus performance requirements. Finally, a simulation example is utilized to illustrate the usefulness of the proposed control protocol.

In this paper, the dynamic output-feedback control problem is studied for a class of discrete-time nonlinear stochastic systems with successive packet dropouts and uniform quantization effects. The stochastic system under investigation involves state-, control-, and disturbance-dependent noises (also called (x, u, v)-dependent noises) that bring in substantial difficulties in the stability analysis. The phenomenon of successive packet dropouts is governed by a binary switching random sequence. The measurement output is subject to the uniform quantization which results in the norm-bounded disturbances, and the concept of input-to-state stability in probability is introduced to deal with this kind of disturbances. In virtue of intensive stochastic analysis, several sufficient conditions are established to guarantee that the closed-loop system is input-to-state stable in probability under the effects of probabilistic packet dropouts as well as uniform quantizations. As an easy consequence, the design problem with linear output-feedback controllers is discussed for the benefits of practical applications and some simplified conditions are derived. Finally, a numerical example is presented to illustrate the effectiveness of the proposed method.

In this paper, the state estimation problem is investigated for a class of discrete-time complex networks subject to nonlinearities, mixed delays, and stochastic noises. A set of event-based state estimators is constructed so as to reduce unnecessary data transmissions in the communication channel. Compared with the traditional state estimator whose measurement signal is received under a periodic clock-driven rule, the event-based estimator only updates the measurement information from the sensors when the prespecified "event" is violated. Attention is focused on the analysis and design problem of the event-based estimators for the addressed discrete-time complex networks such that the estimation error is exponentially bounded in mean square. A combination of the stochastic analysis approach and Lyapunov theory is employed to obtain sufficient conditions for ensuring the existence of the desired estimators and the upper bound of the estimation error is also derived. By using the convex optimization technique, the gain parameters of the desired estimators are provided in an explicit form. Finally, a simulation example is used to demonstrate the effectiveness of the proposed estimation strategy.

This paper is concerned with the nonfragile H∞filtering problem for a class of discrete-time Takagi-Sugeno (T-S) fuzzy systems with both randomly occurring gain variations (ROGVs) and channel fadings. the phenomenon of the ROGVs is introduced into the system model so as to account for the parameter fluctuations occurring during the filter implementation. Two sequences of random variables obeying the Bernoulli distribution are employed to describe the phenomenon of the ROGVs bounded by prescribed norms. In addition, the Rice fading model is utilized to describe the phenomena of channel fadings, where the occurrence probabilities of the random channel coefficients are allowed to time varying. Through stochastic analysis and Lyapunov functional approach, sufficient conditions are established under which the filtering error dynamics is exponentially mean-square stable with a prespecified H∞performance. The set of the desired nonfragile H∞filters is characterized by solving a convex optimization problem via the semidefinite programming method. An illustrative example is given to show the usefulness and effectiveness of the proposed design method in this paper.

In this paper, a recursive filter algorithm is developed to deal with the state estimation problem for power systems with quantized nonlinear measurements. The measurements from both the remote terminal units and the phasor measurement unit are subject to quantizations described by a logarithmic quantizer. Attention is focused on the design of a recursive filter such that, in the simultaneous presence of nonlinear measurements and quantization effects, an upper bound for the estimation error covariance is guaranteed and subsequently minimized. Instead of using the traditional approximation methods in nonlinear estimation that simply ignore the linearization errors, we treat both the linearization and quantization errors as norm-bounded uncertainties in the algorithm development so as to improve the performance of the estimator. For the power system with such kind of introduced uncertainties, a filter is designed in the framework of robust recursive estimation, and the developed filter algorithm is tested on the IEEE benchmark power system to demonstrate its effectiveness.

This paper is concerned with the distributed H∞filtering problem for a class of discrete-time Takagi-Sugeno fuzzy systems with time-varying delays. The data communications among sensor nodes are equipped with redundant channels subject to random packet dropouts that are modeled by mutually independent Bernoulli stochastic processes. The practical phenomenon of the uncertain packet dropout rate is considered, and the norm-bounded uncertainty of the packet dropout rate is asymmetric to the nominal rate. Sufficient conditions on the existence of the desired distributed filters are established by employing the scaled small gain theorem to ensure that the closed-loop system is stochastically stable and achieves a prescribed average H∞performance index. Finally, an illustrative example is provided to verify the theoretical findings.

