This paper investigates resilient tracking control for multi-agent systems subject to multi-channel asynchronous denial-of-service (DoS) attacks within a directed graph. We consider both homogeneous dynamics and uncertain heterogeneous dynamics in our analysis. We first propose a distributed resilient tracking control strategy against multi-channel asynchronous DoS attacks for homogeneous systems. The concept of equivalent decay-rates across different attacked channels is introduced to derive sufficient conditions for secure tracking control. Building upon this foundation, we extend the method to address tracking control under uncertain heterogeneous dynamics and constrained communication resources. To this end, we propose an event-triggered resilient control strategy based on equivalent decayrate analysis. The strategy reduces communication overhead by adopting demand-driven scheduling and enhances resilience through a decay-rate-guided feedback mechanism. Our proposed algorithms both mitigate asynchronous DoS attacks by constraining attack surfaces and ensure secure tracking under resource constraints. Finally, numerical examples are provided to verify our theoretical analysis.

This paper investigates secure formation control for unknown networked multi-vehicle systems under denial-of-service (DoS) attacks. Unlike most existing studies that assume a unified attack model across all inter-vehicle communication channels, we propose an asynchronous, distributed DoS attack strategy targeting individual communication links. Specifically, we develop a resilient distributed observer capable of withstanding multi-channel asynchronous DoS attacks. This observer simultaneously provides both specific secure state estimation and output tracking references for each vehicle by introducing the concept of channel-dependent decay rates. Building upon the estimated information, we introduce a novel hybrid policy learning algorithm that combines off-policy and on-policy learning mechanisms. This hybrid approach enables data-driven derivation of decentralized formation control policies while overcoming key limitations of traditional methods, including the requirement for initial stabilizing policies and limitations in dynamic optimization capabilities. Finally, numerical simulations of networked multi-vehicle systems demonstrate the effectiveness of our proposed methodology.

Path Integral Strategy Improvement (PI2)-based impedance control is a superior scheme for preventing damage to the physical structure of the fruit during the harvesting process. However, it is highly sensitive to disturbances and has limited generalization ability during the parameter learning process, making it difficult to apply the correct gripping force to fruits with uncertain stiffness. To solve this problem, this paper proposes a variable impedance force control method that integrates PI2 with Dynamic Movement Primitives (DMPs), supplemented by a force error correction loop. Firstly, an adaptive impedance parameter matching mechanism based on gain schedules is designed to facilitate dynamic estimation of impedance parameters and enable precise force control. To further accelerate impedance parameter matching in unknown environments, the PI2 algorithm is introduced to optimize gain schedules, and DMPs are integrated to suppress disturbances, thereby improving the generalization ability of the impedance model’s parameter learning. In addition, an additional force error control loop has been designed to minimize the deviation between the desired and actual gripping force. Finally, the effectiveness of the proposed method is verified through simulation and experiment in fruit teleoperation picking robot.

Unlike existing results on output tracking control in multi-agent systems, which mainly focus on the relative state among agents, in some cases the state of the system may not be available or measurable. In this paper, we investigate the model-free learning-based distributed optimal tracking control for heterogeneous multi-agent systems with dynamic output feedback under a switching reinforcement learning algorithm. First, a relative output-based distributed observer is developed without exchanging state information, which can significantly reduce the interaction load and broaden the range of applications. Then, a distributed feedback-feedforward controller is proposed, where the optimal feedback and feedforward gain matrices can be learned online by solving two optimization problems, employing policy iteration-based reinforcement learning instead of relying on the leader’s state as in existing studies. Subsequently, the policy iteration algorithm (PI) is modified into the value iteration (VI) learning algorithm, which can relax the requirement for an initial stabilizing control policy and does not depend on the known dynamical model. Additionally, a switching reinforcement learning algorithm is put forward by fully integrating the merits of the previously mentioned methods. The new algorithm not only overcomes the initial stabilizing assumption, but also ensures the convergence of the algorithm in a model-free fashion. Finally, a simulation example is provided to illustrate the theoretical analysis.Note to Practitioners—This paper investigates the model-free learning-based distributed optimal tracking control for heterogeneous multi-agent systems with dynamic output feedback under a switched reinforcement learning algorithm. The proposed distributed cooperative control algorithms can be applied to multiple ground vehicles, air vehicles, and underwater vehicles. Unlike existing results on output tracking control, the obtained results rely on accurate system dynamics and ignore the transient performance, which makes the designed controller far from optimal in potential applications. Moreover, the optimal learning algorithm strictly relies on the initial stabilizing control policy related to accurate system dynamics, and it may be helpless when the system model is completely unknown. To overcome those issues, we developed a new data-driven algorithm or data-based model-free reinforcement learning algorithm to study distributed output tracking control with relative output information by collecting system data instead of using accurate system dynamics. The new algorithm not only overcomes the initial stabilizing assumption but also ensures the algorithm convergence in a model-free fashion. Potential applications of the proposed control algorithms include cooperative formation control, and secondary control of microgrids.

