Adaptive iterative learning consensus control for second-order multi-agent systems with unknown control gains

We respond to the consensus control algorithms for second-order non-linear MAS with unknown control gains in the finite time interval by adopting iterative learning control (ILC) methods in this paper. Compared to the current findings, the control gains in the proposed method are unknown functions w...

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Bibliographic Details
Published inNeurocomputing (Amsterdam) Vol. 393; pp. 15 - 26
Main Authors Li, Guilu, Ren, Chang-E, Chen, C.L. Philip, Shi, Zhiping
Format Journal Article
LanguageEnglish
Published Elsevier B.V 14.06.2020
Subjects
Adaptive consensus control
Iterative learning
Multi-agent systems
Neural networks
Unknown control gains
Adaptive consensus control
Unknown control gains
Neural networks
Iterative learning
Multi-agent systems
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Summary:We respond to the consensus control algorithms for second-order non-linear MAS with unknown control gains in the finite time interval by adopting iterative learning control (ILC) methods in this paper. Compared to the current findings, the control gains in the proposed method are unknown functions with unknown and non-identical signs. The topology graph between the follower agents is an undirected connected graph. By referencing the Nussbaum-type function and the neural networks, the adaptive control algorithms are intended to cope with the consensus control between the agents on the limited time interval. Simulation results illustrate the efficacy of the raised algorithms.
ISSN:0925-2312
1872-8286
DOI:10.1016/j.neucom.2020.01.108