Iterative Learning Control for Nonlinear Multi-Agent Systems With Initial Shifts

IEEE Access

Dongjie Chen¹  Huiyun Chen¹  Guojun Li¹  Tiantian Lu¹  Yishi Han¹ 

[1] Basic Courses Department, Zhejiang Police College, Hangzhou, China;

关键词: Multi-agent systems;    iterative learning control;    convergence;    step-by-step correction;   

DOI  :  10.1109/ACCESS.2020.3011189

来源: DOAJ

【 摘 要 】

In this paper, a discussion is made on the consensus tracking control by iterative learning method for high-order nonlinear multi-agent systems. Among them, all agents with initial state errors are enabled to perform a given repetitive task over a finite interval. The method proposed can achieve consensus tracking through a series of initial shifts correction actions. In the process of tracking, this algorithm rectifies the initial error of the state x_n of each agent at first, then the error of x_n-1, and so on. All of these rectifying actions are finished in a specified interval. Furthermore, the algorithm has shown effective in the improvement of tracking performance through simulation.

【 授权许可】

Unknown