非周期图
计算机科学
控制理论(社会学)
网络拓扑
共识
李雅普诺夫函数
多智能体系统
人工神经网络
力矩(物理)
非线性系统
离散时间和连续时间
数学
控制(管理)
人工智能
统计
物理
经典力学
组合数学
量子力学
操作系统
作者
Xue Luo,Jingyi Wang,Jianwen Feng,Jiayi Cai,Yi Zhao
出处
期刊:Mathematics
[MDPI AG]
日期:2023-05-06
卷期号:11 (9): 2196-2196
被引量:1
摘要
This paper presents a solution to the consensus problem for a particular category of uncertain switched multi-agent systems (MASs). In these systems, the communication topologies between agents and the system dynamics are governed by a time-homogeneous Markovian chain in a stochastic manner. To address this issue, we propose a novel neuroadaptive distributed dynamic event-triggered control (DETC) strategy. By leveraging stochastic Lyapunov theory and matrix inequality methodology, we establish sufficient conditions for practical ultimate mean square consensus (UMSBC) of MASs using a combination of neural networks (NNs) adaptive control strategy and DETC method. Our approach employs a distributed adaptive NNs DETC mechanism in MASs with unknown nonlinear dynamics and upgrades it at the moment of event sampling in an aperiodic manner, resulting in significant savings in computation and resources. We also exclude the Zeno phenomenon. Finally, we provide numerical examples to demonstrate the feasibility of our proposed approach, which outperforms existing approaches.
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