This paper aims to establish a unified framework to handle both the exponential synchronization and state estimation problems for a class of nonlinear singularly perturbed complex networks (SPCNs). Each node in the SPCN comprises both "slow" and "fast" dynamics that reflects the singular perturbation behavior. General sector-like nonlinear function is employed to describe the nonlinearities existing in the network. All nodes in the SPCN have the same structures and properties. By utilizing a novel Lyapunov functional and the Kronecker product, it is shown that the addressed SPCN is synchronized if certain matrix inequalities are feasible. The state estimation problem is then studied for the same complex network, where the purpose is to design a state estimator to estimate the network states through available output measurements such that dynamics (both slow and fast) of the estimation error is guaranteed to be globally asymptotically stable. Again, a matrix inequality approach is developed for the state estimation problem. Two numerical examples are presented to verify the effectiveness and merits of the proposed synchronization scheme and state estimation formulation. It is worth mentioning that our main results are still valid even if the slow subsystems within the network are unstable.

In this paper, the event-triggered state estimation problem is investigated for a class of complex networks with mixed time delays using sampled data information. A novel state estimator is presented to estimate the network states. A new event-triggered transmission scheme is proposed to reduce unnecessary network traffic between the sensors and the estimator, where the sampled data is transmitted to the estimator only when the so-called "event-triggered condition" is satisfied. The purpose of the problem addressed is to design an estimator for the complex network such that the estimation error is ultimately bounded in mean square. By utilizing Lyapunov theory combined with the stochastic analysis approach, sufficient conditions are established to guarantee the ultimate boundedness of the estimation error in mean square. Then, the desired estimator gain matrices are obtained via solving a convex problem. Finally, a numerical example is given to illustrate the effectiveness of the results.

In this paper, we investigate the distributed filtering problem over wireless sensor networks (WSNs) with bandwidth and energy constraints. To utilize the limited resources efficiently, a novel event-based mechanism is proposed for the sensor node, such that only selected valuable data are broadcasted to its neighboring sensors via the wireless channel according to whether specific events happen. By resorting to graph theory and utilizing stochastic analysis methods, the filter parameters and the event triggering rules are designed, such that the filtering error converges at an exponential rate in the mean square sense. An adaptive algorithm for determining the triggering threshold is developed, which allows the intelligent sensors to tune the boundary of a local event domain in an online manner, so as to keep the average transmission rate level off a desired value. An illustrative example is given to demonstrate the effectiveness of the proposed strategy.

A bidirectional optimizing approach for the melting spinning process based on an immune-enhanced neural network is proposed. The proposed bidirectional model can not only reveal the internal nonlinear relationship between the process configuration and the quality indices of the fibers as final product, but also provide a tool for engineers to develop new fiber products with expected quality specifications. A neural network is taken as the basis for the bidirectional model, and an immune component is introduced to enlarge the searching scope of the solution field so that the neural network has a larger possibility to find the appropriate and reasonable solution, and the error of prediction can therefore be eliminated. The proposed intelligent model can also help to determine what kind of process configuration should be made in order to produce satisfactory fiber products. To make the proposed model practical to the manufacturing, a software platform is developed. Simulation results show that the proposed model can eliminate the approximation error raised by the neural network-based optimizing model, which is due to the extension of focusing scope by the artificial immune mechanism. Meanwhile, the proposed model with the corresponding software can conduct optimization in two directions, namely, the process optimization and category development, and the corresponding results outperform those with an ordinary neural network-based intelligent model. It is also proved that the proposed model has the potential to act as a valuable tool from which the engineers and decision makers of the spinning process could benefit.

