This paper addresses the dynamic event-triggered (DET) state and fault interval estimation issue for networked switched systems under asynchronous switching scenarios with persistent dwell time (PDT) restriction. To promote a trade-off between performance and communication efficiency, an improved discrete-time DET mechanism with double dynamic variables is proposed, in which the added dynamic variable facilitates more efficient resource saving. By introducing mode-dependent auxiliary variables, new switched intermediate estimators are designed for interval estimation, which not only are free from the estimator matching condition, but also increase the degree of design freedom. Furthermore, considering the general asynchronism between the systems and the estimators induced by the DET mechanism, a zonotope-based iterative procedure is provided to construct mode-dependent zonotopes involving state and faults. By means of the analysis in the asynchronous switching scenarios with PDT restriction, less conservative feasibility conditions are formulated to optimize the design parameters. In addition, estimator-mode-dependent radius functions are constructed to promote the settlement of L∞performance problem for the radii of the zonotopes. Finally, the feasibility and superiority of the designed scheme are confirmed by an application example.

This paper is concerned with the problem of the optimalH∞fuzzy proportional-integral-derivative (PID) controller design for Takagi-Sugeno fuzzy systems subjected to infinite-distributed time delays and weighted try-once-discard (WTOD) scheduling effects. To avoid data collisions in communication networks, an improved WTOD communication protocol is proposed, which offers higher transmission precedence to the most needed data node. According to non-parallel distributed compensation scheme, a novel yet easy-to-implement fuzzy PID controller is devised, in which the integral term of the fuzzy PID controller equipped with a limited time window can be utilized for mitigating computational burden. Subsequently, considering the feasible regions of membership functions (MFs) of the fuzzy PID controller, in the case of guaranteeing stability of explored systems, a novel MFs online iterative learning strategy employing an adaptive momentum estimation algorithm is first provided for networked fuzzy systems. The proposed algorithm addresses the limitation of designing fuzzy controller MFs based on designers’ experience. On the basis of the provided MFs online iterative learning approach, MFs of the fuzzy PID controller are renewed in real-time for achieving betterH∞performance, namely, disturbance attenuation capacity for the explored systems. Ultimately, the feasibility of the developed control strategy is illustrated by means of some simulation results.

Although federated learning stands as a prominent distributed learning paradigm across multiple clients, it has grappled with a dilemma: the choice between developing a singular global model to promote global generalization or nurturing personalized models to accommodate personalization. In this article, a federated learning with warm-start (WarmFed) is proposed to obtain both robust global and personalized models. First, a client knowledge-driven initialization approach named warm-start is introduced, which provides resilient global information to assist subsequent training. Then, a dynamic self-distillation (DSD) strategy is proposed to obtain more resilient personalized models and a model fine-tuning (MFT) mechanism based on synthetic data is designed to improve performance of the global model on the server side. The collaborative deployment of DSD and MFT exploits potential to achieve both generalization and personalization under warm-start. Comprehensive experiments underscore the superiority of our approach across both global and personalized models under domain shift and label skew.

As is known, the problem of making a trained deep reinforcement learning (DRL) model to adapt to a new environment different from where it trains remains open. In this article, DRL-based techniques are investigated and deployed for the robotic motion planning task in locally changed environments. First, a dynamic-entropy actor-critic is proposed to automatically adjust the coefficient of the entropy cluster to enable the agent to efficiently learn the potentially optimal policy. Second, a tutoring-guiding mechanism is established to heuristically explore and sufficiently exploit valuable experience. Third, a mechanism consisting of progressive planner and inverse kinematics mapper is designed to cope with localized changes in the environment, which enables the robot manipulator to rapidly adapt to the newly changed environment. Finally, experimental results have verified the superior performance of the proposed approaches.

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.

In this article, an integral sliding mode (ISM) control scheme is developed to tackle platooning control issues of vehicle systems under both an improved constant-time headway (CTH) spacing strategy and a coding-decoding communication protocol (CDCP) with constrained bit rate (BR). The utilization of CDCPs can effectively reduce communication burden and promote data security, while the improved CTH spacing policy based on relative velocities can ensure high-traffic efficiency as well as platoon safety. First, an ISM surface is constructed to handle matched unmodeled dynamics by resorting to the improved CTH spacing strategy and the decoded information governed by CDCPs. Both an equivalent ISM law and a practical ISM controller are constructed in terms of such an ISM surface, reflecting the effect of both spacing strategies and decoded information. Then, a generally dynamical model of platoon tracking errors involving an unusual coupling with the adopted spacing strategy is derived by leveraging both the equivalent ISM law and a matrix transformation technique. Moreover, the reachability of the proposed ISM surface and the required platooning performance are profoundly discussed and sufficient conditions to determine the required gain parameters are deduced with essential inequality techniques. In terms of vehicle tracking errors, the upper boundary is determined by both the BR assigned and the spacing strategy employed. Finally, the effectiveness of the devised ISM control scheme is professionally evaluated through Carsim’s simulation instances.

