Abstract Designing controllers directly from measurement data has attracted growing attention in recent years, as it avoids the need for accurate system modeling or explicit system identification. This paper focuses on recent advances in data-driven control for linear discrete-time systems with unknown system matrices. For noisy input-state data, an in-depth analysis is provided on several representative approaches, including data-driven control based on Willems et al.’s fundamental lemma, quadratic matrix inequalities, linear fractional transformations for combining prior knowledge with data, and integral quadratic constraints. For noisy input-output data, a concise review is presented on control methods based on quadratic matrix inequalities, along with key insights into their structure and implications. The paper concludes by outlining several challenging problems that merit further investigation in future research.

Abstract This paper is concerned with event-triggered stabilization of a class of nonlinear networked systems. First, a novel event-triggered sampling scheme is proposed based on a monotonically increasing function in the form of the integral of the error norm between the current state and the latest triggered state. It is proven that this scheme ensures the minimum inter-event time to be strictly greater than zero in an explicit expression. Second, a novel Lyapunov functional tailored for the proposed event-triggered scheme is constructed. By applying the looped functional method, a sampling-interval-dependent stabilization criterion is derived. This criterion provides an algorithm to co-design both control gains and event-triggered parameters for a given threshold. Finally, a practical system comprising two identical pendulums is given to demonstrate the effectiveness of the proposed method.

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.

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.

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 paper is concerned with resilience and efficiency of a class of interconnected dynamical networks (IDNs) under limited communication resources and potential cyber-attacks. We first propose a novel framework for synchronization control in IDNs that integrates distributed event-triggered communication with resilience against denial-of-service (DoS) attacks. We start with developing an adaptive event-triggered mechanism (AETM), dependent on the attack parameter, to dynamically adjust threshold parameters for each node based on network conditions and severity of DoS attacks. Then, we design DoS-resilient event-triggered distributed synchronization controllers for each node to achieve the desired synchronization objective, even under sporadic event-based communication and disruptions caused by DoS attacks. Third, we establish co-design criteria to jointly determine the gain matrices of both the AETMs and synchronization controllers. These criteria provide valuable insights into the interplay between communication quality (e.g., triggering frequency and DoS severity) and control quality (e.g., convergence rate). Finally, we validate the effectiveness of the proposed approach through two illustrative examples.

The Internet of Robotic Things (IoRT) merges the capabilities of robotics with the connectivity and computing power of Internet of Things (IoT) technologies, enabling seamless data collection, processing, and exchange. This integration enhances robotic systems with greater intelligence, mobility, and autonomy, unlocking significant potential across various applications, including sustainable agriculture. However, deploying IoRT systems in unpredictable environments poses challenges, such as network instability and hardware failures, which have not been thoroughly explored in the literature. To address these issues, this article introduces Mobility-as-a-Resilience Service (MaaRS), a model that leverages the mobility of active uncrewed aerial vehicles (UAVs), strategically relocating them to critical points of interest in response to potential data collection failures, optimizing resource allocation and enhancing system resilience, particularly in smart farm scenarios. Additionally, a robust multiagent deep deterministic policy gradient (RMADDPG) method is devised to enable efficient task allocation and system recovery in the presence of model uncertainty, observation noise, and reward uncertainty. Extensive simulations demonstrate that the proposed method achieved a significant boost in performance, efficiency, and stability over the state-of-the-art.

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.

This article addresses the containment control problem in multi-unmanned aerial vehicles (multi-UAVs) over communication networks subject to Markov switching topologies and channel fading. Firstly, a decoupling method is skillfully proposed to divide the closed-loop global containment error system into multiple local dynamics, simplifying the design of the distributed self-triggered scheme. Then, a mode-dependent distributed containment controller is designed, leveraging fading depths to mitigate the impact of channel fading. During the containment analysis, a self-triggered auxiliary condition is intelligently derived, forming the basis for the subsequent design of the self-triggered mechanism. Additionally, a novel distributed self-triggered mechanism is designed to significantly reduce the frequency of information exchange in the system while eliminating Zeno behavior. Finally, the effectiveness of the proposed distributed self-triggered containment control is validated through a simulation example.Note to Practitioners—In a multi-UAV system, stable operation primarily depends on information transmission between UAVs. Network-induced constraints will severely undermine system stability and jeopardize coordinated control. Accordingly, studying effective solutions to address network-induced constraints in real-world systems is of practical importance. This paper investigates the containment control problem in a multi-UAV system affected by channel fading, random communication topologies, and self-triggered transmission. By incorporating fading depth in the communication channel, the proposed controller adapts to varying channel conditions, ensuring the UAVs maintain coordination even under poor communication conditions. Additionally, given that the communication topology between UAVs may change due to environmental factors in real-world applications, Markov random switching topologies can be used to model this phenomenon. Limited communication resources may lead to transmission delay or even network congestion due to the large amount of information exchanged between UAVs. Nevertheless, blindly increasing network bandwidth not only imposes high demands on sensors but also raises experimental costs. Therefore, reducing the frequency of information transmission is a commonly used approach. Unlike traditional event-triggered scheme, this paper proposes a distributed self-triggered scheme for addressing containment control problems in multi-UAV systems, eliminating the need for continuous event monitoring. From a practical application perspective, the distributed self-triggered approach does not require additional sensors for event monitoring, further reducing costs. Building on this work, future research should also address the formation control of multi-UAV systems, where multiple UAVs cooperate to form a specific formation through information exchange.