Control gains play an important role in the control of a natural or a technical system since they reflect how much resource is required to optimize a certain control objective. This paper is concerned with the controllability of neuronal networks with constraints on the average value of the control gains injected in driver nodes, which are in accordance with engineering and biological backgrounds. In order to deal with the constraints on control gains, the controllability problem is transformed into a constrained optimization problem (COP). The introduction of the constraints on the control gains unavoidably leads to substantial difficulty in finding feasible as well as refining solutions. As such, a modified dynamic hybrid framework (MDyHF) is developed to solve this COP, based on an adaptive differential evolution and the concept of Pareto dominance. By comparing with statistical methods and several recently reported constrained optimization evolutionary algorithms (COEAs), we show that our proposed MDyHF is competitive and promising in studying the controllability of neuronal networks. Based on the MDyHF, we proceed to show the controlling regions under different levels of constraints. It is revealed that we should allocate the control gains economically when strong constraints are considered. In addition, it is found that as the constraints become more restrictive, the driver nodes are more likely to be selected from the nodes with a large degree. The results and methods presented in this paper will provide useful insights into developing new techniques to control a realistic complex network efficiently.

In this paper, the H∞ fuzzy filtering problem is investigated for a class of discrete-time Takagi-Sugeno (T-S) fuzzy systems with randomly occurring uncertainties and randomly occurring interval time-varying delays, as well as channel fadings. A sequence of random variables obeying the Bernoulli distribution is utilized to govern the randomly occurring uncertainties and probabilistic interval time-varying delays. Simultaneously, the Rice fading model is employed to describe the phenomena of channel fadings by setting different values of the channel coefficients. Our attention is focused on the design of an H∞ fuzzy filter such that the filtering error dynamics is exponentially mean-square stable and the disturbance rejection attenuation is constrained to a given level by means of the H∞-performance index. In the presence of the randomly occurring phenomena, sufficient conditions are derived, via stochastic analysis and Lyapunov functional approach, for the existence of desired filter ensuring both the exponential mean-square stability and the prescribed H∞ performance. The filter parameters can be obtained by solving a convex optimization problem via the semidefinite program method. Finally, a numerical example is utilized to illustrate the usefulness and effectiveness of the proposed design technique.

In this paper, the H∞output-feedback control problem is investigated for a class of discrete-time fuzzy systems with randomly occurring infinite distributed delays and channel fadings. A random variable obeying the Bernoulli distribution is introduced to account for the probabilistic infinite distributed delays. The stochastic Rice fading model is employed to simultaneously describe the phenomena of random time delays and channel fadings via setting different values of the channel coefficients. The aim of this paper is to design an H∞output-feedback fuzzy controller such that the closed-loop Takagi-Sugeno (T-S) fuzzy control system is exponentially mean-square stable, and the disturbance rejection attenuation is constrained to a given level by means of the H∞performance index. Intensive analysis is carried out to obtain sufficient conditions for the existence of desired output-feedback controllers, ensuring both the exponential mean-square stability and the prescribed H∞performance. The cone-complementarity linearization algorithm is utilized to cast the controller design problem into a sequential minimization: one that is solvable by the semi-definite programming method. A simulation result is exploited to illustrate the usefulness and effectiveness of the proposed design technique.

Gold immunochromatographic strip assay provides a rapid, simple, single-copy and on-site way to detect the presence or absence of the target analyte. This paper aims to develop a method for accurately segmenting the test line and control line of the gold immunochromatographic strip (GICS) image for quantitatively determining the trace concentrations in the specimen, which can lead to more functional information than the traditional qualitative or semi-quantitative strip assay. The canny operator as well as the mathematical morphology method is used to detect and extract the GICS reading-window. Then, the test line and control line of the GICS reading-window are segmented by the cellular neural network (CNN) algorithm, where the template parameters of the CNN are designed by the switching particle swarm optimization (SPSO) algorithm for improving the performance of the CNN. It is shown that the SPSO-based CNN offers a robust method for accurately segmenting the test and control lines, and therefore serves as a novel image methodology for the interpretation of GICS. Furthermore, quantitative comparison is carried out among four algorithms in terms of the peak signal-to-noise ratio. It is concluded that the proposed CNN algorithm gives higher accuracy and the CNN is capable of parallelism and analog very-large-scale integration implementation within a remarkably efficient time.

