不确定马尔可夫跳跃多智能体系统的保证成本无领导共识

IF 1.7 4区 计算机科学 Q3 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Journal of Experimental & Theoretical Artificial Intelligence Pub Date : 2022-02-13 DOI:10.1080/0952813X.2021.1960631
A. Parivallal, R. Sakthivel, Chao Wang
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引用次数: 6

摘要

提出了一种具有时变时滞和不确定性的马尔可夫跳变多智能体系统的可解性方法。本文主要关注的是构建一个具有保证成本函数的状态反馈控制设计,该控制设计既能保证共识,又能保证一定的能量消耗。该代价函数是借助于所有智能体的控制输入和相邻智能体之间的状态误差来设计的。通过构造合适的Lyapunov-Krasovskii泛函(LKF),并借助于Kronecker积性质,利用线性矩阵不等式(lmi)导出了考虑的马尔可夫跳跃多智能体系统(MAS)保证成本一致性的一组新的充分条件。最后,通过数值算例说明了所建立理论结果的有效性。
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Guaranteed cost leaderless consensus for uncertain Markov jumping multi-agent systems
ABSTRACT This paper proposes an approach for the solvability of multi-agent systems with Markov jumps subject to time-varying delay and uncertainties. The primary concern of this paper is to construct a state feedback control design with a guaranteed cost function which not only ensures consensus but also assures certain amount of energy consumption. This cost function is designed with the aid of control inputs of all agents and state error among neighbouring agents. A new set of sufficient conditions for the guaranteed cost consensus of the considered Markov jumping multi-agent system (MAS) is derived in terms of linear matrix inequalities (LMIs) by constructing suitable Lyapunov-Krasovskii functional (LKF) and with the aid of Kronecker product properties. Finally, a numerical example is given to illustrate the effectiveness of the developed theoretical results.
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来源期刊
CiteScore
6.10
自引率
4.50%
发文量
89
审稿时长
>12 weeks
期刊介绍: Journal of Experimental & Theoretical Artificial Intelligence (JETAI) is a world leading journal dedicated to publishing high quality, rigorously reviewed, original papers in artificial intelligence (AI) research. The journal features work in all subfields of AI research and accepts both theoretical and applied research. Topics covered include, but are not limited to, the following: • cognitive science • games • learning • knowledge representation • memory and neural system modelling • perception • problem-solving
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