Adaptive Fuzzy Event-Triggered Optimized Consensus Control for Delayed Unknown Stochastic Nonlinear Multi-Agent Systems Using Simplified ADP

作     者：Zhang, Guoping Liang, Chengbin Zhu, Quanxin 

作者机构：Guizhou Univ Sch Math & Stat Guiyang 550025 Guizhou Peoples R China Guizhou Univ Coll Elect Engn Guiyang 550025 Guizhou Peoples R China Hunan Normal Univ Sch Math & Stat Changsha 410081 Hunan Peoples R China 

出 版 物：《IEEE TRANSACTIONS ON AUTOMATION SCIENCE AND ENGINEERING》 (IEEE Trans. Autom. Sci. Eng.)

年 卷 期：2025年第22卷

页      面：11780-11793页

核心收录：

学科分类：0808[工学-电气工程] 08[工学] 0811[工学-控制科学与工程] 

基　　金：National Natural Science Foundation of China Guizhou Provincial Key Technology Research and Development Program [2024 General 136] Guizhou Provincial Basic Research Program (Natural Science) [ZK General 015, ZK General 600] Natural Science Special Research Foundation of Guizhou University Talent Introduction Research Program of Guizhou University Natural Science Foundation of Hunan Province [2023JJ30388] 

主　　题：Stochastic processes Optimal control Fuzzy logic Dynamic programming Consensus control Artificial neural networks Vehicle dynamics Uncertainty Mathematical models Training Stochastic multiagent systems optimized consensus control event-triggered control identifier-actor-critic architecture fuzzy logic systems adaptive dynamic programming 

摘      要：This paper investigates the adaptive fuzzy event-triggered optimized consensus tracking control problem for uncertain stochastic nonlinear multi-agent systems (MASs) with unknown dynamic and time-delay. Typically, optimal control is derived by the solution of the Hamilton-Jacobi-Bellman (HJB) equation, but it is usually challenging to solve this equation since inherent nonlinearity and unknown dynamics. Specifically, the complexity of the MASs in controller design is further exacerbated by the issue of state interdependence. To achieve optimized consensus control, the adaptive dynamic programming strategy is derived using the negative gradient of a simple positive function. As a result, the designed optimized consensus tracking control is relatively simple and can eliminate the persistence excitation (PE) assumption. The fuzzy logic systems (FLSs) are utilized to approximate the current and delayed states of unknown nonlinear functions;the identifier is proposed to estimate the stochastic multi-agent dynamic;the critic and actor FLSs are designed to evaluate control performance and execute control behavior, respectively. Furthermore, the event-triggered control (ETC) method is developed to save transmission load and communication resources. Moreover, we demonstrate that all signals for the MASs are semi-globally uniformly ultimately bounded (SGUUB) in mean square, and that the states of the follower agents can reach consensus with the leader s state. Finally, a numerical example is illustrated to demonstrate the effectiveness of the proposed method. Note to Practitioners-With environmental protection and energy conservation becoming dominant trends, improving efficiency is regarded as a fundamental principle in control design. In addition, MASs are widely affected by factors such as stochastic disturbance, model unknown, time-delay, and uncertain factors, which further increase the difficulty of controller design. In this work, the optimized control approach is deve