This paper investigates dynamic event-triggered resilient formation control for nonlinear multi-agent systems under multi-channel Denial-of-Service (DoS) attacks. Unlike existing resilient formation control strategies, which assume a unified attack model across all communication channels among followers under known system dynamics, which is not realistic. We propose a novel architecture for secure formation control to tackle the challenges posed by heterogeneous and uncertain dynamics, as well as distributed and asynchronous DoS attacks. Specifically, each communication channel, whether between the leader and followers, or among followers, may be independently and asynchronously attacked. In addition, a novel dynamic event-triggered mechanism that incorporates attack parameters is designed to mitigate the overuse of network bandwidth while avoiding the Zeno behavior. Notably, in comparison with conventional methods for heterogeneous cooperative systems, our approach eliminates the need for distributed observers to reconstruct the leader’s information, thus significantly reducing the transmission of additional variables and simplifying the system architecture. Finally, the effectiveness of our proposed algorithms is verified through a practical example involving a multi-robot system. Note to Practitioners—This paper investigates dynamic event-triggered resilient formation control for nonlinear multi-agent systems under multi-channel Denial-of-Service (DoS) attacks. The proposed distributed resilient control algorithms can be applied to multiple ground vehicles, air vehicles, and underwater vehicles. Unlike existing resilient formation control strategies, which assume a unified attack model across all communication channels among followers under known system dynamics, which is not realistic. We propose a novel architecture for secure formation control to tackle the challenges posed by heterogeneous and uncertain dynamics, as well as distributed and asynchronous DoS attacks. Specifically, each communication channel, whether between the leader and followers, or among followers, may be independently and asynchronously attacked. In addition, a novel dynamic event-triggered mechanism that incorporates attack parameters is designed to mitigate the overuse of network bandwidth while avoiding the Zeno behavior. Notably, in comparison with conventional methods for heterogeneous cooperative systems, our approach eliminates the need for distributed observers to reconstruct the leaders information, thus significantly reducing the transmission of additional variables and simplifying the system architecture. Finally, the effectiveness of our proposed algorithms is verified through a practical example involving a multi-robot system.

This paper investigates the distributed resilient output-tracking control problem for heterogeneous multi-agent systems (MASs) subject to multi-channel denial-of-service (DoS) attacks. First, we propose a simplified framework to address the distributed output tracking control for systems with uncertain dynamics. Then, we develop a distributed output feedback-driven resilient control protocol under asynchronous multi-channel DoS attacks, considering both inter-follower communication channels and leader-follower links. To enhance resilience against attacks, a set of channel-dependent decay rates is introduced and designed to ensure self-healing output tracking capabilities under active attacks. Unlike conventional distributed output control methods that heavily depend on distributed observers and assume the leader’s state is globally accessible or transmitted via distributed communication, our resilient control algorithm eliminates the need for distributed observers while reducing communication overhead through compact output information encoding. Finally, the effectiveness of the proposed algorithm is demonstrated through a practical example involving a multi-vehicle system.