Event-triggered control addresses a communication resource constraint challenge for networked control systems (NCS) but it is vulnerable to denial of service (DoS) attacks. This paper is concerned with the secure event-triggered control problem for NCSs under DoS attacks. The novelty lies in the development of a novel threshold-dependent event-triggered transmission scheme and a co-design approach for the event-triggered scheme and the desired secure controller. First, two threshold concepts, namely the critical event-triggered threshold (CETT) and the secure event-triggered threshold (SETT), are proposed for the event-triggered scheme. More specifically, the CETT is defined by the maximum allowable event-triggered parameter which can guarantee stability of the NCS and the SETT is used to ensure timely transmissions when the NCS subject to DoS-resulted packet dropouts. Second, by virtue of the input delay approach, a comprehensive closed-loop system model is derived for the NCS, which accommodates simultaneously the event-based sporadic transmissions, packet delays, and DoS-resulted packet dropouts. Third, a co-design algorithm is presented to determine both the desired threshold parameter and secure state- and output-feedback secure controllers. Finally, numerical simulations on a CAN-based vehicle lateral control system are provided to demonstrate the efficacy of the proposed control method. Note to Practitioners—This paper was motivated by the secure control design problem of the NCSs subject to both limited communication resources and DoS attacks. Most existing works failed to establish the relation between event-triggered parameter and packet dropouts and thus lead to the difficulties in controller synthesis under DoS attacks. In this paper, we mathematically characterize the relation between event-triggered parameter and the number of DoS-resulted packet dropouts. The secure control and event-triggered communication co-design approach of the networked control systems under malicious attacks is carefully derived. To practitioners, this study provides a secure control design basis of the NCS subject to DoS attacks by analyzing the evolution of event-triggered threshold under DoS-resulted packet dropouts. However, such design method just mitigates the effects caused by DoS attacks but is less of ability to regulate the control actions during DoS attack intervals. In future research, the active and intelligent secure control compensation strategies will be addressed.

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.

This article investigates a comprehensive data-driven event-triggered secure lateral control of autonomous vehicles under actuator attacks. We consider stabilization issues of autonomous vehicles subject to modeling difficulties, limited communication resources, and actuator attacks. The dynamic model decomposition (DMD) from data is exploited to characterize the inherent lateral dynamics model of autonomous vehicles, the event-triggered transmission scheme is utilized to alleviate communication burden for limited bandwidth network, and the sliding mode control scheme is designed to ensure the security of autonomous vehicles under actuator attacks. The stability analysis and the stabilization method as well as its algorithm are presented. The proposed secure control scheme can actively counteract the malicious effects caused by actuator attacks and integrates the advantages of both data-driven modeling and model-based control design. Finally, several comparative case studies show the effectiveness of the proposed secure control scheme.

This article is concerned with distributed adaptive resource allocation over general digraphs with resource-demand constraints. The central aim is to tackle two essential challenges in distributed resource allocation, namely, scalable implementation and anytime feasibility, ensuring continuous satisfaction of constraints. For this purpose, two novel fully distributed optimization algorithms, featuring sum-based and product-based schemes for adaptive gains, are first developed. It is shown that these algorithms offer several advantageous features in terms of fully distributed implementation without global knowledge and algorithm simplicity as well as anytime feasibility guarantees over existing methods. Notably, the incorporation of a double-layer adaptive control law with a damping term into each algorithm prevents the continuous growth of adaptive gains, thus avoiding excessively large system gain values and enhancing practical applicability in real-world scenarios. Furthermore, by constructing appropriate Lyapunov functions, rigorous convergence analysis confirms that both algorithms achieve the optimal resource allocation and global asymptotic convergence. Finally, several simulation case studies are conducted to validate the efficiency of the proposed algorithms.

The distributed energy management (DEM) is of significance for smart grids due to the growing concern over potential cyber threats. This study delves into a multiobjective DEM problem that encompasses economic and environmental costs, as well as transmission losses, while also considering the impact of persistent Denial-of-Service (DoS) attacks. To tackle this challenge, a novel distributed optimization algorithm over a digraph is proposed, leveraging a zeroth-order scheme to handle unavailable gradients and incorporating momentum terms to provide accurate descent directions. The theoretical analysis demonstrates the algorithm's ability to achieve a linear convergence rate while ensuring real-time maintenance of decision variables within the feasible domain. Building on this, a novel reputation-based resilient DEM framework is introduced to address scenarios involving persistent DoS attacks. This framework calculates a reputation index for each communication link to monitor its reliability. Considering the impact of attacked links on network connectivity and their reputation indexes, corresponding strategies are devised. Specifically, if an attacked link disrupts network connectivity and its reputation index falls below the threshold, a connectivity restoration optimization algorithm is activated to reconstruct links, minimizing communication costs and alleviating information congestion. Finally, the effectiveness of the proposed algorithm and framework is validated through numerical simulations.

This article extends the problem of safety of the intended functionality (SOTIF) to the platooning of connected autonomous vehicles (CAVs). In such multivehicle systems, SOTIF objectives are particularly challenging, since new issues arise due to the presence of interactions that propagate and intensify the effects and the risk connected to faults/failures/uncertainties, triggered by any platoon member and external conditions. To tackle and solve the SOTIF-oriented platoon control problem, we propose a robust and resilient decentralized control protocol, based on distributed observer which exploits the information shared via the Vehicle-to-Everything (V2X) communication network. The approach allows maintaining acceptable SOTIF performance in adversarial conditions, mitigating the effects of the unreasonable risks originated from sensors/actuators performance limitations/faults and disturbances. The uniformly ultimate bounded (UUB) stability and the input-to-state string stability (ISSS) of the platoon dynamics are analytically proved via the Lyapunov theory. Feasible linear matrix inequalities (LMIs) are obtained as stability conditions, whose solution allows adapting the distributed control/observer gains according to leader-tracking performance and robustness margins. A detailed analysis via the vehicular co-simulation platform mixed traffic simulator (MiTraS) corroborates the theoretical derivations and shows the achievement of the SOTIF platoon goals for a set of nontrivial and uncertain driving scenarios.