In this paper, the synchronization problem is studied for an array of N identical delayed neutral-type neural networks with Markovian jumping parameters. The coupled networks involve both the mode-dependent discrete-time delays and the mode-dependent unbounded distributed time delays. All the network parameters including the coupling matrix are also dependent on the Markovian jumping mode. By introducing novel Lyapunov-Krasovskii functionals and using some analytical techniques, sufficient conditions are derived to guarantee that the coupled networks are asymptotically synchronized in mean square. The derived sufficient conditions are closely related with the discrete-time delays, the distributed time delays, the mode transition probability, and the coupling structure of the networks. The obtained criteria are given in terms of matrix inequalities that can be efficiently solved by employing the semidefinite program method. Numerical simulations are presented to further demonstrate the effectiveness of the proposed approach.

In this paper, the problems of fault detection, isolation, and estimation are considered for a class of discrete time-varying networked sensing systems with incomplete measurements. A unified measurement model is utilized to simultaneously characterize both the phenomena of multiple communication delays and data missing. A least-squares filter that minimizes the estimation variance is first designed for the addressed time-varying networked sensing systems, and then a novel residual matching (RM) approach is developed to isolate and estimate the fault once it is detected. The RM strategy is implemented via a series of Kalman filters, where each filter is designed to estimate the augmented signal composed of the system state and a specific fault signal. The design scheme for each filter is proposed in a recursive way. The main idea for the fault detection and estimation is that the Kalman filter with least residual value is regarded as corresponding to the right fault signal, and its estimation is utilized to represent the actual occurred fault. The effectiveness of our proposed method is demonstrated via simulation experiments on a real Internet-based three-tank system.

In this paper, the H∞ state estimation problem is investigated for a class of complex networks with uncertain coupling strength and incomplete measurements. With the aid of the interval matrix approach, we make the first attempt to characterize the uncertainties entering into the inner coupling matrix. The incomplete measurements under consideration include sensor saturations, quantization, and missing measurements, all of which are assumed to occur randomly. By introducing a stochastic Kronecker delta function, these incomplete measurements are described in a unified way and a novel measurement model is proposed to account for these phenomena occurring with individual probability. With the measurement model, a set of H∞ state estimators is designed such that, for all admissible incomplete measurements as well as the uncertain coupling strength, the estimation error dynamics is exponentially mean-square stable and the H∞ performance requirement is satisfied. The characterization of the desired estimator gains is derived in terms of the solution to a convex optimization problem that can be easily solved using the semidefinite program method. Finally, a numerical simulation example is provided to demonstrate the effectiveness and applicability of the proposed design approach.

This paper is concerned with the distributed H∞filtering problem for a class of sensor networks with hybrid communication constraints. Three kinds of communications constraints are considered during the process of information transmission among the sensors: (i) the estimation of sensors needs to be sampled before transmitting; (ii) at each sampling instant, the packet dropouts would happen to the sampled data of sensors; (iii) the noise and disturbance exist. It should be noted that the constraints of sampled information and data packet dropouts would lead that less information can be employed for each sensor, which makes the filtering problem in sensor networks more challenging and practical. Some criteria concerning the connection gains are derived and used to design efficient distributed H∞filter to achieve the following objectives: (i) the filtering error system is exponentially mean-square stable in the absence of disturbance and noise; (ii) the prescribed H∞performance constraint is satisfied. A numerical example is utilized to illustrate the effectiveness of the theoretical results.

Controlling regions in cortical networks, which serve as key nodes to control the dynamics of networks to a desired state, can be detected by minimizing the eigenratio R and the maximum imaginary part \sigma of an extended connection matrix. Until now, optimal selection of the set of controlling regions is still an open problem and this paper represents the first attempt to include two measures of controllability into one unified framework. The detection problem of controlling regions in cortical networks is converted into a constrained optimization problem (COP), where the objective function R is minimized and \sigma is regarded as a constraint. Then, the detection of controlling regions of a weighted and directed complex network (e.g., a cortical network of a cat), is thoroughly investigated. The controlling regions of cortical networks are successfully detected by means of an improved dynamic hybrid framework (IDyHF). Our experiments verify that the proposed IDyHF outperforms two recently developed evolutionary computation methods in constrained optimization field and some traditional methods in control theory as well as graph theory. Based on the IDyHF, the controlling regions are detected in a microscopic and macroscopic way. Our results unveil the dependence of controlling regions on the number of driver nodes l and the constraint r. The controlling regions are largely selected from the regions with a large in-degree and a small out-degree. When r=+ \infty, there exists a concave shape of the mean degrees of the driver nodes, i.e., the regions with a large degree are of great importance to the control of the networks when l is small and the regions with a small degree are helpful to control the networks when l increases. When r=0, the mean degrees of the driver nodes increase as a function of l. We find that controlling \sigma is becoming more important in controlling a cortical network with increasing l. The methods and results of detecting controlling regions in this paper would promote the coordination and information consensus of various kinds of real-world complex networks including transportation networks, genetic regulatory networks, and social networks, etc.