This paper investigates the event-triggered output path-tracking control of networked heterogeneous multi-vehicle (agent) systems with unknown model dynamics. Different from most existing distributed observer methods to estimate the leader vehicle’s state matrix and state, these state-based observer approaches raise the disadvantages of high dimensionality and high frequency of data exchange of state. To address this, in this paper, we propose a novel adaptive distributed output observer (ADOO) that estimates the coefficients of the minimal polynomial instead of requiring knowledge of all the entries of the leader vehicles system matrix. Moreover, our proposed ADOO is model-free without relying on the leader’s accurate system, unlike the model-based way in existing works. Meanwhile, an asynchronous dynamic event-triggered control strategy is developed to reduce the communication load among neighboring vehicles. Then, a decentralized path-tracking controller is learned via a model-free matrix updating learning technique to achieve optimal path-tracking control without requiring an initial stabilizing control policy. By rigorous mathematical analysis shows that our proposed algorithms not only can greatly reduce the dimension of existing observer methods and the frequency of information exchange among neighboring vehicles, but also exclude the Zeno phenomenon. Finally, the numerical simulation is used to validate the efficiency of the theoretical algorithms under investigation. Note to Practitioners—This paper investigates the learning-based distributed optimal path-tracking control for heterogeneous vehicle systems with event-triggered communications. The proposed distributed control algorithms can be applied to multiple ground vehicles, air vehicles, and underwater vehicles. Unlike existing results on distributed state observer methods, the obtained results rely on accurate system dynamics and ignore the transient performance, which makes the designed controller far from optimal in potential applications. Moreover, the optimal learning algorithm strictly relies on the initial stabilizing control policy related to accurate system dynamics, and it may be helpless when the system model is completely unknown. To overcome those issues, we developed a model-free matrix updating learning technique to study distributed output path tracking control by collecting system data instead of using accurate system dynamics. Our algorithm not only overcomes the initial stabilizing assumption but also ensures the algorithm convergence in a model-free fashion. Besides, our proposed distributed observer can greatly lower the dimension and the frequency of information exchange of existing observers. Potential applications of the proposed control algorithms include cooperative formation control of heterogeneous unmanned systems.

In this article, we study the optimal output synchronization problem (OOSP) for uncertain networked Euler-Lagrange (EL) systems. Specifically, the system outputs are expected to be synchronized at the solution of an uncertain distributed time-varying quadratic optimization problem, where each local time-varying cost function includes uncertain parameters. From a centralized perspective, we first develop a controller with adaptive control gains to guide the output of a double-integrator system toward the time-varying optimal solution. By employing the modified average estimators, we extend the centralized design to a distributed implementation to address the OOSP for uncertain EL systems. Using matrix trace properties and composite Lyapunov analysis, we prove that the system outputs can asymptotically converge to the desired time-varying optimal solution. Two examples are used to verify the proposed designs.

The passivity-based asynchronous control is tackled for 2-D Roesser Markovian jump systems (MJSs) and stabilization is guaranteed when 2-D MJSs are susceptible to Denial-of-Service (DoS) attacks. A novel jump model is proposed in this article, where the switching law of subsystems is regulated by the sum of the horizontal and vertical coordinates' values. This differs from the conventional jump model, which presumes that the transition probabilities are identical in both directions. The proposed jump model can avoid the mode ambiguity problem. Given the openness and sharing nature of communication networks, they are susceptible to malicious cyber-attacks that impair system performance. The concept of global time is introduced to help characterize the jump law and construct DoS attack model. Besides, a hidden Markov model (HMM) is utilized to manage the inevitable mismatched mode problem induced by any delay or data dropouts. With the above considerations, several conditions are established for ensuring passivity performance of 2-D MJSs and stabilization when facing DoS attacks. Several equivalent solvable conditions are derived via decoupling strategy and matrix inequality technique. Finally, two simulation examples are provided to demonstrate the validity of the established theoretical results.