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.

This paper is concerned with a resilient load frequency control (LFC) of multi-area power systems subject to unknown load disturbances and intermittent denial-of-service (DoS) attacks. The central aim is to develop a feasible and less conservative DoS-resilient LFC scheme that constrains jammer’s actions as less as possible and explores available attack information as much as possible in desired analysis and design criteria. Towards this aim, we first present a partially known DoS model that bounds merely the DoS-on durations. We then elaborate a sampled-data-based transmission paradigm which characterizes explicitly the uniform sampling instants and intermittent DoS-on and -off instants as well as the nonuniform arriving instants of the transmitted system data. Furthermore, we develop a novel attack-parameter-dependent Lyapunov functional to establish less conservative conditions for both stability analysis and controller design. It is shown that the exponential stability of the resultant closed-loop LFC system can be guaranteed, while also preserving both the desired minimum sleeping rate of DoS attacks and the prescribedH∞performance index against changing load disturbances. Finally, several case studies of a three-area power system are provided to verify the effectiveness and superiority of the derived theoretical results.

This article is concerned with the secure fully distributed event-triggered consensus problem of general linear multiagent systems subject to distributed sequential scaling (DSS) attacks. First, a generic DSS attack model is proposed, which enables different attack strategies in terms of scaling factors, attack frequency, and duration to be incorporated in various communication channels. Different from existing attack models, the DSS attack is a kind of scaling attacks in which attack sequences are characterized by the sequential attacks and with distributed attack strategies for different attack objectives. To resist the adverse effects of such DSS attacks and further reduce the unnecessary communication consumption of each communication channel, a channel-based dynamic event-triggered mechanism is next presented. Moreover, a fully distributed event-triggered consensus control protocol is developed such that the dependence of any global information of the network topology can be eliminated. Formal analysis criteria on the asymptotic convergence of the resultant consensus errors and Zeno-freeness are then derived, where the relationship of the triggering parameters, distributed scaling factors, and attack constraints is explicitly expressed. Furthermore, an offline algorithm without any global information is provided to determine both the adaptive consensus protocol gain matrices and the triggering parameters. Therefore, the parameters calculated by this algorithm are applicable to multiagent systems of different scales, which also confirms the flexibility and scalability of the proposed fully distributed event-triggered consensus control protocol. Finally, two simulation examples involving different scales of intelligent vehicles are given to validate the efficacy of the obtained theoretical results.

This article is concerned with the leader–follower formation control problem of multiple networked mobile robots (MRs), where the information exchanges over communication networks among the MRs suffer from replay attacks. The central aim is to develop an effective secure control scheme for the multiple-MR system such that the desired leader–follower formation control objectives are accomplished regardless of the simultaneous presence of replay attacks, network-induced delays, system uncertainties, and external disturbances. Toward this aim, an extended state observer is first constructed to estimate the unknown nonlinear terms consisting of system uncertainties and external disturbances. Then, leveraging the time-stamp technique, a dedicated data packet analyzer is developed for each MR to detect and handle replay attacks. Furthermore, a secure leader–follower formation control scheme, consisting of a network-based cooperative kinematic control law and two local kinetic control laws, is designed. Finally, both simulation and experiment results are provided to validate the efficacy of the proposed secure leader–follower formation control scheme.

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 is concerned with the event-triggered output feedback cluster consensus of leader-following multi-agent systems (MASs) under limited communication resources. Specifically, the distributed agents are divided into several clusters to accomplish different collective tasks under diverse intracluster and intercluster communications. First, to alleviate excessive communication resource consumption, two sampled-data-based event-triggered schemes are developed to distinguish agent-to-agent communications within clusters and between clusters. Based on these schemes, an event-based cluster consensus control protocol is proposed to solve the problem. Then, sufficient criteria on asymptotic stability of the resulting closed-loop system are derived and expressed in terms of matrix inequalities. It is noteworthy that the derived criteria for controller design are nonlinear and nonconvex with respect to the output feedback control gains and triggering parameters. To handle this issue, a modified genetic algorithm (MGA) with multiple subpopulations is proposed, where the subpopulations are independent of each other. The key feature of the designed MGA lies in that the fitness value is described as an accumulation of initial value and weighing value of each matrix inequality. Finally, an application of satellite formation flying is exemplified to demonstrate the effectiveness of the derived theoretical results.

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.

Cyber security of modern power systems has become increasingly significant due to their open communication architecture and expanding network connectivity exposed to malicious cyber attacks. Resilient control represents an effective means to preserve the survivability of the power system under cyber attacks. In this paper, we address the resilient load frequency control (LFC) design for multi-area power systems under a new class of time-constrained denial-of-service (DoS) attacks. First, different from the widely-explored duration- and frequency-constrained DoS attack models, we consider a general time-constrained DoS attack model where only the attack durations are confined into some bounds, which represents less a priori knowledge of an attacker’s actions. Second, instead of using a traditional yet conservative time-invariant Lyapunov function (TILF), we develop an attack-parameter-dependent time-varying Lyapunov function (TVLF) approach to enable a resilient LFC design without jeopardizing the desired closed-loop system stability and performance. Furthermore, we provide a formal stability and performance analysis condition as well as a design criterion for the desired DoS-resilient output feedback LFC controller. We also show that the minimum allowable sleeping period and the maximum allowable active period of the attacked LFC system can be explicitly disclosed. Finally, we present two simulation case studies on a two-area LFC system and a three-area LFC system to demonstrate the effectiveness of the obtained results.

