The rapid development of intelligent connected vehicles has led to widespread attention for platooning control, which is an effective method to mitigate a variety of societal issues. This paper is concerned with platooning control via a refined constant-time headway (CTH) strategy in the framework of model predictive control (MPC), where round-robin (RR) protocols are introduced to alleviate the communication burden. First, a dynamic model for platoon tracking errors is developed by using matrix transformation to account for the influence of the refined CTH strategy and the RR protocol. With the help of stability analysis, the original optimization problem involving unknown disturbances is transformed into an auxiliary MPC optimization problem to minimize its upper bound. Considering the cyclic characteristics of RR protocols, some sufficient conditions are then acquired to ensure the recursive feasibility of the MPC optimization problem. The desired controller parameters are obtained by means of an online optimization algorithm. Furthermore, the stability of platooning systems is disclosed under the developed sufficient conditions. Finally, the effectiveness of the devised control scheme is evaluated through numerical simulations.

This paper addresses the problem of input-output data-based ultimate boundedness control for a class of networked systems subject to probabilistic bit flips and false data injection (FDI) attacks under the try-once-discard (TOD) protocol. First, a prior experiment is conducted to obtain a set of input-output data from the considered system, which will be utilized for the data-based controller design. An uniform-quantization-based encoding-decoding mechanism is employed to digitalize measurement signals. The TOD protocol is adopted to schedule signal transmissions between encoders and decoders. Considering the nature of digital communication, an ellipsoid constraint and a sequence of Bernoulli variables are introduced to account for the FDI attacks and bit flips during the transmission, respectively. To expediently design the data-based controller, a novel autoregression-based method is proposed subject to probabilistic bit flips and protocol-induced effects. This paper aims to design a data-driven controller that ensures the ultimate boundedness of the closed-loop system under the effects of TOD protocol scheduling and communication failures. Sufficient conditions are presented to ensure the desired control performance by using the S-Lemma from data. An improved cone complementarity linearization (CCL) algorithm is developed to calculate the controller gain. Eventually, a numerical simulation example is provided to demonstrate the effectiveness and feasibility of the proposed data-based ultimate boundedness control scheme.

Vehicle platoon systems (VPSs) constitute a fundamental component of intelligent transportation systems (ITSs), enhancing traffic efficiency, safety, and energy conservation. This paper presents an adaptive control strategy for unknown non-linear heterogeneous VPSs under a bidirectional communication topology. The proposed approach simultaneously addresses asymmetric spacing constraints, actuator saturation, and dead-zone nonlinearities. First, an equivalent transformation is adopted to eliminate the need for precise modeling of both system dynamics and actuator nonlinearities. An adaptive mechanism is then designed to jointly estimate the bounds of approximation errors, the norm of ideal NN weights, and the derivative bounds of control inputs. Based on both the estimated bounds and the gap about the time-headway policy, a novel controller is constructed to enhance safety and vehicle stability. Furthermore, a barrier Lyapunov function (BLF) is incorporated to rigorously enforce inter-vehicle spacing constraints, thereby ensuring collision avoidance. The proposed control scheme is theoretically proven and numerically validated to guarantee both individual vehicle stability and string stability of the platoon.

• First Mamba-based framework HMamba-3DFT tailored for 3D facial tracking • BSTV-Mamba with BSTS-Scan capture spatiotemporal facial dynamics • Dual optimization integrates dynamic emotion-driven modeling with semantic alignment Monocular video-based 3D face tracking is vital for interactive pattern recognition and human avatars. Most existing image-based methods fail to model temporal dependencies in video, causing jitter and inaccuracies. Furthermore, they also often neglect the continuous multi-modal signals present in facial videos such as expression dynamics and emotional cues that provide essential temporal drivers for facial modeling. To this end, this study first explores the Mamba architecture tailored for 3D facial tracking by proposing a hierarchical Mamba framework, termed HMamba-3DFT. The proposed network can efficiently capture and track variations in 3D facial shapes from a monocular video. To exploit the global spatiotemporal correlations across frames of the dynamic face, we develop a bidirectional spatiotemporal vision Mamba (BSTV-Mamba) module featuring a bidirectional spatiotemporal selective scan (BSTS-Scan) mechanism. To capture temporally evolving multi-modal emotion signals embedded in continuous video sequences, we introduce a dynamic emotion-driven mechanism. Additionally, to mitigate the potential degradation of reconstruction fidelity caused by an over-reliance on emotion-driven cues, we integrate facial semantic alignment with facial emotion driving to enhance the accuracy of emotion-driven facial modeling. This integrated dual-optimization strategy systematically guides the network during training, ensuring that the reconstructed 3D facial mesh not only accurately captures the emotional attributes of the input frames but also benefits from enhanced optimization for more precise reconstruction. Extensive evaluations on benchmark datasets show competitive performance against state-of-the-art methods.