In this paper, a hybrid extended Kalman filter (EKF) and switching particle swarm optimization (SPSO) algorithm is proposed for jointly estimating both the parameters and states of the lateral flow immunoassay model through available short time-series measurement. Our proposed method generalizes the well-known EKF algorithm by imposing physical constraints on the system states. Note that the state constraints are encountered very often in practice that give rise to considerable difficulties in system analysis and design. The main purpose of this paper is to handle the dynamic modeling problem with state constraints by combining the extended Kalman filtering and constrained optimization algorithms via the maximization probability method. More specifically, a recently developed SPSO algorithm is used to cope with the constrained optimization problem by converting it into an unconstrained optimization one through adding a penalty term to the objective function. The proposed algorithm is then employed to simultaneously identify the parameters and states of a lateral flow immunoassay model. It is shown that the proposed algorithm gives much improved performance over the traditional EKF method.

In this paper, the state estimation problem is investigated for a class of discrete time-delay nonlinear complex networks with randomly occurring phenomena from sensor measurements. The randomly occurring phenomena include randomly occurring sensor saturations (ROSSs) and randomly varying sensor delays (RVSDs) that result typically from networked environments. A novel sensor model is proposed to describe the ROSSs and the RVSDs within a unified framework via two sets of Bernoulli-distributed white sequences with known conditional probabilities. Rather than employing the commonly used Lipschitz-type function, a more general sector-like nonlinear function is used to describe the nonlinearities existing in the network. The purpose of the addressed problem is to design a state estimator to estimate the network states through available output measurements such that, for all probabilistic sensor saturations and sensor delays, the dynamics of the estimation error is guaranteed to be exponentially mean-square stable and the effect from the exogenous disturbances to the estimation accuracy is attenuated at a given level by means of an H∞-norm. In terms of a novel Lyapunov-Krasovskii functional and the Kronecker product, sufficient conditions are established under which the addressed state estimation problem is recast as solving a convex optimization problem via the semidefinite programming method. A simulation example is provided to show the usefulness of the proposed state estimation conditions.

In this paper, a new synchronization problem is addressed for an array of 2-D coupled dynamical networks. The class of systems under investigation is described by the 2-D nonlinear state space model which is oriented from the well-known Fornasini-Marchesini second model. For such a new 2-D complex network model, both the network dynamics and the couplings evolve in two independent directions. A new synchronization concept is put forward to account for the phenomenon that the propagations of all 2-D dynamical networks are synchronized in two directions with influence from the coupling strength. The purpose of the problem addressed is to first derive sufficient conditions ensuring the global synchronization and then extend the obtained results to more general cases where the system matrices contain either the norm-bounded or the polytopic parameter uncertainties. An energy-like quadratic function is developed, together with the intensive use of the Kronecker product, to establish the easy-to-verify conditions under which the addressed 2-D complex network model achieves global synchronization. Finally, a numerical example is given to illustrate the theoretical results and the effectiveness of the proposed synchronization scheme.

This article is concerned with a new distributed state estimation problem for a class of dynamical systems in sensor networks. The target plant is described by a set of differential equations disturbed by a Brownian motion and randomly occurring nonlinearities (RONs) subject to time delays. The RONs are investigated here to reflect network-induced randomly occurring regulation of the delayed states on the current ones. Through available measurement output transmitted from the sensors, a distributed state estimator is designed to estimate the states of the target system, where each sensor can communicate with the neighboring sensors according to the given topology by means of a directed graph. The state estimation is carried out in a distributed way and is therefore applicable to online application. By resorting to the Lyapunov functional combined with stochastic analysis techniques, several delay-dependent criteria are established that not only ensure the estimation error to be globally asymptotically stable in the mean square, but also guarantee the existence of the desired estimator gains that can then be explicitly expressed when certain matrix inequalities are solved. A numerical example is given to verify the designed distributed state estimators.