Fixed-time and prescribed-time consensus control can bring an explicit estimate of the settling time without dependence on initial conditions, which is important in providing control engineersa priorisystem information. This article aims at presenting a survey of recent trends and methodologies of fixed-time and prescribed-time consensus control in multiagent systems. First, some typical fixed-time consensus results are reviewed. Despite the advantage in deriving a fixed settling time bound, fixed-time consensus controllers usually result in a conservative estimate of the bound and a large magnitude of initial control input, which in turn show the necessity of designing prescribed-time consensus controllers. Second, characteristics and controller design of (practical, respectively) prescribed-time consensus are provided in detail. Particularly, representative time-varying function-based controllers are presented, by which (practical, respectively) consensus can be achieved in prescribed time. Third, applications of fixed-time and prescribed-time consensus control in mobile robots and smart grids are illustrated in case studies. Finally, several challenging issues in prescribed-time consensus control are discussed for future research.

This article is concerned with the extended dissipativity of discrete-time neural networks (NNs) with time-varying delay. First, the necessary and sufficient condition on matrix-valued polynomial inequalities reported recently is extended to a general case, where the variable of the polynomial does not need to start from zero. Second, a novel Lyapunov functional with a delay-dependent Lyapunov matrix is constructed by taking into consideration more information on nonlinear activation functions. By employing the Lyapunov functional method, a novel delay and its variation-dependent criterion are obtained to investigate the effects of the time-varying delay and its variation rate on several performances, such as$H_\infty $performance, passivity, and$l_{2}-l_\infty $performance, of a delayed discrete-time NN in a unified framework. Finally, a numerical example is given to show that the proposed criterion outperforms some existing ones.

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 paper is concerned with the secure event-triggered distributed cooperative longitudinal control problem of virtually coupled high-speed trains (VCHSTs) subject to DoS attacks. First, a DoS-aware dynamic event-triggered transmission mechanism (DETM) is proposed to reduce the frequency of train data transmissions over train-to-train (T2T) information flow channels. Via actively prolonging the inter-event times, the resilience of the developed DETM to DoS attacks can be greatly improved. Second, based on the intermittently arrived T2T data, a distributed event-based secure cooperative control protocol is proposed for each train in the convoy. Third, a sufficient condition is derived to guarantee the asymptotic stability of the train tracking error system under the prescribed H∞performance. Furthermore, a co-design method for solving out the desired train controllers and the triggering conditions is developed. Finally, the efficacy of the derived theoretical results is verified through a case study of a realistic railway line.

This paper addresses a secure event-triggered consensus control problem for a class of nonlinear leader-following multi-agent systems (MASs) under denial of service (DoS) attacks and deception attacks. The novelty of this study lies in the development of a secure event-triggered impulsive control (ETIC) method that can effectively deal with the effects of random DoS and deception attacks, while also achieving the desired resource-efficient consensus control performance. More specifically, in order to save the previous communication resources, an event-triggered impulsive consensus control protocol is proposed to reduce data exchanges among the agents. Our further aim is to simultaneously guarantee the mean-square exponential consensus performance of the controlled MAS and achieve attack resilience as well as resource efficiency. Sufficient conditions on the consensus performance analysis are derived, and the lower bound of impulsive sequences is further specified to exclude the Zeno behavior. Finally, a numerical example involving a group of Chua’s circuits is given to illustrate the effectiveness of the derived theoretical results.

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 addresses the problem of dynamic event-triggered platooning control of automated vehicles over a vehicular ad-hoc network (VANET) subject to random vehicle-to-vehicle communication topologies. First, a novel dynamic event-triggered mechanism is developed to determine whether or not the sampled data packets of each vehicle should be released into the VANET for intervehicle cooperation. More specifically, the threshold parameter in the triggering condition is dynamically adjusted over time according to the vehicular data variations, the dynamic threshold updating laws, and the bandwidth occupancy indication. Second, a unified platooning control framework is established to account for various spacing policies, randomly switching communication topologies, unknown leader control input, and external disturbances. Then, a new scheduling and platooning control co-design approach is presented such that the controlled vehicular platoon can successfully track the leader vehicle under random communication topologies and different spacing policies, including constant spacing, constant time headway spacing, and variable time headway spacing, meanwhile maintaining efficient bandwidth-aware resource management. Finally, comparative studies are provided to substantiate the effectiveness and merits of the proposed co-design approach.

This article deals with the problem of secure distributed adaptive platooning control of automated vehicles over vehicular ad-hoc networks (VANETs) in the presence of intermittent denial-of-service (DoS) attacks. The platoon, which is wirelessly connected via directed vehicle-to-vehicle (V2V) communication, is composed of a group of following vehicles subject to unknown heterogeneous nonlinearities and external disturbance inputs, and a leading vehicle subject to unknown nonlinearity and external disturbance as well as an unknown control input. Under such a platoon setting, this article aims to accomplish secure distributed platoon formation tracking with the desired longitudinal spacing and the same velocities and accelerations guided by the leader regardless of the simultaneous presence of nonlinearities, uncertainties, and DoS attacks. First, a new logical data packet processor is developed on each vehicle to identify the intermittent DoS attacks via verifying the time-stamps of the received data packets. Then, a scalable distributed neural-network-based adaptive control design approach is proposed to achieve secure platooning control. It is proved that under the established design procedure, the vehicle state estimation errors and platoon tracking errors can be regulated to reside in small neighborhoods around zero. Finally, comparative simulation studies are provided to substantiate the effectiveness and merits of the proposed control design approach on maintaining the desired platooning performance and attack tolerance.