The distributed energy management (DEM) problem of smart grids is devoted to achieving optimal energy dispatch and allocation to ensure social welfare maximization. It can be modelled as a distributed optimization problem with physical constraints, whose solution depends on data sharing between smart devices. The exchange of information creates a potential risk for eavesdroppers to intercept and access private data. To avoid privacy leakages, a privacy-preserving optimization strategy via output masks in a consensus framework is proposed to realize social welfare maximization of energy management, where power demand relies on the parameters of the demand sides. Theoretical analysis discloses that eavesdroppers are unable to accurately infer private information of the generators/demand loads. Furthermore, both the convergence and optimality of the proposed strategy are discussed by means of the matrix perturbation theory and the famous KKT optimality condition. Finally, the effectiveness of the proposed strategy for solving the DEM problem is confirmed by the simulation experiments.

High-fidelity 3-D face reconstruction is critical for enhancing personalized and immersive human–machine interaction experiences. However, existing methods struggle to capture the full spectrum of facial textures, particularly fine-scale details, such as wrinkles and pores, due to limitations in multiscale representation. To address this challenge, we propose a self-supervised multiscale hierarchical network to hierarchically model fine geometric details in multiple scales in this study. We design a global and local Markov random field loss and a detail perception loss to provide a global and local sensory field of view guidance for retaining fine-scale detail structure information of the face. In addition, we introduce a learnable Gabor-aware texture enhancement module to enhance the network’s sensitivity to fine textures. Extensive experiments show that the proposed method can reconstruct fine-scale details of the face and has superior performance to the state-of-the-art methods in terms of reconstruction accuracy and visual effect.

Frequency stability is vital for the reliable operation of critical equipment in power systems. However, increasing renewable integration and unpredictable actuator faults hinder traditional controllers from achieving frequency regulation within a fixed and predetermined time. To overcome such a challenge, a novel resilient decentralized fixed-time dynamic feedback controller is proposed for multi-area power systems subject to non-homogeneous Markovian jumps, actuator faults, and load fluctuations. In contrast to existing fixed-time control approaches dependent on state change rates, the proposed dynamic controller is uniquely governed by both control amplitude bounds and quadratic state terms. The designed controller rigorously guarantees both the stochastic fixed-time stability and the L∞ performance despite simultaneous actuator failures and non-homogeneous parameter jumps. By applying Lyapunov differential inequalities and set measure theory, a tractable design criterion is established to facilitate the solution of desired gain matrices, ensuring scalability and plug-and-play functionality. A three-area power system is finally utilized to evaluate the effectiveness of the developed control strategy regarding fixed time convergence and resilience against load fluctuations and actuator faults.

Head avatar reenactment focuses on creating animatable personal avatars from monocular videos, serving as a foundational element for applications like social signal understanding, gaming, human-machine interaction, and computer vision. Recent advances in 3D Morphable Model (3DMM)-based facial reconstruction methods have achieved remarkable high-fidelity face estimation. However, on the one hand, they struggle to capture the entire head, including non-facial regions and background details in real time, which is an essential aspect for producing realistic, high-fidelity head avatars. On the other hand, recent approaches leveraging generative adversarial networks (GANs) for head avatar generation from videos can achieve high-quality reenactments but encounter limitations in reproducing fine-grained head details, such as wrinkles and hair textures. In addition, existing methods generally rely on a large amount of training data, and rarely focus on using only a simple selfie video to achieve avatar reenactment. To address these challenges, this study introduces a method for detailed head avatar reenactment using a selfie video. The approach combines 3DMMs with a StyleGAN-based generator. A detailed reconstruction model is proposed, incorporating mixed loss functions for foreground reconstruction and avatar image generation during adversarial training to recover high-frequency details. Qualitative and quantitative evaluations on self-reenactment and cross-reenactment tasks demonstrate that the proposed method achieves superior head avatar reconstruction with rich and intricate textures compared to existing approaches.

Abstract Event-triggered control (ETC) offers an efficient strategy for significantly reducing communication and computation resources in networked systems by triggering control updates only when necessary. This study provides an overview of recent advances in ETC. First, data-driven (or model-free) ETC, which has gained significant attention in recent years, is reviewed for linear systems with and without unknown disturbances. Second, co-design issues are deeply analyzed for both state feedback and dynamic output feedback control. Third, the separation principle is thoroughly examined in the context of event-triggered observer-based output feedback control. Fourth, some insightful discussions are made on the ideal execution property of event-triggered schemes, as well as the modeling of ETC under packet dropouts. Finally, several challenging issues for future research are outlined.

This study addresses the problem of event-triggered and privacy-preserved platooning control of connected automated vehicles with finite communication resources and data privacy constraints. To efficiently use the communication resources, an asynchronous edge-based dynamic event-triggered mechanism that features adaptive edge-related triggering parameters is designed. Such a design allows for dynamic scheduling of the inter-vehicle communication on a per-edge basis while avoiding the Zeno behavior. Privacy of transmitted vehicular data is then protected through a novel hybrid privacy-preserving strategy that combines output masking with matrix transformation. Subsequently, a set of event-triggered adaptive distributed estimators with guaranteed privacy is developed to facilitate each follower vehicle’s accurate estimation of the full leader motion state. The state estimates are then employed in the design of neural adaptive platoon controllers such that each follower vehicle in the platoon follows the leader with synchronized speed and acceleration under a refined constant time headway spacing policy. Tractable design criteria for admissible estimator and controller gains as well as triggering and learning parameters, are further derived. Finally, co-simulations using CarSim and MATLAB/Simulink are performed to validate the effectiveness of the derived results.