This paper is concerned with the network-based robust fault detection problem for a class of uncertain discrete-time Takagi-Sugeno fuzzy systems with stochastic mixed time delays and successive packet dropouts. The mixed time delays comprise both the multiple discrete time delays and the infinite distributed delays. A sequence of stochastic variables is introduced to govern the random occurrences of the discrete time delays, distributed time delays, and successive packet dropouts, where all the stochastic variables are mutually independent but obey the Bernoulli distribution. The main purpose of this paper is to design a fuzzy fault detection filter such that the overall fault detection dynamics is exponentially stable in the mean square and, at the same time, the error between the residual signal and the fault signal is made as small as possible. Sufficient conditions are first established via intensive stochastic analysis for the existence of the desired fuzzy fault detection filters, and then, the corresponding solvability conditions for the desired filter gains are established. In addition, the optimal performance index for the addressed robust fuzzy fault detection problem is obtained by solving an auxiliary convex optimization problem. An illustrative example is provided to show the usefulness and effectiveness of the proposed design method.

Although the last decade has witnessed a great deal of improvements achieved for the microarray technology, many major developments in all the main stages of this technology, including image processing, are still needed. Some hardware implementations of microarray image processing have been proposed in the literature and proved to be promising alternatives to the currently available software systems. However, the main drawback of those proposed approaches is the unsuitable addressing of the quantification of the gene spot in a realistic way without any assumption about the image surface. Our aim in this paper is to present a new image-reconstruction algorithm using the cellular neural network that solves the Navier-Stokes equation. This algorithm offers a robust method for estimating the background signal within the gene-spot region. The MATCNN toolbox for Matlab is used to test the proposed method. Quantitative comparisons are carried out, i.e., in terms of objective criteria, between our approach and some other available methods. It is shown that the proposed algorithm gives highly accurate and realistic measurements in a fully automated manner within a remarkably efficient time.

This paper deals with the distributed state estimation problem for a class of sensor networks described by discrete-time stochastic systems with randomly varying nonlinearities and missing measurements. In the sensor network, there is no centralized processor capable of collecting all the measurements from the sensors, and therefore each individual sensor needs to estimate the system state based not only on its own measurement but also on its neighboring sensors' measurements according to certain topology. The stochastic Brownian motions affect both the dynamical plant and the sensor measurement outputs. The randomly varying nonlinearities and missing measurements are introduced to reflect more realistic dynamical behaviors of the sensor networks that are caused by noisy environment as well as by probabilistic communication failures. Through available output measurements from each individual sensor, we aim to design distributed state estimators to approximate the states of the networked dynamic system. Sufficient conditions are presented to guarantee the convergence of the estimation error systems for all admissible stochastic disturbances, randomly varying nonlinearities, and missing measurements. Then, the explicit expressions of individual estimators are derived to facilitate the distributed computing of state estimation from each sensor. Finally, a numerical example is given to verify the theoretical results.

In this paper, new synchronization and state estimation problems are considered for an array of coupled discrete time-varying stochastic complex networks over a finite horizon. A novel concept of bounded H(∞) synchronization is proposed to handle the time-varying nature of the complex networks. Such a concept captures the transient behavior of the time-varying complex network over a finite horizon, where the degree of bounded synchronization is quantified in terms of the H(∞)-norm. A general sector-like nonlinear function is employed to describe the nonlinearities existing in the network. By utilizing a time-varying real-valued function and the Kronecker product, criteria are established that ensure the bounded H(∞) synchronization in terms of a set of recursive linear matrix inequalities (RLMIs), where the RLMIs can be computed recursively by employing available MATLAB toolboxes. The bounded H(∞) state estimation problem is then studied for the same complex network, where the purpose is to design a state estimator to estimate the network states through available output measurements such that, over a finite horizon, the dynamics of the estimation error is guaranteed to be bounded with a given disturbance attenuation level. Again, an RLMI approach is developed for the state estimation problem. Finally, two simulation examples are exploited to show the effectiveness of the results derived in this paper.