Multiagent systems (MASs) provide an effective means for coordinating spatially distributed and networked agents (or nodes, subsystems) such that the desired cooperative tasks can be accomplished with promising reliability, manipulability, scalability, and efficiency. One key issue in the study of MASs is the design of distributed cooperative control protocol and algorithm that depend on only local and real-time information exchanges among interacting agents over networks. However, network-enabled information sharing and increasing connectivity in practical MASs present several attack factors for malicious adversaries, thereby rendering secure control of MASs fundamentally significant. This article provides a brief survey of systems and control technologies that have been available for addressing different secure control problems of MASs in the face of various malicious attacks. First, attacks on MASs are classified based on different configuration layers. Then, the existing attack models and strategies on communication layer and agent layer are systematically examined, respectively. Furthermore, some typical secure control techniques for MASs that have been employed to handle these attacks are surveyed. Finally, several challenging issues are envisioned for potential future research.

This article considers the distributed adaptive fault-tolerant control problem for heterogeneous linear multiagent systems with actuator faults and nonuniform time-varying communication delays. First, novel distributed switching observers are proposed to estimate the system matrix and the state of the exosystem. The observers allow each agent to share its information with its underlying neighbors only at sampled instants of time, thus making a distinct difference from the existing results that require the exosystem matrix to be accessible to all agents at every continuous instant of time. Second, a sufficient condition is derived such that the observer error systems are exponentially stable under the influence of communication delays among interacting agents. Third, new distributed adaptive fault-tolerant controllers, which promise fewer adaptive parameters, are presented to compensate for the actuator faults. It is shown that the developed controllers are capable to effectively and efficiently solve the cooperative fault-tolerant output regulation problem with reduced computational complexity. Finally, three illustrative examples, including an inverted pendulum system, an electronic double-integrator circuit system, and an AC microgrid system, are presented to show the validity and efficiency of the developed method.

This article is concerned with the problem of distributed prescribed finite-time observer design for a strictfeedback nonlinear system with external disturbance. The purpose is to reconstruct the unavailable system state based on a group of distributed observers, where each of them can only receive at most 1-D output measurement from the system. First, in the absence of disturbance, a new distributed prescribed finite-time observer featuring time-varying gains is constructed and designed under the assumption of joint observability. It is analytically proved that for any prescribed instant independent of system initial conditions and other design parameters, the obtained distributed observer can guarantee not only the asymptotic convergence to zero of the state error between each observer state and the system state at this prescribed instant but also the definite zero-state error after this prescribed instant. Second, a distributed prescribed finite-time bounded observer is delicately proposed to account for the presence of external disturbance in the system dynamics. It is shown that the state error can be bounded by an arbitrarily positive constant after a prescribed instant. Finally, a numerical example and an electromechanical system are presented to demonstrate the effectiveness of the proposed results.

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.

This article is concerned with the resilient tracking control of a networked control system under cyber attacks. The attacker is an active adversary whose aim is to severely degrade the tracking performance of the system by launching deception attacks on the sensor-to-controller communication channels and denial-of-service attacks on the controller-to-plant channels, respectively. First, a concept of resilient set-membership tracking control is presented, through which the system's true state is guaranteed to reside in a bounding ellipsoidal set of the reference state regardless of the existence of attacks and unknown-but-bounded (UBB) noises. Second, in the case that full information of the system's state is not implicitly trusted in the presence of attacks, a resilient set-membership estimation strategy is provided to secure the state estimates against the deception attacks. Furthermore, based on a recursive computation of a reference state ellipsoid and confidence state estimation ellipsoids, a convex optimization algorithm in terms of recursive linear matrix inequalities is proposed to obtain the gain parameters for both the desired resilient state estimator and the tracking controller. Finally, the effectiveness of the proposed method is illustrated through an Internet-based three-tank system.

This article is concerned with the distributed resilient estimation of a positive system over a sensor network. First, a heterogeneous sensor interaction framework, where each sensor is capable of sharing its local information of measurement as well as state estimate with its underlying neighbors via distinct interaction topologies, is proposed to account for different sensor communication capacities. During the information exchanges among the sensors, topological attacks are suitably modeled in such a way to incorporate the random and intermittent disruption of the heterogeneous sensor interaction topologies. Second, two sets of distributed resilient estimators are delicately constructed to cope with the resulting random denial of information exchanges within the specific repaired periods and compromised periods caused by the topological attacks. Third, the resilience performance analysis with a prescribedl1-gain attenuation level is carried out, and a linear programming approach is then developed to achieve the design of the desired distributed estimators. Finally, the effectiveness of the proposed design method is verified through a vehicle formation monitoring system.

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.

This paper is concerned with the event-triggered control problem of vehicle active suspension systems subject to uncertain actuator faults. Firstly, networked modeling of the vehicle suspension control system is presented and a dynamic event-triggered communication mechanism is developed to reduce some unnecessary data transmissions between networked sensor and controller, thus enabling efficient usage of the limited communication resources for the in-vehicle network. Different from most existing event-triggered mechanisms, the threshold parameter of the proposed triggering condition is adaptively regulated in accord with a dynamic rule. Secondly, by Lyapunov functional method, a co-design criterion of the desired event-triggering parameter and H∞controller gain is derived so as to guarantee the asymptotic stability of the resulting closed-loop system and preserve several performance requirements of the suspension system, including ride comfort, road holding and suspension deflection/stroke limitation. Finally, simulation results are provided to show the effectiveness of the proposed method.

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.