This article proposes online data-based reinforcement learning (RL) algorithm for adaptive output consensus control of heterogeneous multiagent systems (MASs) with unknown dynamics. First, we employ the adaptive control technique to design a distributed observer, which provides an estimation of the leader for partial agents, thereby eliminating the need for the global information. Then, we propose a novel data-based adaptive dynamic programming (ADP) approach, associated with a double-integrator operator, to develop an online data-driven learning algorithm for learning the optimal control policy. However, existing optimal control strategy learning algorithms rely on the persistent excitation conditions (PECs), the full-rank condition, and the offline storage of historical data. To address these issues, our proposed method learns the optimal control policy online by solving a data-driven linear regression equations (LREs) based on an online-verifiable interval excitation (IE) condition, instead of relying on PEC. In addition, the uniqueness of the LRE solution is established by verifying the invertibility of a matrix, instead of satisfying the full-rank condition related to PEC and historical data storage as required in existing algorithms. It is demonstrated that our proposed learning algorithm not only guarantees optimal tracking with unknown dynamics but also relaxes some of the strict conditions of existing learning algorithms. Finally, a numerical example is provided to validate the effectiveness and performance of the proposed algorithms.

This article intends to study the asynchronous control problem for 2-D Markov jump systems (MJSs) with nonideal transition probabilities (TPs) under the Roesser model. Two practical considerations motivate the current work. First, considering that the system mode cannot always be observed accurately, a hidden Markov model (HMM) is adopted to describe the relationship between the mismatched modes. Second, considering that the TPs information related to the Markov process and the observation process is difficult to obtain, the nonideal TPs (unknown or uncertain) are simultaneously considered on the two processes. Under the considerations, several new sufficient conditions are developed for concerned closed-loop 2-D MJSs with nonideal TPs, by which the asymptotic mean square stability is ensured with an${\mathcal {H}}_{\infty }$performance index. A nonconservative separation strategy is utilized to decouple the system mode TPs and the observation TPs to facilitate the analysis of nonideal TPs. An unified LMI-based condition is finally developed for the concerned closed-loop 2-D MJSs with/without nonideal TPs, showing more satisfactory conservatism than that in the literature. In the end, we present two examples to validate the superiority of the proposed design method.

In this paper, the distributed coordination control of path tracking and nash equilibrium seeking of networked automated ground vehicles systems with unknown dynamics is investigated under the framework of graphical games. Different from existing works assuming that the vehicle dynamics are known, each vehicle with completely unknown system dynamics is considered in this paper. To solve this problem, a learning-based data-driven technique is proposed to identify and reconstruct the unknown system matrices. Then, based on the identified system matrices, an offline reinforcement learning (RL) algorithm is proposed to derive both the optimal control policies and the policy iteration solution for graphical games, as well as its corresponding convergence is analyzed. Besides, an online learning algorithm only relying on the online information of states and inputs in an online way is developed to solve the optimal path tracking control problem. As a result, the requirement of relying on the vehicle’s dynamics in the traditional tracking control protocols is completely relaxed by our proposed method. The optimal distributed control policies found by the proposed RL algorithm satisfies the global Nash equilibrium and synchronizes all tracked vehicles to the pinning vehicle. Numerical simulation results are provided to show the effectiveness of the theoretical analysis.

This article considers the output tracking control problem of nonidentical linear multiagent systems (MASs) using a model-free reinforcement learning (RL) algorithm, where partial followers have no prior knowledge of the leader's information. To lower the communication and computing burden among agents, an event-driven adaptive distributed observer is proposed to predict the leader's system matrix and state, which consists of the estimated value of relative states governed by an edge-based predictor. Meanwhile, the integral input-based triggering condition is exploited to decide whether to transmit its private control input to its neighbors. Then, an RL-based state feedback controller for each agent is developed to solve the output tracking control problem, which is further converted into the optimal control problem by introducing a discounted performance function. Inhomogeneous algebraic Riccati equations (AREs) are derived to obtain the optimal solution of AREs. An off-policy RL algorithm is used to learn the solution of inhomogeneous AREs online without requiring any knowledge of the system dynamics. Rigorous analysis shows that under the proposed event-driven adaptive observer mechanism and RL algorithm, all followers are able to synchronize the leader's output asymptotically. Finally, a numerical simulation is demonstrated to verify the proposed approach in theory.