In this paper, the consensus problem of multiagent nonlinear directed networks (MNDNs) is discussed in the case that a MNDN does not have a spanning tree to reach the consensus of all nodes. By using the Lie algebra theory, a linear node-and-node pinning method is proposed to achieve a consensus of a MNDN for all nonlinear functions satisfying a given set of conditions. Based on some optimal algorithms, large-size networks are aggregated to small-size ones. Then, by applying the principle minor theory to the small-size networks, a sufficient condition is given to reduce the number of controlled nodes. Finally, simulation results are given to illustrate the effectiveness of the developed criteria.

In this paper, the robustH∞-control problem is investigated for a class of uncertain discrete-time fuzzy systems with both multiple probabilistic delays and multiple missing measurements. A sequence of random variables, all of which are mutually independent but obey the Bernoulli distribution, is introduced to account for the probabilistic communication delays. The measurement-missing phenomenon occurs in a random way. The missing probability for each sensor satisfies a certain probabilistic distribution in the interval. Here, the attention is focused on the analysis and design ofH∞fuzzy output-feedback controllers such that the closed-loop Takagi-Sugeno (T-S) fuzzy-control system is exponentially stable in the mean square. The disturbance-rejection attenuation is constrained to a given level by means of theH∞-performance index. Intensive analysis is carried out to obtain sufficient conditions for the existence of admissible output feedback controllers, which ensures the exponential stability as well as the prescribedH∞performance. The cone-complementarity-linearization procedure is employed to cast the controller-design problem into a sequential minimization one that is solved by the semi-definite program method. Simulation results are utilized to demonstrate the effectiveness of the proposed design technique in this paper.

In this paper, the problem of stochastic synchronization analysis is investigated for a new array of coupled discrete-time stochastic complex networks with randomly occurred nonlinearities (RONs) and time delays. The discrete-time complex networks under consideration are subject to: 1) stochastic nonlinearities that occur according to the Bernoulli distributed white noise sequences; 2) stochastic disturbances that enter the coupling term, the delayed coupling term as well as the overall network; and 3) time delays that include both the discrete and distributed ones. Note that the newly introduced RONs and the multiple stochastic disturbances can better reflect the dynamical behaviors of coupled complex networks whose information transmission process is affected by a noisy environment (e.g., internet-based control systems). By constructing a novel Lyapunov-like matrix functional, the idea of delay fractioning is applied to deal with the addressed synchronization analysis problem. By employing a combination of the linear matrix inequality (LMI) techniques, the free-weighting matrix method and stochastic analysis theories, several delay-dependent sufficient conditions are obtained which ensure the asymptotic synchronization in the mean square sense for the discrete-time stochastic complex networks with time delays. The criteria derived are characterized in terms of LMIs whose solution can be solved by utilizing the standard numerical software. A simulation example is presented to show the effectiveness and applicability of the proposed results.

Takagi-Sugeno (T-S) fuzzy models, which are usually represented by a set of linear submodels, can be used to describe or approximate any complex nonlinear systems by fuzzily blending these subsystems, and so, significant research efforts have been devoted to the analysis of such models. This paper is concerned with the passivity and passification problems of the stochastic discrete-time T-S fuzzy systems with delay. We first propose the definition of passivity in the sense of expectation. Then, by utilizing the Lyapunov functional method, the stochastic analysis combined with the matrix inequality techniques, a sufficient condition in terms of linear matrix inequalities is presented, ensuring the passivity performance of the T-S fuzzy models. Finally, based on this criterion, state feedback controller is designed, and several criteria are obtained to make the closed-loop system passive in the sense of expectation. The results acquired in this paper are delay dependent in the sense that they depend on not only the lower bound but also the upper bound of the time-varying delay. Numerical examples are also provided to demonstrate the effectiveness and feasibility of our criteria.