An event-triggered mechanism is of great efficiency in reducing unnecessary sensor samplings/transmissions and, thus, resource consumption such as sensor power and network bandwidth, which makes distributed event-triggered estimation a promising resource-aware solution for sensor network-based monitoring systems. This paper provides a survey of recent advances in distributed event-triggered estimation for dynamical systems operating over resource-constrained sensor networks. Local estimates of an unavailable state signal are calculated in a distributed and collaborative fashion based on only invoked sensor data. First, several fundamental issues associated with the design of distributed estimators are discussed in detail, such as estimator structures, communication constraints, and design methods. Second, an emphasis is laid on recent developments of distributed event-triggered estimation that has received considerable attention in the past few years. Then, the principle of an event-triggered mechanism is outlined and recent results in this subject are sorted out in accordance with different event-triggering conditions. Third, applications of distributed event-triggered estimation in practical sensor network-based monitoring systems including distributed grid-connected generation systems and target tracking systems are provided. Finally, several challenging issues worthy of further research are envisioned.

This paper is concerned with secure 11-gain performance analysis and distributed finite-time filter design for a positive discrete-time linear system over a sensor network in the presence of deception attacks. A group of intercommunicating sensors is densely deployed to measure, gather, and process the output of the positive system. Each sensor is capable of sharing its measurement with its neighboring sensors in accordance with a prescribed network topology while suffering from random communication link failure. Meanwhile, the aggregated measurement on each sensor during network transmission is corrupted by stochastic deception attacks which compromise the sensor's measurement integrity. First, a unified sensor measurement transmission model is put forward to account for the simultaneous presence of deception attacks and various network-induced constraints. Second, delicate secure distributed filters are constructed by admitting the corrupted sensor measurement. Third, theoretical analysis on finite-time 11-gain boundedness of the filtering error system and design of desired positive filters are carried out. The solution to the filter gain parameters is characterized by a set of linear programming inequalities. Finally, the effectiveness of the obtained results is verified through the secure monitoring of power distribution in the smart grid.

This article is concerned with designing resilient state feedback controllers for a class of networked control systems under denial-of-service (DoS) attacks. The sensor samples system states periodically. The DoS attacks usually prevent those sampled signals from being transmitted through a communication network. A logic processor embedded in the controller is introduced to not only receive sampled signals but also capture information on the duration time of each DoS attack. Note that the duration time of DoS attacks is usually both lower and upper bounded. Then the closed-loop system is modeled as an aperiodic sampled-data system closely related to both lower and upper bounds of duration time of DoS attacks. By introducing a novel looped functional, which caters for the N -order canonical Bessel-Legendre inequalities, some N -dependent stability criteria are presented for the resultant closed-loop system. It is worth pointing out that a number of identity formulas are uncovered, which enable us to apply the notable free-weighting matrix approach to derive less conservative stability criteria. A linear-matrix-inequality-based criterion is provided to design stabilizing state-feedback controllers against DoS attacks. A satellite control system is given to demonstrate the effectiveness of the proposed method.

This paper is concerned with passivity of a class of delayed neural networks. In order to derive less conservative passivity criteria, two Lyapunov-Krasovskii functionals (LKFs) with delay-dependent matrices are introduced by taking into consideration a second-order Bessel-Legendre inequality. In one LKF, the system state vector is coupled with those vectors inherited from the second-order Bessel-Legendre inequality through delay-dependent matrices, while no such coupling of them exists in the other LKF. These two LKFs are referred to as the coupled LKF and the noncoupled LKF, respectively. A number of delay-dependent passivity criteria are derived by employing a convex approach and a nonconvex approach to deal with the square of the time-varying delay appearing in the derivative of the LKF. Through numerical simulation, it is found that: 1) the coupled LKF is more beneficial than the noncoupled LKF for reducing the conservatism of the obtained passivity criteria and 2) the passivity criteria using the convex approach can deliver larger delay upper bounds than those using the nonconvex approach.

Fault-tolerant cooperative control of multiagent systems has attracted ever-increasing attention in recent years due to the fact that multiple agents can provide much more redundancy than a single agent system, thereby making the fault tolerant cooperative control design more flexible. However, multiagent systems may bring severe challenges that do not exist in single-agent systems. This article aims at presenting a survey of trends and methodologies of fault tolerant cooperative control in multiagent systems. Depending on the countermeasure against the faults, the existing fault-tolerant cooperative control methodologies are first classified into four categories: Individual methodologies, cooperative methodologies, topology reconfiguration-based methodologies, and composition reconfiguration-based methodologies. Then the characteristics and implementation schemes of four categories of methodologies are discussed in detail. Furthermore, the applicability of fault tolerant cooperative control in smart grids is outlined. Finally, several challenging issues are envisioned for future research.

This paper is concerned with the distributed attack detection and recovery in a vehicle platooning control system, wherein inter-vehicle information is propagated via a wireless communication network. An active adversary may launch malicious cyber attacks to compromise both sensor measurements and control command data due to the openness of the wireless communication. First, a distributed attack detection algorithm is developed to identify any of those attacks. The core of the algorithm lies in that each designed filter can provide two ellipsoidal sets: a state prediction set and a state estimation set. Whether a filter can detect the occurrence of such an attack is determined by the existence of intersection between these two sets. Second, two recovery mechanisms are put forward, through which the adversarial effects of cyber attacks can be mitigated in a timely manner. The recovery mechanisms depend on reliable modifications of the attacked signals required for the computation of the two ellipsoidal sets. Finally, simulation is provided to validate the effectiveness of the proposed method in both detection and recovery phases.