Intelligent transportation systems (ITSs) are viewed as a potential solution to various social and environmental issues caused by the rapid increase in the number of vehicles. As a kernel of ITSs, platooning control reveals a superior ability in enhancing traffic efficiency. This paper looks into the issue of collision-free platooning control for automated vehicles based on watermarking-based information exchange. The relative velocity of vehicles is used to propose an improved constant time headway (ICTH) that enhances both traffic efficiency and safety requirements. Then, an effective platooning controller is created by combining a platooning tracking protocol and a collision avoidance protocol via an artificial potential field. Some sufficient conditions are established to ensure the required platooning requirement with collision avoidance, as well as data privacy under the adopted watermarking scheme. In addition, the desired gain of collision-free controllers can be achieved by solving matrix inequalities, regardless of the number of vehicles involved. Finally, the proposed collision-free platooning control scheme is verified by comprehensive simulation results.

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.

Distributed economic dispatch (ED) has emerged as a critical issue in microgrid operations due mainly to the wide application of various clean energy as well as energy storage units. The openness of communication networks in microgrids can lead to privacy breaches, which pose a serious threat to the entire electricity market. As such, this paper presents a distributed ED algorithm based on the alternating direction method of multipliers (ADMM), where a quantization-based encryption and decryption rule is integrated to avoid privacy leakage while iteratively acquiring the optimal ED scheme. By resorting to the property of monotonically convergent sequences, a sufficient condition about the learning rate is profoundly revealed to guarantee the algorithm convergence. Two extended results are presented, respectively, to enhance the convergence rate and meet the requirement of plug-and-play scenarios. Finally, the validity (both privacy and optimality) of the proposed algorithm is verified by using the dual-source trolleybus system in Beijing. Note to Practitioners—This paper develops an engineering-oriented ED algorithm that optimizes the total generation costs of smart grids online while guaranteeing system constraints. Shared network communication undoubtedly plays a significant role in achieving iteratively the optimal solution of distributed algorithms. However, some crucial and sensitive information exchanged via an open and shared network could be eavesdropped by malicious attackers, which could result in a serious security threat affecting the reliability and stability of the smart grid. To overcome such a shortage, an encryption-decryption rule is constructed via a dynamic quantizer. In light of such a rule, the presented algorithm based on ADMM can iteratively acquire the optimal ED solution in a distributed way, realizing the requirements of optimality and privacy. The desired range of the learning rate is disclosed to guide the parameter selection, and two improved versions are proposed to meet more general engineering practice involving plug-and-play scenarios.

Within the scope of location-based services and personalized recommendations, the challenges of recommending new and unvisited points of interest (POIs) to mobile users are compounded by the sparsity of check-in data. Traditional recommendation models often overlook user and POI attributes, which exacerbates data sparsity and cold-start problems. To address this issue, a novel multiplex hypergraph attribute-based graph collaborative filtering is proposed for POI recommendation to create a robust recommendation system capable of handling sparse data and cold-start scenarios. Specifically, a multiplex network hypergraph is first constructed to capture complex relationships between users, POIs, and attributes based on the similarities of attributes, visit frequencies, and preferences. Then, an adaptive variational graph auto-encoder adversarial network is developed to accurately infer the users’/POIs’ preference embeddings from their attribute distributions, which reflect complex attribute dependencies and latent structures within the data. Moreover, a dual graph neural network variant based on both Graphsage K-nearest neighbor networks and gated recurrent units are created to effectively capture various attributes of different modalities in a neighborhood, including temporal dependencies in user preferences and spatial attributes of POIs. Finally, experiments conducted on Foursquare and Yelp datasets reveal the superiority and robustness of the developed model compared to some typical state-of-the-art approaches and adequately illustrate the effectiveness of the issues with cold-start users and POIs.

This paper proposes a resilient output-feedback frequency regulation mechanism for multi-area power systems, ensuring secure and stable operation. Firstly, the uncertainties introduced by state-of-charge (SOC) variations are considered for the addressed system involved electric vehicle (EV). Subsequently, the behavior of hybrid replay-DoS attacks is taken into account, where a non-homogeneous Markov jump framework is employed to capture both stochastic transitions and temporal dependencies of attack behaviors, and a decentrilized resilient controller is designed to mitigate these adverse effects. To facilitate controller synthesis, a refined model of the lossless power network is further developed by equivalently transforming tie-line power deviations in specific areas, ensuring compliance with tie-line power constraints while preserving system controllability. Based on this model, a comprehensive analysis of system dynamics is performed, and a set of delay-dependent, computationally tractable criteria is derived for controller gain selection. Finally, extensive simulations on three-area power systems validate the effectiveness of the proposed strategy in maintaining frequency stability, mitigating SOC uncertainties, and counteracting hybrid replay-DoS attacks.