Robust stability serves as an important regulation mechanism in system biology and synthetic biology. In this paper, the robust stability analysis problem is investigated for a class of nonlinear delayed genetic regulatory networks with parameter uncertainties and stochastic perturbations. The nonlinear function describing the feedback regulation satisfies the sector condition, the time delays exist in both translation and feedback regulation processes, and the state-dependent Brownian motions are introduced to reflect the inherent intrinsic and extrinsic noise perturbations. The purpose of the addressed stability analysis problem is to establish some easy-to-verify conditions under which the dynamics of the true concentrations of the messenger ribonucleic acid (mRNA) and protein is asymptotically stable irrespective of the norm-bounded modeling errors. By utilizing a new Lyapunov functional based on the idea of "delay fractioning", we employ the linear matrix inequality (LMI) technique to derive delay-dependent sufficient conditions ensuring the robust stability of the gene regulatory networks. Note that the obtained results are formulated in terms of LMIs that can easily be solved using standard software packages. Simulation examples are exploited to illustrate the effectiveness of the proposed design procedures.

A general model for an array of discrete-time neural networks with hybrid coupling is proposed, which is composed of nonlinear coupling and time-varying delays. The coupling terms are described in terms of Lipchitz-type conditions that reflect more realistic dynamical behaviors of coupled systems in practice. The properties of Kronecker product are employed in order to pursue mathematical simplicity of dynamics analysis. On the basis of Lyapunov stability theory, an effective matrix functional is utilized to establish sufficient conditions under which the considered neural networks are globally synchronized. These conditions, which are dependent on the lower bound and the upper bound of the time-varying time delays, are expressed in terms of several linear matrix inequalities (LMIs), and therefore can be easily verified by utilizing the numerically efficient Matlab LMI toolbox. One illustrative example is given to justify the validity and feasibility of the proposed synchronization scheme.

In this paper, a new robustHinfinfiltering problem is investigated for a class of time-varying nonlinear system with norm-bounded parameter uncertainties, bounded state delay, sector-bounded nonlinearity and probabilistic sensor gain faults. The probabilistic sensor reductions are modeled by using a random variable that obeys a specific distribution in a known interval [alpha,beta], which accounts for the following two phenomenon: 1) signal stochastic attenuation in unreliable analog channel and 2) random sensor gain reduction in severe environment. The main task is to design a robustHinfinfilter such that, for all possible uncertain measurements, system parameter uncertainties, nonlinearity as well as time-varying delays, the filtering error dynamics is asymptotically mean-square stable with a prescribedHinfinperformance level. A sufficient condition for the existence of such a filter is presented in terms of the feasibility of a certain linear matrix inequality (LMI). A numerical example is introduced to illustrate the effectiveness and applicability of the proposed methodology.

In this paper, the extended Kalman filter (EKF) algorithm is applied to model the gene regulatory network from gene time series data. The gene regulatory network is considered as a nonlinear dynamic stochastic model that consists of the gene measurement equation and the gene regulation equation. After specifying the model structure, we apply the EKF algorithm for identifying both the model parameters and the actual value of gene expression levels. It is shown that the EKF algorithm is an online estimation algorithm that can identify a large number of parameters (including parameters of nonlinear functions) through iterative procedure by using a small number of observations. Four real-world gene expression data sets are employed to demonstrate the effectiveness of the EKF algorithm, and the obtained models are evaluated from the viewpoint of bioinformatics.

This paper is concerned with theHinfinfuzzy control problem for a class of systems with repeated scalar nonlinearities and random packet losses. A modified Takagi-Sugeno (T-S) fuzzy model is proposed in which the consequent parts are composed of a set of discrete-time state equations containing a repeated scalar nonlinearity. Such a model can describe some well-known nonlinear systems such as recurrent neural networks. The measurement transmission between the plant and controller is assumed to be imperfect and a stochastic variable satisfying the Bernoulli random binary distribution is utilized to represent the phenomenon of random packet losses. Attention is focused on the analysis and design ofHinfinfuzzy controllers with the same repeated scalar nonlinearities such that the closed-loop T-S fuzzy control system is stochastically stable and preserves a guaranteedHinfinperformance. Sufficient conditions are obtained for the existence of admissible controllers, and the cone complementarity linearization procedure is employed to cast the controller design problem into a sequential minimization one subject to linear matrix inequalities, which can be readily solved by using standard numerical software. Two examples are given to illustrate the effectiveness of the proposed design method.