Distributed coordination control is the current trend in networked systems and finds prosperous applications across a variety of fields, such as smart grids and intelligent transportation systems. One fundamental issue in coordinating and controlling a large group of distributed and networked agents is the influence of intermittent interagent interactions caused by constrained communication resources. Event-triggered communication scheduling stands out as a promising enabler to strike a balance between the desired control performance and the satisfactory resource efficiency. What distinguishes dynamic event-triggered scheduling from traditional static event-triggered scheduling is that the triggering mechanism can be dynamically adjusted over time in accordance with both available system information and additional dynamic variables. This article provides an up-to-date overview of dynamic event-triggered distributed coordination control. The motivation of dynamic event-triggered scheduling is first introduced in the context of distributed coordination control. Then some techniques of dynamic event-triggered distributed coordination control are discussed in detail. Implementation and design issues are well addressed. Furthermore, this article exemplifies two applications of dynamic event-triggered distributed coordination control in the fields of microgrids and automated vehicles. Several challenges are suggested to direct the future research.

With the rapid development and widespread utilization of advanced communication, sensing, and computation technologies, increasing attention has been paid to develop new techniques of control and filtering for distributed networked systems. Note that the utilization of communication networks can improve efficiency, flexibility, and scalability in designing networked controllers and filters. However, communication networks usually suffer from communication resource constraints with detrimental consequences, such as long latency, increased packet dropout, reduced throughput, and so on. In order to address the challenges caused by limited communication resources, an event-triggered communication paradigm is an effective and promising technique that can significantly reduce computation/communication utilization while maintaining the desired estimation and the control performance. As a result, a variety of event-triggered control and filtering techniques for distributed networked systems have been proposed.

This paper studies an attack detection problem for a networked leader-following multi-agent system subject to unknown-but-bounded system noises and quantization effects, where an adversary launches malicious cyber attacks on agents' measurement outputs aiming to distrust the leader-following consensus. An effective distributed attack detection algorithm is firstly developed for each follower such that the attack can be identified at the time of its occurrence. The core of the algorithm lies in a set-membership filtering approach from which each designed filter can provide an ellipsoidal state prediction set and an ellipsoidal state estimation set. Whether a filter can detect the occurrence of such an attack is then determined by the existence of intersection between these two sets. Furthermore, a convex optimization algorithm is established to solve out anticipated consensus protocol and two-step set-membership filter by resorting to some recursive linear matrix inequalities. Finally, an illustrative example is given to show the effectiveness of the proposed main results.

This paper is concerned with the distributed target tracking for a moving target of discrete time-varying nonlinear dynamics over a wireless sensor network. A number of spatially distributed sensors are deployed to measure the state of the target, calculate local state predictions as well as local state estimates, and further exchange local information with their underlying neighboring sensors. Due to adversarial attacks, a subset of sensors are deliberately manipulated and thus misbehaving. Accordingly, information exchanges from the misbehaving sensors to their neighbors become antagonistic rather than cooperative as in normal operation. First, a novel distributed target tracking scheme in terms of local state predictors and state estimators is developed for each sensor over a partially misbehaving sensor network. Second, criteria for designing the desired distributed target tracking scheme and the time-varying adjacency matrix are derived such that two ellipsoidal prediction and estimation sets can be recursively computed. It is analytically proved that these two sets guarantee the containment of the true target state at every instant of time regardless of misbehaving sensors as well as unknown-but-bounded process and measurement noises. Third, based on the proposed design criteria, optimization methods are put forward to minimize the calculated ellipsoids. Finally, an application to vehicle tracking is given to show the effectiveness of the results.

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

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

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.

Event-triggered consensus of multiagent systems (MASs) has attracted tremendous attention from both theoretical and practical perspectives due to the fact that it enables all agents eventually to reach an agreement upon a common quantity of interest while significantly alleviating utilization of communication and computation resources. This paper aims to provide an overview of recent advances in event-triggered consensus of MASs. First, a basic framework of multiagent event-triggered operational mechanisms is established. Second, representative results and methodologies reported in the literature are reviewed and some in-depth analysis is made on several event-triggered schemes, including event-based sampling schemes, model-based event-triggered schemes, sampled-data-based event-triggered schemes, and self-triggered sampling schemes. Third, two examples are outlined to show applicability of event-triggered consensus in power sharing of microgrids and formation control of multirobot systems, respectively. Finally, some challenging issues on event-triggered consensus are proposed for future research.

This paper is concerned with the problem of robust ${H}_{\infty}$ control of an uncertain discrete-time Takagi-Sugeno fuzzy system with an interval-like time-varying delay. A novel finite-sum inequality-based method is proposed to provide a tighter estimation on the forward difference of certain Lyapunov functional, leading to a less conservative result. First, an auxiliary vector function is used to establish two finite-sum inequalities, which can produce tighter bounds for the finite-sum terms appearing in the forward difference of the Lyapunov functional. Second, a matrix-based quadratic convex approach is employed to equivalently convert the original matrix inequality including a quadratic polynomial on the time-varying delay into two boundary matrix inequalities, which delivers a less conservative bounded real lemma (BRL) for the resultant closed-loop system. Third, based on the BRL, a novel sufficient condition on the existence of suitable robust ${H}_{\infty}$ fuzzy controllers is derived. Finally, two numerical examples and a computer-simulated truck-trailer system are provided to show the effectiveness of the obtained results.

Fixed-time cooperative control is currently a hot research topic in multiagent systems since it can provide a guaranteed settling time, which does not depend on initial conditions. Compared with asymptotic cooperative control algorithms, fixed-time cooperative control algorithms can achieve better closed-loop performance and disturbance rejection properties. Different from finite-time control, fixed-time cooperative control produces the faster rate of convergence and provides an explicit estimation of the settling time independent of initial conditions, which is desirable for multiagent systems. This paper aims at presenting an overview of recent advances in fixed-time cooperative control of multiagent systems. Some fundamental concepts about finite- and fixed-time stability and stabilization are first recalled with insight understanding. Then, recent results in finite- and fixed-time cooperative control are reviewed in detail and categorized according to different agent dynamics. Finally, this paper raises several challenging issues that need to be addressed in the near future.

