Self-triggered Consensus Research Articles

Self-triggered consensus resilient control for multi-agent systems against sensor deception attacks based on a single parameter learning method

This paper presents a self-triggered consensus resilient control method for nonlinear multi-agent systems (MASs) under sensor deception attacks. A single parameter learning method is integrated into backstepping technique to simplify design procedure. The neural networks are utilized to compensate for unknown dynamics of the MASs. Moreover, a self-triggered mechanism is presented for MASs to refrain from continuously monitoring triggering conditions and conserve communication resources. The designed controller can resist sensor deception attacks and guarantee that all signals of the MASs are uniformly bounded. An expository simulation example reveals the virtue of the presented method.

Fuzzy prescribed-time self-triggered consensus control for nonlinear multi-agent systems with dead-zone output

This paper investigates the fuzzy prescribed-time self-triggered consensus control for nonlinear multi-agent systems (MASs) with dead-zone output. First, by proposing a dead-zone output approximation model and utilizing Nussbaum-type functions, an adaptive compensation mechanism is developed to alleviate the effect of unknown dead-zone output. Meanwhile, the fuzzy logic systems are incorporated to approximate the unknown functions of the nonlinear MASs. Next, by adopting a time-varying scaling function, a consensus control method is recursively established to steer the consensus errors into a small range within the prescribed time. Furthermore, a self-triggered scheme is established to conserve communication resources while avoiding continuously monitoring trigger conditions. It is illustrated that boundedness of all signals and practical prescribed-time leader-following consensus can be achieved. Finally, the simulation results show the effectiveness of the proposed method.

Self-Triggered Consensus Control of Multi-Agent Systems From Data

Self-Triggered Consensus Control of Multi-Agent Systems From Data

Adaptive Self-Triggered Control for Multi-Agent Systems with Actuator Failures and Time-Varying State Constraints

This work focuses on the consensus problem for multi-agent systems (MASs) with actuator failures and time-varying state constraints, and presents a fixed-time self-triggered consensus control protocol. The use of time-varying asymmetrical barrier Lyapunov functions (BLF) avoids the violation of time-varying state constraints in MASs, ensuring stability and safety. Meanwhile, the system’s performance is further enhanced by leveraging the proposed adaptive neural networks (NNs) control method to mitigate the effects of actuator failures and nonlinear disturbances. Moreover, a self-triggered mechanism based on a fixed-time strategy is proposed to reach rapid convergence and conserve bandwidth resources in MASs. The mechanism achieves consensus within a predefined fixed time, irrespective of the system’s initial states, while conserving communication resources. Finally, the proposed method’s effectiveness is confirmed through two simulation examples, encompassing diverse actuator failure scenarios.

A Decentralized Dynamic Self-Triggered Control Approach to Consensus of Multiagent Systems

This article studies the self-triggered consensus control problem for linear multiagent systems (MASs) with the aim of improving resources utilization efficiency, e.g., saving communication bandwidth and reducing node energy consumption. First, a distributed control protocol is designed and a novel decentralized dynamic self-triggering mechanism (DDSTM) is proposed, with which, each agent can not only compute its own next triggering instant but also is able to compute the neighbors’ next triggering instant. Then, a hybrid system model is constructed to completely describe the close-loop system with jump dynamics. Based on the hybrid model, Lyapunov conditions are derived for MAS consensus analysis. The proposed DDSTM can overcome some limitations of the existing distributed STMs in the sense of synchronously acquiring consensus error or uniquely determining triggering instants, moreover, it allows transceivers to be turned on only at triggering instants and enter into sleep mode at the rest time, such that node energy is efficiently saved. Preadjustable minimum triggering interval is imposed between any consecutive triggering events; thus, a minimum sleep period for transceivers is always guaranteed. Finally, a numerical example is presented to demonstrate the effectiveness of the proposed approach.