Face occlusion poses a challenge for many human–machine systems, such as facial expression perception, social signal analysis, and human identity verification. Accurate face deocclusion is essential for improving the performance of identity recognition, expression recognition, and the robustness of human–machine systems. As a result, this area has attracted significant attention from researchers in recent years. However, most existing methods rely heavily on synthetic occluded face datasets and predefined occlusion masks labels, which limits their applicability in real-world scenarios. To this end, we propose a novel self-supervised generative adversarial networks (GANs)-based framework for face deocclusion in this study, which integrates 3-D facial reconstruction with outlier segmentation guidance. To achieve reliable self-supervised occlusion guidance, we introduce an outlier segmentation module that utilizes statistical priors to generate accurate occlusion masks, facilitating the deocclusion process. Furthermore, we design a GAN-based dual-branch module, which is capable of simultaneously generating the occlusion mask and the deoccluded face. Extensive experiments on the widely used datasets demonstrate the superior performance of our approach on existing methods. Our method achieves 35.71 in peak signal to noise ratio (PSNR) and 0.891 in structural similarity index measure (SSIM) for occluded face restoration, outperforming state-of-the-art techniques.

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 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 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, a neural-network (NN)-based approach of distributed state estimation with probabilistic constraints is proposed for a class of nonlinear systems over sensor networks. For the discussed plant, the unknown nonlinear dynamics are approximated by resorting to NNs and the communication among estimators and sensors is scheduled by encoding–decoding schemes. The goal of the addressed problem is to design a distributed estimator such that, in the presence of the bounded noises, all possible errors are confined to some certain region in a predetermined probability while achieving the exponentially bounded performance in a finite time domain. In light of the matrix operation, some sufficient conditions are obtained to ensure the existence of the desired gains of estimators, which are computed by dealing with the corresponding matrix inequalities in an iterative way. The effectiveness of the proposed distributed state estimation method is verified by presenting an example of a one-track model.

This article mainly considers the networked reset proportional-integral (PI) control issue for a class of cyber physical systems subject to malicious denial-of-service (DoS) attacks and bounded disturbances. Sensor measurement signals are encoded into symbols, and transmitted through the shared communication channel, which has bit rate constraints while susceptible to malicious DoS attacks. In this context, the final closed-loop system is modeled as a hybrid one, which better emphasizes the characteristics of the discussed networked phenomena and reset mechanisms. Then, with the help of the constructed comparison system, a bit rate constrained condition is obtained to ensure that the decoding error is bounded. Subsequently, a series of sufficient conditions are proposed to provide the exponential boundedness of the closed-loop system. Finally, a numerical simulation is presented to validate the effectiveness of the proposed theoretical results.

Economic dispatch plays a significant role in intelligent microgrids, which offer a promising approach to facilitate the integration of distributed renewable energy. In the past few years, information sharing has promoted new development opportunities for distributed economic dispatch algorithms of microgrids. However, integrating communications into distributed algorithms gives rise to significant concerns about data security and privacy. As such, this paper investigates a privacy-preserving distributed economic dispatch problem within microgrids. The primary objective is to propose a privacy-preserving distributed zero-gradient-sum (DZGS) algorithm that realizes optimal power dispatch with the balance of supply and demand while ensuring sensitive information from potential leakage. To achieve this goal, the hybrid privacy strategies based on a variable decomposition rule and noise injection are implemented to enhance the security of sensitive data. Sufficient conditions, including a suitable range of step sizes, are established theoretically to reveal the linear convergence of the algorithm. Furthermore, the privacy of the algorithm is analyzed from the perspectives of both honest-but-curious nodes and external eavesdroppers. Finally, the effectiveness of the algorithm is validated through empirical analyses of the IEEE 39-bus system.

元宇宙可被视为一个社会化和虚拟化的网络空间, 与现实世界平行但互动. 得益于云计算和数字孪生的快速发展, 元宇宙正在将带有传统控制和滤波范式的工业自动化系统转变为信息物理社会融合系统. 在此情况下, 未来的工业自动化系统可能是在一定时间和空间范围内具有强大计算能力的现实世界系统与虚拟孪生系统的集成. 在该领域中, 元宇宙重点涉及信息传输管理、 用户的行为识别以及控制、 滤波和决策, 且性能和成本将是系统在网络空间和现实世界中行为的综合反映. 毫无疑问, 虚拟网络空间和现实世界之间的信息交换的内在特征, 唤起了对安全控制和滤波的新需求. 如何保证期望的系统性能和实现理想的参数设计, 已然成为该领域研究的重大挑战. 然而, 目前对元宇宙的研究主要集中在解决其社会关切和寻求其在各个领域的应用, 其在控制领域的发展仍处于初级阶段. 当网络空间与现实世界交换感兴趣的信息时, 恶意攻击不可避免, 这可能导致数据不可靠或隐私泄露. 因此, 定义合适的评估标准来揭示网络攻击的影响, 并提供面向工程的安全控制和滤波方案, 具有重要意义. 本期专题旨在推进工业元宇宙中安全控制与滤波的理论研究和技术开发, 服务于智能制造的发展需要. 感谢所有作者对此专题的贡献, 感谢所有为作者提供宝贵意见的评审人. 衷心感谢《信息与电子工程前沿(英文)》期刊执行副主编王飞跃教授鼓励我们组织本期专题, 以及编辑人员的全程支持.

