离散多智能体系统的区间观测器协调控制

IF 8.6 1区 计算机科学 Q1 AUTOMATION & CONTROL SYSTEMS
Miaohong Luo;Housheng Su;Wei Xing Zheng
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引用次数: 0

摘要

本文研究了受未知初始状态和外界干扰影响的离散多智能体系统的协调控制问题。受单系统构造的区间观测器的启发,给出了离散时间质量的分布式区间观测器的定义,其中通过求解一个改进的代数Riccati方程得到的每个agent的控制协议依赖于与自身及其邻居相连的区间观测器的有界信息。利用协同性理论和Lyapunov稳定性理论,建立了分布式区间观测器不仅可以在任意时刻获得MASs的一些信息,即智能体状态各分量的上界和下界,而且可以在涉及网络同步和系统矩阵特征值不稳定的一些必要条件下实现MASs的协同行为。此外,借助新的时变变换矩阵,构造新的区间观测器,消除非负约束。最后,通过两个数值模拟验证了所得结果的有效性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Interval Observer-Based Coordination Control for Discrete-Time Multi-Agent Systems
In this article, the coordination control problem of discrete-time multiagent systems (MASs) affected by uncertainties, namely unknown initial states and external disturbances, is considered. Inspired by the interval observer constructed by the single system, the definition of distributed interval observer for discrete-time MASs is given, in which the control protocol of each agent obtained by solving a modified algebraic Riccati equation depends on the bounded information of the interval observer connected to itself and its neighbors. By the cooperativity theory and Lyapunov stability theory, it is established that the distributed interval observer can not only access some information about MASs at any instant, that is, the upper and lower bounds of each component of the agent state, but also realize the cooperative behavior of MASs under some essential conditions involving network synchronization and the unstable eigenvalue of the system matrix. In addition, with the help of a new time-varying transformation matrix, the new interval observer is constructed to eliminate the non-negative constraint. Finally, two numerical simulations are provided to confirm the validity of the derived results.
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来源期刊
IEEE Transactions on Systems Man Cybernetics-Systems
IEEE Transactions on Systems Man Cybernetics-Systems AUTOMATION & CONTROL SYSTEMS-COMPUTER SCIENCE, CYBERNETICS
CiteScore
18.50
自引率
11.50%
发文量
812
审稿时长
6 months
期刊介绍: The IEEE Transactions on Systems, Man, and Cybernetics: Systems encompasses the fields of systems engineering, covering issue formulation, analysis, and modeling throughout the systems engineering lifecycle phases. It addresses decision-making, issue interpretation, systems management, processes, and various methods such as optimization, modeling, and simulation in the development and deployment of large systems.
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