Self-Triggered Consensus Filtering over Asynchronous Communication Sensor Networks

In this paper, a self-triggered consensus filtering is developed for a class of discrete-time distributed filtering systems. Different from existing event-triggered filtering, the self-triggered one does not require to continuously judge the trigger condition at each sampling instant and can save computational burden while achieving good state estimation. The triggering policy is presented for pre-computing the next execution time for measurements according to the filter’s own data and the latest released data of its neighbors at the current time. However, a challenging problem is that data will be asynchronously transmitted within the filtering network because each node self-triggers independently. Therefore, a co-design of the self-triggered policy and asynchronous distributed filter is developed to ensure consensus of the state estimates. Finally, a numerical example is given to illustrate the effectiveness of the consensus filtering approach.

Leader–Follower-Based Self-Triggered Consensus Control of Industrial Induction Motor Drives

Alternating current motors are critical to industry, as they drive many machines in the manufacturing and processing industries. To accomplish heavy tasks, often, a number of small motors must operate cooperatively, which means that the operation of the motors must be coordinated using, for example, consensus control. To accomplish this, the motors must communicate with each other. This communication can be periodic or event driven. As periodic communication may waste communication resources when no control update is needed, we propose a need-based self-triggered communication (STC) mechanism to achieve improved communication efficiency. We propose an STC technique for the leader–follower-based consensus control of induction motors. To study this method, we developed both centralized and distributed STC models. In the centralized approach, each motor is connected to a central unit that calculates the next communication time. When distributed STC is used, each motor calculates the next communication time solely based on information from directly connected neighboring motors, thus eliminating the possibility of a single point of failure. Extensive simulations were conducted to validate the proposed approaches. Our results show that the proposed self-triggered consensus control technique gets the same level of performance as a standard periodic control approach while utilizing fewer communication resources.

Neural-Network Based Adaptive Self-Triggered Consensus of Nonlinear Multi-Agent Systems With Sensor Saturation

This paper aims to propose a self-triggered consensus control scheme for a class of nonlinear multi-agent systems with sensor saturation. Because of the existence of unknown nonlinear dynamics, this study borrows the approximation capability of neural networks to design the consensus control protocol. This paper adopts neural network to approximate the ideal controller, instead of using the combination of neural network and adaptive method to approximate the unknown system dynamics. Thus, the extended approximation property of neural network for event-based sampling can be beneficially introduced. Moreover, the designed controller only updates at discrete time, which enables that the system can be modeled as a hybrid system with impulsive dynamics. Thus, the stability theory of impulsive systems can be used to analyze the convergence of the system. It should be noted that this is the first time to propose an effective event-triggered consensus control algorithm based on neural network. Furthermore, this paper also considers a frequently encountered phenomenon of sensor saturation. The convex hull method is adopted to deal with sensor saturation problem, instead of the widely used sector condition method. Finally, the performance of the proposed neural-network based self-triggered consensus control algorithm is demonstrated by the numerical examples.

A Zeno-Free Self-Triggered Approach to Practical Fixed-Time Consensus Tracking With Input Delay

This article considers the practical fixed-time self-triggered consensus tracking problem of delayed multiagent networks (MANs) subject to external disturbances under undirected topology and directed topology. The fixed-time consensus implies that the consensus is reached in a finite time and the convergence time is independent of initial conditions under the nonlinear consensus protocols. A self-triggered control (STC) strategy is developed based on the event-triggered control (ETC) strategy. For the ETC strategy, the nonlinear controllers and the measurement errors are designed based on the hyperbolic tangent function to avoid a nondifferential problem and Zeno behavior. To avoid continuous monitoring, the STC strategy is presented. Furthermore, the minimal interevent interval is strictly positive, which implies that no Zeno behavior occurs in the STC strategy. Finally, a numerical example is presented to verify the availability of the algorithms.

Event/Self-Triggered Consensus Control of Multiagent Systems With Undesirable Sensor Signals.