Secure state estimation is becoming more popular due to the inherent vulnerabilities of communication networks in essence, which could give rise to potential data leakage and manipulation of microgrids. The paper addresses the issue of secure distributed state estimation for a class of microgrids with potential outliers occurring in sensor measurements. First, a secure distributed estimator is constructed by introducing both an artificial saturation rule to achieve outlier resilience and a variable decomposition strategy to safeguard data security, where the generated dynamic key is a time-varying sequence satisfying the predetermined constraint. Deep variance analysis is carried out to profoundly disclose the relationship between private and public estimation error covariance, in accordance with the employed decomposition rule. An upper bound of error covariance is determined by two sets of recursive matrix equations in contrast to that of traditional distributed estimation. Furthermore, the desired estimator gains are obtained recursively with the aid of optimizing the upper bound obtained above. In the end, a simulation example is proposed to confirm the effectiveness and security of the proposed algorithm.

Video summarization (VS) technologies can automatically extract key frames with effective information and thus can help to quickly identify the events or speed up the decision-making process, especially for accidents. With the fast development of deep learning technologies, many generative adversarial network (GAN)- and reinforcement learning (RL)-based unsupervised VS methods have been developed in recent years. However, these methods could suffer from the problems of unstable training and difficulty of reward function formulation, respectively. To this end, we present an unsupervised VS method called diffusion model of feature fusion (DMFF) in this article, which consists of a diffusion module (DM), a feature extraction and compression module (FECM), and a coarse-fine frame selector (CFFS). DM is designed to avoid the training instability problem caused by GAN’s alternate training generator and discriminator. FECM is used to extract and compress video features. CFFS is designed to capture both low-level and high-level features between frames to handle complex and diverse accident videos. Then, high-level local and global features are fused to generate a multigrained final frame score. Experiments on two widely used benchmark datasets, SumMe and TVSum, demonstrate the effectiveness and superiority of the proposed network to the state-of-the-art methods, and the training is more stable.

In this article, the zonotopic distributed fusion estimation problem is investigated for a class of general nonlinear systems over binary sensor networks subject to unknown-but-bounded (UBB) noises. The network communication from nodes to the fusion center is confined to the limited bit rate. To alleviate the impact from less measurement information of the binary sensor, a modified innovation is constructed to improve the estimation accuracy. Then, a novel coding-decoding approach is proposed to ensure that the decoder has the ability to decode information from each node. Based on the matrix weighting fusion method, a distributed fusion algorithm is put forward under the zonotopic set-membership filtering framework, and the F-radius of the local zonotopic sets are derived and minimized by selecting the filtering gain parameters. Moreover, the bit rate allocation scheme and the weighting coefficients are determined by resolving two optimization problems. In addition, a sufficient condition is established to guarantee the uniform boundedness of the F-radius of the fused zonopotic. Finally, the ballistic object tracking systems is utilized to illustrate the availability of the presented algorithm.

Symmetric, high-dimensional and sparse (SHiDS) networks usually contain rich knowledge regarding various patterns. To adequately extract useful information from SHiDS networks, a novel biased triple factorization-based (TF) symmetric and non-negative latent factor (SNLF) model is put forward by utilizing the transfer learning (TL) method, namely biased TL-incorporated TF-SNLF (BT$^{2}$-SNLF) model. The proposed BT$^{2}$-SNLF model mainly includes the following four ideas: 1) the implicit knowledge of the auxiliary matrix in the ternary rating domain is transferred to the target matrix in the numerical rating domain, facilitating the feature extraction; 2) two linear bias vectors are considered into the objective function to discover the knowledge describing the individual entity-oriented effect; 3) an improved momentum-incorporated additive gradient descent algorithm is developed to speed up the model convergence as well as guarantee the non-negativity of target SHiDS networks; and 4) a rigorous proof is provided to show that, under the assumption that the objective function is$L$-smooth and$\mu$-convex, when$t\geq t_{0}$, the algorithm begins to descend and it can find an$\epsilon$-solution within$O(ln((1+\frac{\mu L}{L(1+\mu )+8\mu })/\epsilon ))$. Experimental results on six datasets from real applications demonstrate the effectiveness of our proposed T$^{2}$-SNLF and BT$^{2}$-SNLF models.

This article is concerned with supplementary control of discrete-time nonlinear systems with multiple controllers in the framework of goal representation heuristic dynamic programming (GrHDP), where a logarithmic quantizer is used to govern the network communication. For the addressed problem, a neural network (NN)-based observer is first proposed to estimate the unknown system state in the simultaneous presence of quantized influence. In light of the estimated states and the ideal control inputs via a zero-sum game, a GrHDP algorithm with a reinforced term is developed to implement the supplementary control task, where some novel weight updating rules are constructed by virtue of an additional tunable parameter to improve the system performance. Furthermore, a set of conditions about the stability of estimated error dynamics of both observer states and updated NNs' weights are derived by resorting to the Lyapunov stability theory. Finally, the effectiveness of the developed method is verified by a power system and a numerical experiment.

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.