Although robotic trajectory generation problem has been extensively investigated, existing solutions are almost customized to specific robot geometry, and generalized schemes are yet to be explored. In this article, a general motion planning framework based on deep reinforcement learning (DRL) and artificial neural networks (ANNs) is proposed for robot with arbitrary geometry. First, a unique screening and grafting mechanism is established to improve the policy learning by exploiting valuable experience sufficiently. Second, based on the reward-oriented characteristics of DRL, a forward progression mechanism is proposed to facilitate the path planning for complicated tasks. Third, a structure consisting of an adventurer and conservator algorithm with automatic optimization and an ANN-based mapper is designed integrally to derive the inverse kinematics solutions without considering the robot geometry. Finally, experimental results have verified the superior performance of the proposed approach.Note to Practitioners—This article is aiming to provide a general method to solve the problem of motion planning for robots via deep reinforcement learning (DRL) and artificial neural networks (ANNs). Compared to the existing approaches, which are highly specialized and limited to robots with specific geometries, and often cumbersome, our method can be easily applied to robots with arbitrary geometries and has good generalization, where to simplify the training based on DRL for diverse practical motion planning tasks, a universal forward progression mechanism is used to partition a complex task into multiple successive simple phases. Furthermore, an ANN-based mapper can cope with the burdensome inverse kinematics of robots with both common and uncommon geometries, especially the ones with redundant degrees of freedom. The proposed method is also validated to be superior to other state-of-the-art methods in real-world experimental studies.

This paper is concerned with cluster consensus of multi-agent systems (MASs) with homogeneous and heterogeneous dynamics. Firstly, a resilient dynamic event-triggered (RDET) mechanism is proposed by introducing two parameters in the dynamic triggering threshold to describe busy and free networks of each cluster. An auxiliary function is configurated to describe the network status of agents in different clusters. Agents allocated in multiple clusters are classified into two categories: some agents are in the busy network environment and the rest are in the free network environment. The resulting consensus error system is modeled as an augmented system involving two time-varying delays with different bounds. Then, some sufficient criteria are derived for the stability analysis of homogeneous and heterogeneous MASs, respectively. Based on the obtained stability criteria, an algorithm consisting of particle swarm optimization (PSO) and a genetic algorithm (GA) is designed for a co-design of control gains and event-triggered parameters. Finally, a numerical example of satellite formation flying is simulated to illustrate the merits of the developed theoretical results.Note to Practitioners—The objective of this article is to design addresses the challenge of achieving consensus in MASs with diverse dynamics, such as those found in autonomous vehicle fleets or sensor networks. We’ve introduced a RDET mechanism that can handle both busy and free network environments by adjusting its triggering threshold based on network status. This allows agents to communicate efficiently, reducing the need for constant communication and conserving resources. Agents in multiple clusters are categorized into those in high-traffic (busy) and low-traffic (free) networks. We’ve developed a model for the consensus error system that accounts for time-varying delays, which is crucial for stability analysis in both homogeneous and heterogeneous MASs. To optimize performance, we propose an algorithm combining PSO and GA. This powerful tool co-designs control gains and event-triggered parameters, ensuring efficient and stable communication strategies for all agents regardless of their specific dynamics. In practical terms, this work provides a robust framework to improve inter-agent cooperation in complex industrial scenarios, such as large-scale robot swarms, intelligent transportation systems, or even satellite constellation management. By implementing the RDET mechanism with our optimization algorithms, practitioners can expect to achieve tighter synchronization, reduced communication costs, and better overall system resilience, as demonstrated through a simulation of satellite formation flying.

Economic dispatch problems (EDPs), as a basic issue of smart grids, have appealed to a wide range of research interests owing to the expansion of network scales and the increase of system complexity. The flexibility of economic dispatch algorithms puts forward urgent requirements of distributed optimization methods dependent on information exchanges, which may lead to the leakage of private information. To solve this problem, a privacy-preserving strategy in a distributed paradigm is proposed by adding artificial sequences to the transmitted multi-step gradient information. In light of such a strategy, a new distributed privacy-preserving optimization approach in light of multi-step gradient information is developed to handle the addressed EDPs. When introduced parameter sequences satisfy suitable conditions, both the convergence to the optimal solution and the privacy of sensitive parameters in the generator cost are effectively guaranteed. Finally, an illustrative simulation is specially offered to verify the validity of the developed strategy.