This article focuses on event-triggered consensus control for multiagent systems subject to sensor faults or noises. First, a descriptor state observer with a low-pass filtering characteristic being developed for each agent using output information. The convergence regions of estimation errors can be reduced by a nonsingular suppression matrix. Leader-follower event-triggered consensus protocols with continuous-time communication are designed for multiagent systems based on the estimated states. By virtue of the Jordan form of the Laplacian matrix, the stability conditions are derived by using the Lyapunov analysis. Then, new self-triggered consensus protocols are designed for the multiagent systems to remove the requirement of the continuous monitoring triggering condition and continuous communication simultaneously. The triggering interval is proved greater than 0, and the Zeno behavior is excluded for all agents. Finally, numerical simulations are conducted to demonstrate the effectiveness of the proposed design.

Self-Triggered Consensus of Vehicle Platoon System With Time-Varying Topology.

This paper focuses on the consensus problem of a vehicle platoon system with time-varying topology via self-triggered control. Unlike traditional control methods, a more secure event-triggered controller considering the safe distance was designed for the vehicle platoon system. Then, a Lyapunov function was designed to prove the stability of the platoon system. Furthermore, based on the new event-triggered function, a more energy efficient self-triggered control strategy was designed by using the Taylor formula. The new self-triggered control strategy can directly calculate the next trigger according to the state information of the last trigger. It avoids continuous calculation and measurement of vehicles. Finally, the effectiveness of the proposed two self-triggered control strategies were verified by numerical simulation experiments.

Self-triggered consensus control for linear multi-agent systems with input saturation

In this paper, we study the consensus problem for a class of linear multi-agent systems &#x0028 MASs &#x0029 with consideration of input saturation under the self-triggered mechanism. In the context of discrete-time systems, a self-triggered strategy is developed to determine the time interval between the adjacent triggers. The triggering condition is designed by using the current sampled consensus error. Furthermore, the consensus control protocol is designed by means of a state feedback approach. It is shown that the considered multi-agent systems can reach consensus with the presented algorithm. Some sufficient conditions are proposed in the form of linear matrix inequalities &#x0028 LMIs &#x0029 to show the positively invariant property of the domain of attraction &#x0028 DOA &#x0029 . Moreover, some sufficient conditions of controller synthesis are provided to enlarge the volume of the DOA and obtain the control gain matrix. A numerical example is simulated to demonstrate the effectiveness of the theoretical analysis results.

The distributed output consensus control of linear heterogeneous multi-agent systems based on event-triggered transmission mechanism under directed topology

In this paper, the distributed output consensus control of linear heterogeneous multi-agent systems (LH_MASs) based on event-triggered transmission strategy under directed topology is addressed. Under the assumption that the state of each node is unmeasurable, the state observer is first designed. Then, in order to decrease the information interaction among agents, the event-triggered consensus protocol (ETCP) based on the designed state observer and dynamic compensator is proposed. Here, a novel triggering function is designed such that each agent does not need to monitor the states of its neighbors for event detection. In addition, on account of state-dependent threshold and time-dependent threshold, the combined threshold is presented to take both advantages into account at the same time. By selecting an appropriate Lyapunov function and regulation parameters, the output consensus of LH_MASs can be achieved and the Zeno behavior can be excluded. To avoid continuous monitoring of each agent’s state, we further propose the self-triggered consensus protocol (STCP). Furthermore, the novel event-triggered consensus protocol for mitigating external disturbances (ED_ETCP) is raised. Finally, several numerical examples are provided to illustrate the validity of the theoretical results.

Event- and self-triggered consensus for double-integrator networks with relative state measurements

ABSTRACTThis paper addresses the consensus problem of double-integrator networks via event- and self-triggered control approaches. In the proposed control protocol, only relative state information is utilised to synchronise all agents. First, an event-triggering algorithm with periodic event detection is designed to determine the event times of each agent. Then, a self-triggered control algorithm is provided to further reduce resource consumption. Two numerical simulations are conducted to illustrate the effectiveness of the results.

Self-Triggered Leader-Following Consensus for High-Order Nonlinear Multiagent Systems via Dynamic Output Feedback Control.

