Fixed-Time Cluster Consensus for Multi-Agent Systems with Objective Optimization on Directed Networks

This paper studies the cluster consensus of multi-agent systems (MASs) with objective optimization on directed and detail balanced networks, in which the global optimization objective function is a linear combination of local objective functions of all agents. Firstly, a directed and detail balanced...

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Veröffentlicht in:Journal of systems science and complexity Jg. 36; H. 6; S. 2325 - 2343
Hauptverfasser: Duan, Suna, Yu, Zhiyong, Jiang, Haijun, Ouyang, Deqiang
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Berlin/Heidelberg Springer Berlin Heidelberg 01.12.2023
Springer Nature B.V
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ISSN:1009-6124, 1559-7067
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Zusammenfassung:This paper studies the cluster consensus of multi-agent systems (MASs) with objective optimization on directed and detail balanced networks, in which the global optimization objective function is a linear combination of local objective functions of all agents. Firstly, a directed and detail balanced network is constructed that depends on the weights of the global objective function, and two kinds of novel continuous-time optimization algorithms are proposed based on time-invariant and time-varying objective functions. Secondly, by using fixed-time stability theory and convex optimization theory, some sufficient conditions are obtained to ensure that all agents’ states reach cluster consensus within a fixed-time, and asymptotically converge to the optimal solution of the global objective function. Finally, two examples are presented to show the efficacy of the theoretical results.
Bibliographie:ObjectType-Article-1
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ISSN:1009-6124
1559-7067
DOI:10.1007/s11424-023-2337-z