This study addresses an H ∞ filtering problem for distributed Markovian jump systems (DMJSs) in multi‐sensor networks with inaccessible global jumping modes, random data losses, random sensing topologies and incomplete mode transition rates (TRs). First, locally overlapped clusters are introduced to reformulate the DMJS to account for the scenario that the global modes of the DMJS are not accessible for filter design. Second, a new cluster and local mode‐dependent H ∞ filtering framework is presented to incorporate the simultaneous presence of Markovian data losses and sensing topologies as well as incomplete mode TRs. The proposed H ∞ filters depend on only available information of cluster modes and local modes within this cluster, and thus eliminating the requirement of complete global modes. Third, criteria for designing desired filters are derived to preserve the stochastic stability of the resulting filtering error system under a prescribed H ∞ performance index. The proposed results are shown to be more general by covering some existing results as special cases. Finally, an F404 aircraft engine system is employed to demonstrate the effectiveness of the proposed filter design approach.

This paper addresses the consensus problem for a continuous-time multiagent system (MAS) with Markovian network topologies and external disturbance. Different from some existing results, global jumping modes of the Markovian network topologies are not required to be completely available for consensus protocol design. A network topology mode regulator (NTMR) is first developed to decompose unavailable global modes into several overlapping groups, where overlapping groups refer to the scenario that there exist commonly shared local modes between any two distinct groups. The NTMR schedules which group modes each agent may access at every time step. Then a new group mode-dependent distributed consensus protocol on the basis of relative measurement outputs of neighboring agents is delicately constructed. In this sense, the proposed consensus protocol relies only on group and partial modes and eliminates the need for complete knowledge of global modes. Sufficient conditions on the existence of desired distributed consensus protocols are derived to ensure consensus of the MAS with a prescribed $H_{\infty }$ performance level. Two examples are provided to show the effectiveness of the proposed consensus protocol.

This paper addresses an event-based consensus problem for a continuous-time nonlinear multi-agent system subject to communication resource constraints. The agents' states are measured by a multi-sensor network, in which sensor nodes are dispersedly deployed in a sensor field. Each sensor node can gather information from its neighboring nodes, process aggregated information by a prescribed network topology, sample processed information at a synchronous sampling period and then transmit sampled information to a remote controller node via a shared network medium. First, to reduce the frequency of transmitting sensors' sampled-data, an event-based communication strategy is presented to schedule sensors' data transmission. Second, based on transmitted aggregated measurement, a delicate event-based consensus protocol is proposed. Compared with some existing event-based consensus protocols based on transmitted local measurement, the proposed protocol offers some advantages. Third, a criterion for designing desired event-based consensus protocol and event triggering strategy is provided such that the proposed event-based consensus is achieved. It is shown that the consensus protocol gain and the event strategy threshold parameter can be jointly determined by solving a Riccati inequality. Finally, a vertical taking-off and landing aircraft model is employed to show the effectiveness of the proposed result.

Consensus problems of multi-agent systems have attracted an ever-increasing interest in the control community due to their great potential in various applications such as cooperative unmanned air vehicles, automated highway systems scheduling, air traffic control, sensor networks. This paper presents a brief overview of theoretical development of consensus in a sampled-data setting, paying special attention to those published since 2011. Recent results in this area are categorized into several directions, such as consensus based on periodic sampling, variable sampling, stochastic sampling, and event-triggered communication. In particular, for event-triggered consensus, some typical patterns of event-triggering conditions are reviewed based on different definitions of a transmission error and a threshold.

This paper is concerned with distributed event-triggered H∞filtering for a discrete-time linear system over a sensor network. Sensor nodes are physically distributed and communicated through network medium. Firstly, a distributed event-triggered communication scheme is presented to determine when the measurement output on each sensor node should be broadcast and transmitted to its neighboring nodes. Each sensor node is equipped with an event monitor which consists of an event generator and a store. The event generator is configured to produce a series of events according to an event triggering rule. The store is employed to reserve the latest data packets. Secondly, based on the Lyapunov functional approach, criteria for analyzing H∞filtering performance and designing desired distributed event-triggered H∞filters are derived such that the resultant filtering error system is asymptotically stable with a prescribed H∞performance index. The filter design problem is posed in terms of linear matrix inequalities. Finally, an illustrative example is given to demonstrate the effectiveness of the obtained theoretical results.

This paper is concerned with the problem of designing distributed event-triggered H∞filters over sensor networks subject to heterogeneous coupling intercommunication delays. A new distributed event-triggered scheme is proposed to determine whether or not each sensor's current sampled data should be broadcasted and transmitted to its underlying neighboring nodes through the communication network. In this scheme, each sensor node is able to make its own decisions to broadcast and transmit only when its local measurement output error exceeds a designed threshold. Heterogeneous coupling delays are incorporated in the intercommunication between the specific sensor node and its interacting neighbors. A refined technique is proposed to realize the complicated decoupling among the exchanged measurement outputs in the presence of coupling intercommunication delays. Then the resulting filter error system is modeled by a new delay system subject to finite time-varying “state” delays. Based on the Lyapunov-Krasovskii functional method, a sufficient condition for distributed event-triggered H∞filter design is established, from which the desired filter parameters and the triggering parameter in the event condition can be co-designed. The filter design problem is posed in terms of linear matrix inequalities. A quarter-car suspension model is finally presented to show the effectiveness and feasibility of the developed theoretical results.