Dynamic Event-Triggered Consensus Control for Multi-Agent Systems Using Adaptive Dynamic Programming

Zhang, Qi; Yang, Yang; Xie, Xiaoran; Chen, Xu; Han, Yu

doi:10.1109/access.2022.3214849

Cited by 9 publications

(2 citation statements)

References 45 publications

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“…Designing a feasible and effective dynamic event-triggered strategy while ensuring the absence of Zeno behavior is undoubtedly a formidable challenge. As is evident from the findings in [9,40,41], the development of practical event-triggered strategies is intricately tied to the solutions of certain matrix inequalities. Ensuring their existence is no small feat.…”

Section: Resultsmentioning

confidence: 99%

Distributed Dynamic Event-Triggered Control to Leader-Following Consensus of Nonlinear Multi-Agent Systems with Directed Graphs

Guan,

Ren,

Tan

2024

Entropy

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This paper investigates achieving leader-following consensus in a class of multi-agent systems with nonlinear dynamics. Initially, it introduces a dynamic event-triggered strategy designed to effectively alleviate the strain on the system’s communication resources. Subsequently, a distributed control strategy is proposed and implemented in the nonlinear leader-follower system using the dynamic event-triggered mechanism, aiming to ensure synchronization across all nodes at an exponential convergence speed. Thirdly, the research shows that under the dynamic event-triggered strategy the minimum event interval of any two consecutive triggers guarantees the elimination of Zeno behavior. Lastly, the validity of the calculation results is verified by a simulation example.

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Section: Resultsmentioning

confidence: 99%

Distributed Dynamic Event-Triggered Control to Leader-Following Consensus of Nonlinear Multi-Agent Systems with Directed Graphs

Guan,

Ren,

Tan

2024

Entropy

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“…Remark 2. Conditions ( 6) and (7) show that the existence condition of consensus protocol (5) is that (E −1 (ε)A, E −1 (ε)B) is strongly controllable. On the other hand, we do not require that the matrix A 22 should be nonsingular, which means that our method can be used for multi-time-scale agent networks with both the standard and nonstandard agents.…”

Section: A the Relative States Based Consensus Analysismentioning

confidence: 99%

Cooperative Control of Multi-Time-Scale Agent Networks Under Digraphs

2023

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This paper addresses cooperative control for a class of multi-agent networks. An interesting feature of such network is that each agent possesses the characteristic of multi-time-scale. By developing the relative states or the relative outputs based consensus protocols, some well-conditioned consensus criteria in terms of linear matrix inequalities are derived successively based on the ε-strict Lyapunov function. Compared with undirected graphs or strongly connected digraphs in existing works, a more general digraph that it contains at least a directed spanning tree is considered herein. Moreover, a fully distributed consensus protocol with time-varying coupling strength is proposed to avoid using global information. Finally, the obtained theoretical results are validated by two examples including standard and nonstandard agents.

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Distributed optimal control of nonlinear multi‐agent systems based on integral reinforcement learning

Xu,

Li,

2024

Optim Control Appl Methods

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In this article, a distributed optimal control approach is proposed for a class of affine nonlinear multi‐agent systems (MASs) with unknown nonlinear dynamics. The game theory is used to formulate the distributed optimal control problem into a differential graphical game problem with synchronized updates of all nodes. A data‐based integral reinforcement learning (IRL) algorithm is used to learn the solution of the coupled Hamilton–Jacobi (HJ) equation without prior knowledge of the drift dynamics, and the actor‐critic neural networks (A‐C NNs) are used to approximate the control law and the cost function, respectively. To update the parameters synchronously, the gradient descent algorithm is used to design the weight update laws of the A‐C NNs. Combining the IRL and the A‐C NNs, a distributed consensus optimal control method is designed. By using the Lyapunov stability theory, the developed optimal control method can show that all signals in the considered system are uniformly ultimately bounded (UUB), and the systems can achieve Nash equilibrium when all agents update their controllers simultaneously. Finally, simulation results are given to illustrate the effectiveness of the developed optimal control approach.

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Dynamic Event-Triggered Consensus Control for Multi-Agent Systems Using Adaptive Dynamic Programming

Cited by 9 publications

References 45 publications

Distributed Dynamic Event-Triggered Control to Leader-Following Consensus of Nonlinear Multi-Agent Systems with Directed Graphs

Distributed Dynamic Event-Triggered Control to Leader-Following Consensus of Nonlinear Multi-Agent Systems with Directed Graphs

Cooperative Control of Multi-Time-Scale Agent Networks Under Digraphs

Distributed optimal control of nonlinear multi‐agent systems based on integral reinforcement learning

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