Relying on a large amount of data, recent object trackers achieve superior performance. However Siamese-like trackers expose considerable shortcomings in the case of brief occlusion. To address these shortages, the paper proposes a novel pre-frame auxiliary and fusion tracking framework. Within this framework, a retained variable is first introduced to avoid some additional twin branches while retaining the previously obtained deep features of the search frames. Based on such a variable, a pre-frame auxiliary module is constructed to establish the relationship between encoding features and the retained pre-frame information and a decoding fusion module is designed to fuse the generated similarity relationship. Moreover, the Efficient IoU (EIoU) loss is employed to increase the precision of predicted bounding boxes by adding three penalty terms for the differences in the center point, length, and width of the two bounding boxes. Finally, the superiority over state-of-the-art methods is verified by numerous tests on visual tracking benchmarks.

This paper addresses the problem of simultaneous cyber attack estimation and sensor attack detection for connected automated vehicles (CAVs), where the vehicle-to-vehicle communication network suffers from false data injection attacks and vehicular radar sensors experience spoofing attacks. First, a delicate proportional-integral observer (PIO) is developed to estimate the unavailable longitudinal vehicle tracking errors and the injected attack signal in real-time. Second, leveraging a residual signal generated from the PIO, an effective detection mechanism, which involves residual evaluation and thresholding, is designed to identify the radar spoofing attacks. Furthermore, due to the concurrent effects of false data injection attacks and spoofing attacks on the estimation errors, formal stability and performance analysis in terms of robustness against false data injection and sensitivity against spoofing is then performed. Finally, simulation examples are provided to demonstrate the efficacy of the proposed attack estimation and attack detection method.

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 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.

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.

Metaverse allows the physical reality to tightly integrate with the digital universe. As one typical metaverse application, platooning control of vehicular cyber–physical systems has attracted extensive attention as it is beneficial to improve traffic efficiency, driving safety, and emission reduction. However, due to the open nature of wireless communication networks, the transmitted vehicle-to-vehicle (V2V) data packets become exposed to the public and concomitant data leakage can lead to unintended consequences to vehicular platoons. This article is concerned with the privacy-preserving platooning control issue of vehicular cyber–physical systems with input saturations. First, a novel distributed proportional-integral observer is proposed to estimate the full state of each vehicle, where the integral terms with a forgetting factor facilitate to realize the tradeoff between transient performance and steady-state performance for the platoon. Second, sampled-data-based dynamic encryption and decryption schemes, featuring a dynamic private key, are developed such that the encrypted and decrypted V2V data can be kept private to each platoon vehicle. It is then shown that the platooning control problem over a generic communication topology can be cast into the stability issue of an auxiliary dynamic system. Furthermore, sufficient conditions on the existence of the desired observer and controller gains as well as the private key parameter selection are derived to guarantee the desired platoon stability and privacy preservation requirements. Finally, an illustrative example is given to demonstrate the effectiveness of the proposed control method.

This article addresses the proportional-integral (PI)-based security control issue of large-scale systems subject to randomly occurring joint attacks. Specifically, the considered cyber-attacks could happen in both sensor-to-observer and controller-to-actuator, and only partial data of sensors and controllers are randomly tampered with by malicious attacks due to energy limits. For the addressed problem, an observer-based PI controller is constructed by resorting to the compensation of randomly occurring joint attacks, which are modeled by two diagonal matrices combined with a set of stochastic variables. A sufficient condition only dependent on the local system dynamics as well as the local interconnected matrices is derived in the framework of the input-to-state stability (ISS) theory, and the desired gains of both the controller and the observer are obtained by the cone complementarity linearization (CCL) algorithm. Benefiting from the element matrix inequality, the developed design scheme satisfies the scalability requirement. In the end, the simulation test based on IEEE 39-bus power systems is seriously used to demonstrate the validity of the proposed control scheme.

The paper is concerned with the supplementary control based on adaptive dynamic programming (ADP) for a class of discrete-time networked system with the simultaneous presence of dynamic event-triggered mechanisms and Denial-of-Service (DoS) attacks. The dynamic behavior of DoSs is described by a model with the appropriate frequency and durations. A neural network (NN)-based observer is first designed to estimate system states in order to resolve the limitation in ADP-based control due mainly to data sparsity. The performance analysis and gain design of the NN-based observer are systematically discussed in light of the switched system theory combined with the average dwell-time method. Subsequently, the policy iteration algorithm with an actor-critic structure is developed to implement the designed supplementary ADP controller, and the corresponding condition on learning rates in weight updating rules is derived by virtue of the well-known Lyapunov stability. Finally, the effectiveness of the developed approach is demonstrated by an application in load frequency control of power systems.

This article focuses on the distributed maximum correntropy filtering issue for general stochastic nonlinear systems subject to deception attacks. The considered nonlinear functions consist of a determined one and a stochastic one, and the stochastic signals sent by deception attacks with identified statistic characteristics could be non-Gaussian. The corresponding calculation formulas of both the filter gains and the upper bound of the filter error covariance are proposed by means of the Taylor series expansion and the fixed-point iterative update rule, where the weighted maximum correntropy criterion is utilized to take the place of traditional minimum covariance indexes. Such an upper bound is only dependent on the local information, neighbor information, and the identified statistics of deception attacks and, therefore, the developed filtering scheme realizes the requirement of distributed calculation. Furthermore, a simplified version is obtained by removing weights in the correntropy criterion. Finally, an illustrative example is given to verify the effectiveness of developed distributed maximum correntropy filtering subject to deception attacks.