In this article, a data-efficient off-policy reinforcement learning (RL) approach is proposed for distributed output tracking control of heterogeneous multiagent systems (HMASs) using approximate dynamic programming (ADP). Different from existing results that the kinematic model of the exosystem is addressable to partial or all agents, the dynamics of the exosystem are assumed to be completely unknown for all agents in this article. To solve this difficulty, an identifiable algorithm using the experience-replay method is designed for each agent to identify the system matrices of the novel reference model instead of the original exosystem. Then, an output-based distributed adaptive output observer is proposed to provide the estimations of the leader, and the proposed observer not only has a low dimension and less data transmission among agents but also is implemented in a fully distributed way. Besides, a data-efficient RL algorithm is given to design the optimal controller offline along with the system trajectories without solving output regulator equations. An ADP approach is developed to iteratively solve game algebraic Riccati equations (GAREs) using online information of state and input in an online way, which relaxes the requirement of knowing prior knowledge of agents' system matrices in an offline way. Finally, a numerical example is provided to verify the effectiveness of theoretical analysis.

This article considers the security-based passivity problem for a class of discrete-time Markov jump systems in the presence of deception attacks, where the deception attacks aim to change the transmitted signal. Considering the impact of deception attacks on network disruption, it causes the existence of time-varying delays in signal transmission inevitably, which makes the controlled system and the controller work asynchronously. The asynchronous control method is employed to overcome the nonsynchronous phenomenon between the system mode and controller mode. On the other hand, to reduce the frequency of data transmission, a resilient asynchronous event-triggered control scheme taking deception attacks into account is designed to save communication resources, and the proposed controller can cover some existing ones as special examples. Moreover, different triggering conditions corresponding to different jumping modes are developed to decide whether state signals should be transferred. A new stability criterion is derived to ensure the passivity of the resultant system although there exist deception attacks. Finally, a simulation example is given to verify the theoretical analysis.

This paper investigates the resilient distributed secure output path following control problem of heterogeneous autonomous ground vehicles (AGVs) subject to cyber attacks based on reinforcement learning algorithm. Most existing results are subject to the same attack models for all communication channels, however multiple channels launched by different attackers are considered in this paper. First, a predictor-acknowledgement clock algorithm for each vehicle is proposed to judge whether the communication channel among neighboring vehicles is attacked or not by receiving or transmitting an acknowledgement. Then, a resilient distributed predictor is proposed to predict the pinning vehicle’s state for each vehicle. In addition, a resilient local control protocol consisting of the feedforward state provided by the predictor and the local feedback state of each vehicle is developed for the output path following problem, which is further converted to the optimal control problem by designing a discounted performance function. Discounted algebraic Riccati equations (AREs) are derived to address the optimal control problem. An off-policy reinforcement learning (RL) algorithm is put forward to learn the solution of discounted AREs online without any prior knowledge of vehicles’ dynamics. It is shown that the RL-based output path following control problem of AGVs imposed by cyber attacks can be achieved in an optimal manner. Finally, a numerical example is provided to verify the effectiveness of theoretical analysis.

This paper focuses on the distributed consensus seeking of multi-agent systems (MASs) with discrete-time control updating and intermittent communications among agents. Compared with existing linearly coupled protocols, a nonlinear coupled Zeno-free event-triggered controller is first proposed, which is further to project the static and dynamic triggering mechanisms exploited by using the deadband control method. Then, the node-based nonlinear coupled adaptive event-triggered controller with online self-tuning of time-varying coupling weight and its corresponding to static and dynamic deadband-based event-triggered mechanisms are designed, respectively. The exploited adaptive event-triggered controller does not rely on any global information of interaction structure and is implemented in a fully distributed fashion. In addition, two dynamic proposals not only cover existing static strategies as special cases, but also show that the minimal inter-execution time of dynamic one is not smaller than that of static one. Theoretical analysis shows that the proposed static and dynamic deadband-based event-triggered mechanisms can not only ensure the average consensus with Zeno-freeness, but also achieve the data reduction of communication and control. Finally, the proposed algorithms applied to circuit implementation are corroborated to prove its practical merits and validity.