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.

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 islanded ac microgrids with heterogeneous distributed generations (DGs), secondary frequency control (SFC) can restore the frequency that deviates from the nominal level due to the primary P-f droop control. Traditionally, to realize SFC, the instantaneous frequency needs to be directly measured or estimated to produce frequency compensations, which increases system cost and complexity. In this article, a new spatial load power forecasting for distributed secondary frequency control (SLPF-DSFC) based on artificial neural networks is proposed. Without frequency measurement, it can effectively eliminate frequency deviations and autonomously cope with various load changes by optimizing active power outputs from DGs. The distributed average and pinning protocols based on graph theory are also included. Comprehensive case studies demonstrate that the proposed SLPF-DSFC method shows superior performance over conventional SFC approaches in terms of frequency stabilization and voltage harmonics reduction during load variations, SFC startup/shutdown, as well as communication delays.

This article is concerned with the problem of dynamic event-triggered (DET) scaled consensus control for multi-agent systems (MASs) in reliable and unreliable networks. First, by introducing an auxiliary dynamic variable (ADV) obeying its own dynamics, a novel DET mechanism is designed. A nonzero exponentially decaying term is introduced in the DET scheme to enlarge the interval between two consecutive trigger instants. In reliable networks, the DET mechanism works with a set of fixed parameters for the nonzero exponentially decaying term. Second, by introducing a stochastic variable into the parameters to describe the uncertain denial-of-service (DoS) attacks, a resilient DET mechanism is proposed. In unreliable networks, the resilient DET mechanism adjusts the set of parameters between two sets of values by detecting the launch of DoS attacks. Then, by choosing suitable Lyapunov–Krasovskii functions (LKFs), some stability criteria are derived for scaled consensus of MASs both in reliable and unreliable networks. Based on the stability criteria expressed in matrix inequalities, a control design algorithm based on genetic algorithm (GA) is proposed to calculate the control gains and event trigger parameters jointly. Finally, the effectiveness of the results is verified via a numerical example of multiple two-wheel mobile vehicles.

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.

This article addresses jointly distributed entropy filtering issues based on the generalized maximum correntropy criterion (GMCC) for discrete-time stochastic parameter systems with fault and non-Gaussian noise effects. By taking current and historical triggered information, a memory-based event-triggered scheme with a time-varying threshold is put forward to govern the network communication. According to the constructed jointly distributed entropy filter with a two-step form, the upper bounds of the filtering error covariance matrices are derived and an ideal filter gain is obtained to maximize GMCC. Furthermore, an accessible gain is received via fixed-point iterative rules and the corresponding convergence is disclosed in theory. Finally, an application of the proposed distributed filter in ballistic object tracking is provided to show its effectiveness under non-Gaussian environments.

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.

This article is concerned with the robust recursive filtering (RF) problem for a class of stochastic uncertain systems subject to time-correlated fading channels. The measurement received by the sensor is transmitted to the remote filter through the time-correlated fading channel where the channel coefficient evolves according to a certain dynamics and hence exhibits a time-correlated nature. The parameter uncertainties of the system are described by norm-bounded unknown matrices. By introducing a class of auxiliary variables, an augmented system is constructed to reflect the dynamics of the fading coefficient and state simultaneously. Then, a recursive filter is designed which is capable of online computation. Furthermore, an upper bound is guaranteed for the filtering error covariance (FEC) for the possible parameter uncertainties as well as the time-correlated fading channels. With the help of the completing-the-squares technique, filter gains are parameterized by minimizing the obtained upper bound. Finally, two examples are employed to verify the effectiveness of the proposed robust RF method.

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.

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.

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.

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 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 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.

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.

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 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, a data-driven scheme of key performance indicator (KPI) prediction and diagnosis is developed for complex industrial processes. For static processes, a KPI prediction and diagnosis approach is proposed in order to improve the prediction performance. In comparison with the standard partial least squares (PLS) method, the alternative approach significantly simplifies the computation procedure. By means of a data-driven realization of the so-called left coprime factorization (LCF) of a process, efficient KPI prediction, and diagnosis algorithms are developed for dynamic processes, respectively, with and without measurable KPIs. The proposed KPI prediction and diagnosis scheme is finally applied to an industrial hot strip mill, and the results demonstrate the effectiveness of the proposed scheme.

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.

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.

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.

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.