This paper investigates the event-based leader-following consensus problem for high-order nonlinear multiagent systems whose dynamics are in strict feedback forms and satisfy Lipschitz condition. By using self-triggered control scheme and dynamic output feedback control method in combination, a new class of distributed self-triggered consensus protocols is proposed based only on the relative output measurements of neighboring agents. It is noted that the proposed protocols only require the output information of neighboring agents to be shared and the designed self-triggered algorithm can avoid continuous communication among neighboring agents, thus the communication cost is reduced significantly. Sufficient conditions in terms of matrix inequalities are derived to guarantee the exponential leader-following consensus. The effectiveness of the theoretical results is illustrated through a simulation example.

Event-based network consensus with communication delays

In this paper, the consensus problem with communication delays is studied under event-triggered protocols. We propose two novel event-triggered sampled-data transmission strategies (asynchronous and synchronous), where only local and event-triggering states are utilized to update the broadcasting state of each agent. Under the circumstance of limited information exchanging and distributed communication delays, it is strictly proved that the proposed protocols can realize consensus. Furthermore, a self-triggered consensus algorithm is proposed in which a set of iterative procedures are given to compute the event-triggered instants. Interestingly, the final consensus value is theoretically obtained even in the presence of event-based communication and distinct finite delays. Finally, the effectiveness of the theoretical results is demonstrated by numerical example.

A Jamming-Resilient Algorithm for Self-Triggered Network Coordination

The issue of cyber-security has become ever more prevalent in the analysis and design of cyber-physical systems. In this paper, we investigate self-triggered consensus networks in the presence of communication failures caused by denial-of-service (DoS) attacks. A general framework is considered in which the network links can fail independent of each other. By introducing a notion of persistency-of-communication (PoC), we provide an explicit characterization of DoS frequency and duration under which consensus can be preserved by suitably designing time-varying control and communication policies. An explicit characterization of the effects of DoS on the consensus time is also provided. The considered notion of PoC is compared with classic average connectivity conditions that are found in pure continuous-time consensus networks. Finally, examples are given to substantiate the analysis.

Decentralized event-triggered consensus for linear multi-agent systems under general directed graphs

In this paper, the event-triggered consensus problem is studied for multi-agent systems with general linear dynamics under a general directed graph. Based on state feedback, we propose a decentralized event-triggered consensus controller (ETCC) for each agent to achieve consensus, without requiring continuous communication among agents. Each agent only needs to monitor its own state continuously to determine when to trigger an event and broadcast its states to its out-neighbors. The agent updates its controller when it broadcasts its states to its out-neighbors or receives new information from its in-neighbors. The ETCC can be implemented in multiple steps. it is proved that under the proposed ETCC there is no Zeno behavior exhibited. To relax the requirement of continuous monitoring of each agent’s own states, we further propose a self-triggered consensus controller (STCC). Simulation results are given to illustrate the theoretical analysis and show the advantages of the event-triggered and self-triggered controllers proposed in this paper.

Sampling-based event-triggered consensus for multi-agent systems

This work considers the event-based consensus algorithms for multi-agent systems with periodic sampling schemes. An event-triggered consensus algorithm using sampled states are proposed. The measurement error in the event design is defined based on the state of local neighborhood center in order to reduce the number of events. Based on this algorithm, a self-triggered consensus protocol is proposed to further reduce the amount of communication and state sampling, as well as the number of events and thus the controller updates. It is noted that by introducing the periodic sampling scheme, Zeno behaviors can be naturally avoided in both of the algorithms. The effectiveness of the proposed algorithms is illustrated by simulation examples.

Self-triggered output feedback control for consensus of multi-agent systems

This paper studies the self-triggered control consensus problem of general linear multi-agent systems(MASs). A novel self-triggered control strategy based on output feedback is proposed for centralized and distributed cases, respectively. In consideration of the states of agents that are not available, a state observer is adopted. A dynamic observer-based control law is employed to improve the transient response. Under this triggering strategy, both the estimated states of MASs and the states of controller are updated at triggering time. The next triggering time is predetermined at the last triggering instant. Moreover, the asymptotic consensus of MASs can be guaranteed. Finally, the effectiveness of the proposed control strategy is illustrated by a numerical example.