首页|Neural networks-based iterative learning control consensus for periodically time-varying multi-agent systems

Neural networks-based iterative learning control consensus for periodically time-varying multi-agent systems

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In this paper,the problem of adaptive iterative learning based consensus control for periodically time-varying multi-agent systems is studied,in which the dynamics of each follower are driven by nonlinearly parameterized terms with periodic disturbances.Neural networks and Fourier base expansions are introduced to describe the periodically time-varying dynamic terms.On this basis,an adaptive learning parameter with a positively convergent series term is constructed,and a distributed control protocol based on local signals between agents is designed to ensure accurate consensus of the closed-loop systems.Furthermore,con-sensus algorithm is generalized to solve the formation control problem.Finally,simulation experiments are implemented through MATLAB to demonstrate the effectiveness of the method used.

multi-agent systemsadaptive iterative learning controlnonlinearly parameterized dynamicsFourier series expansionneural networks

CHEN JiaXi、LI JunMin、CHEN WeiSheng、GAO WeiFeng

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School of Mathematics and Statistics,Xidian University,Xi'an 710071,China

School of Aerospace Science and Technology,Xidian University,Xi,an 710071,China

National Natural Science Foundation of ChinaNational Natural Science Foundation of ChinaNational Natural Science Foundation of ChinaNational Natural Science Foundation of ChinaNational Natural Science Foundation of ChinaFundamental Research Funds for the Central UniversitiesFundamental Research Funds for the Central UniversitiesFundamental Research Funds for the Central UniversitiesChina Postdoctoral Science FoundationNatural Science Basic Research Program of ShaanxiNatural Science Basic Research Program of Shaanxi

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2024

10.1007/s11431-023-2464-1

中国科学:技术科学(英文版)
中国科学院

中国科学:技术科学(英文版)

CSTPCDEI
影响因子：1.056
ISSN：1674-7321
年,卷(期)：2024.67(2)
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