In this article, recursive filtering is investigated for time-varying discrete sequential systems (DSSs) under weighted try-once-discard (WTOD) protocols, which are employed to govern the access authorization of a shared network in order to remit the communication burden. A transmission model, dependent on a Bernoulli distributed white sequence, is developed to describe the phenomenon of deception attacks. In light of the adopted protocol and the attack model, a recursive algorithm with the form of Riccati-like difference equations is developed to optimize the filtering performance in the mean square sense. Furthermore, by resorting to the mathematical induction, the convergence of the proposed recursive algorithm is discussed profoundly. Finally, a simulation example is presented to verify the availability of the designed recursive filter.

This article focuses on the finite-horizonH∞bipartite consensus control problem for a class of discrete time-varying cooperation-competition multiagent systems (DTV-CCMASs) with the round-robin (RR) protocol. The cooperation-competition relationship among agents is characterized by a signed graph, whose edges are with positive or negative connection weights. Specifically, a positive weight corresponds to an allied relationship between two agents and a negative one means an adversary relationship. The data exchange between each agent and its neighbors is orchestrated by an RR protocol, where only one neighboring agent is authorized to transmit the data packet at each time instant, and therefore, the data collision is prevented. This article aims to design a bipartite consensus controller for DTV-CCMASs with the RR protocol such that the predeterminedH∞bipartite consensus is satisfied over a given finite horizon. A sufficient condition is first established to guarantee the desiredH∞bipartite consensus by resorting to the completing square method. With the help of an auxiliary cost combined with the Moore-Penrose pseudoinverse method, a design scheme of the bipartite consensus controller is obtained by solving two coupled backward recursive Riccati difference equations (BRRDEs). Finally, a simulation example is given to verify the effectiveness of the proposed scheme of the bipartite consensus controller.

Cyber-physical systems (CPSs) empower the integration of physical processes and cyber infrastructure with the aid of ubiquitous computation resources and communication capabilities. CPSs have permeated modern society and found extensive applications in a wide variety of areas, including energy, transportation, advanced manufacturing, and medical health. The security of CPSs against cyberattacks has been regarded as a long-standing concern. However, CPSs suffer from extendable vulnerabilities that are beyond classical networked systems due to the tight integration of cyber and physical components. Sophisticated and malicious cyberattacks continue to emerge to adversely impact CPS operation, resulting in performance degradation, service interruption, and system failure. Secure state estimation and control technologies play a vital role in warranting reliable monitoring and operation of safety-critical CPSs. This article provides a review of the state-of-the-art results for secure state estimation and control of CPSs. Specifically, the latest development of secure state estimation is summarized in light of different performance indicators and defense strategies. Then, the recent results on secure control are discussed and classified into three categories: 1) centralized secure control; 2) distributed secure control; and 3) resource-aware secure control. Furthermore, two specific application examples of water supply distribution systems and wide-area power systems are presented to demonstrate the applicability of secure state estimation and control approaches. Finally, several challenging issues are discussed to direct future research.

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.

In this article, the distributed adaptive Kalman filtering is investigated for discrete-time stochastic nonlinear systems with gain perturbation as well as unknown covariance of process noises. For the adopted event-triggered communication scheduling, a distributed Kalman filter with an event timestamp is first constructed to effectively fuse the information from neighbors and itself while guaranteeing the unbiasedness. In light of stochastic analysis, the desired filter gain, achieving the suboptimality of filtering performance, is obtained recursively by solving two optimization issues with the form of Riccati-like difference equations. With the help of the fashionable weighted fusion conception combined with the well-known law of large numbers, a recursive estimation of process noise covariance is derived step by step and consequently suits for online computation. Finally, the effectiveness of the proposed filtering scheme is verified via a “lineland” system model.

In this paper, the distributed state estimation problem is studied for a class of state-saturated power systems subject to Denial-of-Service (DoS) attacks. The randomly occurring DoS attacks is modeled by a series of Bernoulli distributed stochastic variables with known probability distributions. The aim of this paper is to design a distributed estimator ensure, in the presence of both cyber-attacks and state saturations, the desired estimation performance is satisfied. By virtue of some typical matrix inequalities, a tight upper bound of estimation error covariance is derived. The estimation parameters are obtained with the help of the solution of a set of Riccati-like difference equations. The developed recursive algorithm is independent of the global information and thus satisfies the requirements of scalability and online application. Finally, a practical example is developed to verify the validity of the designed estimator.

