Event-triggered iterative learning control for nonlinear multi-agent systems with data random packet dropouts

被引：0

作者：

Wang H.-W. ^{[1
,2
]}

Li H.-Z. ^{[2
]}

机构：

[1] School of Control Science and Engineering, Dalian University of Technology, Liaoning, Dalian

[2] School of Electrical Engineering, Xinjiang University, Xinjiang, Urumqi

来源：

Kongzhi Lilun Yu Yingyong/Control Theory and Applications | 2022年 / 39卷 / 09期

基金：

中国国家自然科学基金;

关键词：

consensus; event-triggering communication; iterative learning control; nonlinear multi-agent systems; random link failures;

D O I：

10.7641/CTA.2022.10849

中图分类号：

学科分类号：

摘要：

An even-driven distributed model-free iterative learning control strategy is proposed to solve the consensus problem of nonlinear multi-agent with random link packet loss, limited communication bandwidth and unknown dynamics. Firstly, the event-driven decision-making mechanism of the system is established, and the communication trigger condition based on output information is given. When the condition is met, the event is triggered, and the agents communicate, and when the condition is not met, the agents do not communicate, which can effectively reduce a large amount of communication and energy dissipation between agents. Secondly, the pseudo partial derivative is used to dynamically linearize the nonlinear system along the iterative axis, the random link packet loss compensation mechanism is designed with the help of the neighbor’s output information when the previous event is triggered, and then the distributed control protocol is designed combined with the event-driven communication mechanism. The convergence performance of the algorithm is analyzed by using the principle of compressed mapping. The simulation results show that with the increase of iteration times, the event trigger interval becomes larger, and all agents will complete the tracking of the desired trajectory. © 2022 South China University of Technology. All rights reserved.

引用

页码：1688 / 1698

页数：10

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