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Distributed learning consensus control based on neural networks for heterogeneous nonlinear multiagent systems
Author(s) -
Shen Dong,
Zhang Chao,
Xu JianXin
Publication year - 2019
Publication title -
international journal of robust and nonlinear control
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.361
H-Index - 106
eISSN - 1099-1239
pISSN - 1049-8923
DOI - 10.1002/rnc.4627
Subject(s) - convergence (economics) , artificial neural network , nonlinear system , computer science , multi agent system , iterative learning control , consensus , control theory (sociology) , function (biology) , control (management) , artificial intelligence , physics , quantum mechanics , evolutionary biology , economics , biology , economic growth
Summary This paper considers a novel distributed iterative learning consensus control algorithm based on neural networks for the control of heterogeneous nonlinear multiagent systems. The system's unknown nonlinear function is approximated by suitable neural networks; the approximation error is countered by a robust term in the control. Two types of control algorithms, both of which utilize distributed learning laws, are provided to achieve consensus. In the provided control algorithms, the desired reference is considered to be an unknown factor and then estimated using the associated learning laws. The consensus convergence is proven by the composite energy function method. A numerical simulation is ultimately presented to demonstrate the efficacy of the proposed control schemes.