This paper investigates the finite-horizon H∞containment control issue for a general discrete time-varying linear multiagent systems with multileaders. All followers in such a system are driven into a convex hull spanned by multiple leaders, which can be transformed into a problem of tracking a virtual trajectory generated by these leaders. For this purpose, a local state observer is put forward to estimate the state of each agent itself. Then, the estimated state is transmitted to corresponding neighbors governing by an innovation-based event-triggered scheduling protocol. The purpose of the addressed problem is to design both an event-based distributed controller and a state observer such that a prescribed H∞containment index can be achieved over a given finite horizon. First, with the help of the completing the square method, a sufficient condition is established to ensure the desired H∞containment performance. Then, by resort to a novel nominal energy cost index combined with Moore-Penrose pseudoinverse method, the desired controller and observer parameters are obtained by solving two coupled backward recursive Riccati difference equations. Two positive scalars in proposed nominal energy cost index provide a tradeoff among the controlled tracking errors, the energy of transformed control inputs, and the precision of estimated states. Finally, a simulation example is given to illustrate the usefulness of the proposed theoretical results.

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, a quantized control problem is investigated for a class of networked switched systems under the Round-Robin protocol. A more general switching rule is employed, under which the probability distribution of switching is dependent on both the sojourn time and the system mode. The Round-Robin protocol is adopted to accommodate the limitation of the network bandwidth from the viewpoint of fairness. The quantized measurement signal is sent to the controller as a feedback signal at each sampling instant. The aim of the problem addressed is to design a quantized controller such that, under the Round-Robin protocol, the switched system with the known joint distribution is mean-square stable. Considering the sojourn time and the periodicity of Round-Robin protocol, we present an interval-dependent periodic condition to guarantee the stability of addressed systems and then derive the explicit expressions of the controller for each subsystem by solving a set of matrix inequalities. Finally, a simulation example is given to demonstrate the effectiveness of the controller design method.

The phenomena of unknown correlations are ubiquitously existing in general distributed filtering problems over sensor networks. And the covariance intersection (CI) fusion rule is an effective tool to tackle with this phenomena. During the recent years, the related CI-based Kalman consensus filters (CIKCFs) have been adopted to deal with unknown correlations in sensor networks. However, a systematic stability analysis result for the general CIKCF in the time-varying system setting remains to be established. This paper is written for this purpose. First, a general CIKCF with full features of CI is presented. Accordingly, the conditions for CIKCF to reach consensus with varying weights are investigated. Furthermore, a novel detectability condition, i.e., collectively uniform detectability, is proposed to ensure the error covariances of the CIKCF are uniformly bounded. Based on this condition, the estimation errors are further proven to be exponentially bounded in mean square with the aid of the stochastic stability lemma. Finally, an example is given to validate the effectiveness of the theoretical results.

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.

This paper is concerned with the reliable filtering for discrete-time nonlinear systems with abnormal measurements and probabilistic distributed time-delays. Two binary stochastic sequences are employed to model stochastic occurring nonlinearities and probabilistic distributed time-delays, respectively. The considered abnormal measurements could be outliers or injected data resulting from cyber-attackers. A factitious saturation constraint on innovation is adopted to remove these abnormal measurements in the designed filter. By resorting to the stochastic analysis combined with Lyapunov stability theory, a sufficient condition is proposed to check whether or not the augmented system is bounded in mean square. Furthermore, the desired filter gain depends on the solution of a linear matrix inequality. Finally, an illustrative example is adopted to verify the effectiveness of the developed design 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.

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.

This paper addresses the distributed resilient filtering problem for a class of power systems subject to denial-of-service (DoS) attacks. A novel distributed filter is first constructed to practically reflect the impact from both cyber-attacks and gain perturbations. For all possible occurrence of DoS attacks and gain perturbations, an upper bound of filtering error covariance is derived by resorting to some typical matrix inequalities. Furthermore, the desired filter gain relying on the solution of two Riccati-like difference equations is obtained with the help of the gradient-based approach and the mathematical induction. The developed algorithm with a recursive form is independent of the global information and thus satisfies the requirements of scalability and distributed implementation online. Finally, a benchmark simulation test is exploited to check the usefulness of the designed filter.

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 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.

In this brief, we study the detectability issues in the context of distributed state estimation problems for a class of locally undetectable sensor networks. First, we introduce a novel detectability condition, i.e., weightedly uniform detectability (WUD), which is a sufficient condition to prove that the error covariances of the consensus filtering are uniformly bounded even though the local sensor nodes are undetectable. Different from the existing detectability (or observability) conditions, our condition includes the interacting weights which could further optimize the lower detectability Gramian bound. Hence, a new weights selection method is derived in term of the criterion of WUD. This new rule of selecting weights provides a new framework for distributed state estimation. The advantages of this approach lead to a better performance in estimation without extra computational burden to the filtering process. Finally, an example shows the effectiveness of the proposed method.

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.

In this correspondence paper, we provide a new look at the boundedness problems of error covariance of Kalman filtering. First, by utilizing the mathematical induction technique, a new bound function which is dependent on system parameters is proposed. In this manner, the boundedness problems of the error covariance can be converted to the study of the corresponding uniform bounds of the bound function. Second, based on such a bound function, the dynamic behaviors, monotonicities, and boundedness problems of error covariance are deeply explored. Consequently, a few quantitative results under minimal conditions about the uniform bounds on error covariance are obtained. Finally, examples are given to verify the correctness and effectiveness of our theoretical analyses.

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.

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, 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.

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.