This article studies the problem of dissipativity-based asynchronous state estimation for a class of discrete-time Markov jump neural networks subject to randomly occurring nonlinearities, sensor saturations, and stochastic parameter uncertainties. First, two stochastic nonlinearities occurring in the system are described by statistical means and obey two Bernoulli processes independently. Then, the hidden Markov model is used to characterize the real communication environment closely between the designed estimator and the system model due to the networked-induced phenomenons that also lead to randomly occurring parametric uncertainties of the estimator considered modeled by two Bernoulli processes. A new criterion is established to guarantee that the resulting error system is stochastically stable with predefined dissipativity performance. Finally, we provide a simulation example to validate the theoretical analysis.

In this article, the problem of distributed synchronization of networked systems with actuator bias faults is investigated. To effectively use the limited network bandwidth and avoid the requirement of global information, a novel adaptive event-triggered state feedback controller and a dynamic triggering law are designed jointly by employing a projection operator approach. The proposed synchronization scheme is different from existing ones that have focused on designing controllers and triggering laws independently. Besides, our scheme is extended to design an observer-based distributed adaptive event-triggered controller and corresponding dynamic triggering law when the system states are unmeasurable. Theoretical analysis shows that under the two different distributed event-triggered synchronization schemes, the following three results can be obtained: 1) fully distributed synchronization can be achieved without knowing global information associated with the underlying communication topology and node's scale; 2) continuous communication among adjacent nodes can be avoided for both designed controllers and dynamic triggering laws; and 3) exclusion of Zeno phenomenon is shown by contradiction. Finally, the effectiveness of the proposed algorithms is verified through three numerical examples.

This article considers the secure consensus of networked nonlinear multiagent systems (MASs) under denial-of-service attacks via unreliable communication networks in a directed graph. Different from existing consensus results in an undirected graph, the case that the communication topology of MASs may be frequently attacked by attackers in a directed graph is considered. The existence of attackers leads to the entire communication topology of MASs having no directed spanning tree that plays a key role in consensus analysis. Besides, to ease the communication load between agents, a resilient leader-following event-triggered control protocol and corresponding to the static triggering policy are first proposed, respectively. Then, an extra key variable is adopted to develop the dynamic triggering policy for the directed graph case to achieve secure consensus while reducing triggering instants, which subsumes several existing static ones as special cases. A unified hybrid framework is established to derive new sufficient conditions of achieving secure consensus without continuous communications among neighboring agents. Besides, parameters of sufficient conditions are designed by solving optimization problems. Finally, the theoretical analysis is verified by a robot manipulator system example.

This article addresses the fixed-time average consensus problem of nonlinear multiagent systems (MASs) subject to input delay, external disturbances, and switching topologies. Different from the finite-time convergence, the convergence time of the fixed-time convergence is independent of initial conditions. Then, an event-based control strategy is presented to reach the fixed-time average consensus under switching topologies and intermittent communication. Because the nonlinear dynamics, external disturbances, switching topologies, and triggering condition for intermittent communication are considered, the fixed-time consensus problem is more challenging under the event-based control than under the continuous-time control. Besides, a new measurement error is designed based on the hyperbolic tangent function to avoid Zeno behavior. Furthermore, an improved triggering function is designed to avoid continuous monitoring. Hence, resource consumption is reduced significantly. Finally, the effectiveness of the algorithms is validated by three simulation examples.

This paper investigates the exponential synchronization (ES) problem for complex networks (CNs) under an intermittent adaptive event-triggered control (IAE-TC) strategy which is based on dynamic IAE-TC during the control activation intervals. In this control mechanism, the intermittent controller has adaptability to the evolution results of the controlled networks and is activated only when the event-triggered condition is violated. Then, by employing this kind of control strategy and the Lyapunov method, some sufficient conditions are proposed to achieve the ES of CNs and it is proven that the Zeno behavior can be eliminated. Besides, an application about the islanded microgrid system and some numerical simulations are provided to verify the effectiveness of the derived theoretical results. Moreover, in the numerical simulations, it is shown that the dynamic IAE-TC strategy can decrease the number of event-triggered instants more availably than the static one.

