Fully distributed time-varying formation tracking control for linear multi-agent systems with unknown external disturbances

IF 6.5 2区 计算机科学 Q1 AUTOMATION & CONTROL SYSTEMS ISA transactions Pub Date : 2025-01-01 Epub Date: 2024-11-28 DOI:10.1016/j.isatra.2024.11.031
Liya Dou , Kang Wang , Jing Wang , Jinglin Zhou
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Abstract

This work investigates the time-varying formation tracking (TVFT) problem for linear multi-agent systems with a leader of unknown input in the presence of unknown external disturbances on directed graphs. Each agent is subjected to different external disturbances generated by unknown exosystems. To eliminate the unknown external disturbances of each follower, an adaptive disturbance observer and a state observer are constructed first. Then, utilizing the estimated information, a fully distributed TVFT protocol is designed with adaptive coupling parameters such that the global information of the communication topology is not required. By the developed distributed controller, the multi-agent system on a communication topology containing a directed spanning tree can asymptotically track the leader with a desired time-varying formation and simultaneously reject external disturbances in spite of their unknown exosystems. Furthermore, the proposed controller is modified to a continuous one to eliminate the chattering problem. Finally, a simulation example is provided to illustrate the effectiveness of theoretical results.
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具有未知外部干扰的线性多智能体系统的全分布时变编队跟踪控制。
本文研究了有向图上存在未知外部干扰时,具有未知输入先导的线性多智能体系统的时变编队跟踪问题。每种药剂都受到未知外部系统产生的不同外部干扰。首先构造自适应扰动观测器和状态观测器,消除每个从动器的未知外部干扰。然后,利用估计的信息,设计了具有自适应耦合参数的全分布式TVFT协议,从而不需要通信拓扑的全局信息。通过所开发的分布式控制器,在包含有向生成树的通信拓扑上,多智能体系统可以渐近地跟踪具有期望时变队形的领导者,并同时拒绝外部系统未知的外部干扰。进一步,将所提出的控制器修改为连续控制器,以消除抖振问题。最后,通过仿真实例验证了理论结果的有效性。
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来源期刊
ISA transactions
ISA transactions 工程技术-工程:综合
CiteScore
11.70
自引率
12.30%
发文量
824
审稿时长
4.4 months
期刊介绍: ISA Transactions serves as a platform for showcasing advancements in measurement and automation, catering to both industrial practitioners and applied researchers. It covers a wide array of topics within measurement, including sensors, signal processing, data analysis, and fault detection, supported by techniques such as artificial intelligence and communication systems. Automation topics encompass control strategies, modelling, system reliability, and maintenance, alongside optimization and human-machine interaction. The journal targets research and development professionals in control systems, process instrumentation, and automation from academia and industry.
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