Nonlinear couplings among units (nodes) are ubiquitous in engineering systems including, e.g., radar and sonar systems, which have been ignored in most works. In this article, the problem of distributed synchronization of nonlinear networked systems with nonlinear couplings is studied. Specifically, two kinds of nodes' communication couplings including nonlinear relative and nonlinear absolute state couplings are considered. To reduce the requirements of control and communication among nodes and avoid any global network information, two edge-based fully adaptive event-triggered control protocols based on nonlinear relative and absolute state couplings are proposed by using the projection operator technique, which is followed by design of corresponding dynamic event-triggered mechanisms. The advantages of our proposed dynamic event-triggered strategies show that it can boil down to existing static ones as special examples, and the minimal inter-execution time of the proposed dynamic triggering laws is larger than that of static ones. Theoretical analysis shows that the proposed algorithm not only guarantees fully adaptive Zeno-free synchronization of networked systems without requiring any global information, but also avoids continuous communications among nodes, and considerably reduce the frequency of controller updates. Finally, the practical merits of the proposed algorithms are corroborated using a Chua's circuit network.

This paper adopts two different approaches, the small-gain technique and the integral quadratic constraints (IQCs), to investigate the synchronization problem of coupled harmonic oscillators (CHOs) via an event-triggered control strategy in a directed graph. First, a novel control protocol is proposed such that every state signal of the CHO decides when to exchange information with its neighbors asynchronously. Then, the resulting closed-loop system based on the designed control protocol is converted into a feedback interconnection of a linear system and a bounded operator, and the stable condition of the feedback interconnection is presented by employing the small-gain technique. In order to better describe the relationship between the input and output, the IQCs theorem is applied to derive the stable condition on the basis of the Kalman-Yakubovich-Popov lemma. Finally, a simulation example is provided to verify the proposed new algorithms.

In this article, the dissipativity-based filtering of the Markovian jump neural networks subject to incomplete measurements and deception attacks is investigated by adopting an event-triggered communication strategy, where the attackers are supposed to occur in a random fashion but obey the Bernoulli distribution. Consider that the information of the system mode is transmitted to the filter over the communication network that is vulnerable to external attacks, which may lead to the undesired performance of the resulting system by injecting malicious information from the attackers. As a result, the filter has difficulty completing information from the original system. Besides, an event-triggered communication mechanism is introduced to reduce the communication frequency between data transmission due to the limited network resources, and different triggering conditions corresponding to different jump modes are developed. Then, based on the above considerations, the sufficient condition is derived to ensure the stochastic stability and dissipativity of the resulting augmented system although the deception attacks and incomplete information exist. A numerical simulated example is provided to verify the theoretical analysis.

This paper studies the event-triggered containment control (CC) problem of multiagent systems with a directed graph, where the followers' communication graph is an undirected graph. A novel event-triggered CC protocol is presented to schedule communications between agents, which depends on both local states and control inputs. Different from traditional approaches using broadcasts, a unique property of the proposed protocol is that it enables agent-to-agent data transmission. To do so, each communication link is associated with a specific local event. The estimated relative interagent states are transmitted over a specific link only when the related input-based event is detected. We develop sufficient conditions to solve CC problem under this protocol and extend it to the adaptive event-triggered protocol that does not require the global knowledge on the smallest positive eigenvalue of the Laplacian. For both protocols, we derive positive low bounds on the interevent time intervals generated by individual events, which eliminate the “Zeno phenomenon.” Feasibility of the proposed algorithm is verified by an example.

This paper studies the problem of event-triggered secure consensus for multiagent systems subject to periodic energy-limited denial-of-service (DoS) attacks, where DoS attacks usually prevent agent-to-agent data transmission. The DoS attacks are assumed to occur periodically based on the time-sequence way and the period of DoS attacks and the uniform lower bound of the communication areas are predetected by some devices. Based on the above assumptions, an event-based protocol consisting of two different measurements corresponding to leader-followers and follower-follower is presented to schedule communications between agents, which can reduce the update frequency of the controller. Then, the stability of the resultant error system is analyzed to derive sufficient conditions of achieving secure consensus by employing the Lyapunov function and the inductive approach. Besides, positive low bounds on any two consecutive intervals of events generated by individual events are calculated to eliminate "Zeno behavior" under the developed triggering condition and event-triggered protocol. Simulation result is provided to verify the theoretical analysis.

This paper applies an input-based triggering approach to investigate the secure consensus problem in multiagent systems under denial-of-service (DoS) attacks. The DoS attacks are based on the time-sequence fashion and occur aperiodically in an unknown attack strategy, which can usually damage the control channels executed by an intelligent adversary. A novel event-triggered control scheme on the basis of the relative interagent state is developed under the DoS attacks, by designing a link-based estimator to estimate the relative interagent state between intermitted communication instead of the absolute state. Compared with most of the existing work on the design of the triggering condition related to the state measurement error, the proposed triggering condition is designed based on the control input signal from the view of privacy protection, which can avoid continuous sampling for every agent. Besides, the attack frequency and attack duration of DoS attacks are analyzed and the secure consensus is reachable provided that the attack frequency and attack duration satisfy some certain conditions under the proposed control algorithm. “Zeno phenomenon” does not exhibit by proving that there exist different positive lower bounds corresponding to different link-based triggering conditions. Finally, the effectiveness of the proposed algorithm is verified by a numerical example.

This paper considers the consensus problem of multiagent systems with the input-based triggering condition. A model-based approach is first given to estimate the relative interagent states between intermittent communications instead of absolute states. A novel consensus protocol consisting of the relative interagent states is proposed for the consensus problem. Besides, compared with some results on the triggering condition consisting of state measurement error, a new triggering condition is constructed based on the control input. Then, the consensus protocol is executed by every agent in a fully distributed way, which has the advantage that the controller design is related to the number of agents instead of using global information. Moreover, the bounds of parameters of the controller and the triggering condition can be obtained by the proposed algorithm simultaneously, which depends on the total number of agents in multiagent networks. The proposed control scheme can ensure that states of all agents can achieve consensus. It is shown that “Zeno behavior” does not appear under the proposed algorithm. Finally, an illustrative example is given to verify the proposed method.

In this paper, the leader-following consensus problem of high-order multiagent systems via event-triggered control is discussed. A novel distributed event-triggered communication protocol based on state estimates of neighboring agents is proposed to solve the consensus problem of the leader-following systems. We first investigate the consensus problem in a fixed topology, and then extend to the switching topologies. State estimates in fixed topology are only updated when the trigger condition is satisfied. However, state estimates in switching topologies are renewed with two cases: 1) the communication topology is switched or 2) the trigger condition is satisfied. Clearly, compared to continuous-time interaction, this protocol can greatly reduce the communication load of multiagent networks. Besides, the event-triggering function is constructed based on the local information and a new event-triggered rule is given. Moreover, “Zeno behavior” can be excluded. Finally, we give two examples to validate the feasibility and efficiency of our approach.

In this paper, the problem of distributed event-triggered pinning control for practical consensus of multiagent systems (MASs) with quantized communication based on a directed graph is investigated. The pinning control for practical consensus of MASs with uniform quantizer is first discussed. Then, in order to decrease communication load of interagent, the event-triggered quantized communication protocol is designed. The nonsmooth analysis and Gronwall's inequality approach is used to guarantee the existence of a solution to the resulting closed-loop system. It is shown that practical consensus is reachable through the event-triggered control and converges to a consensus set. Moreover, “Zeno phenomenon” can be excluded. Finally, an example is given to validate the feasibility and efficiency of the proposed new design method.