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

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Abstract

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.

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Authors and Affiliations

  1. School of Mathematics and Statistics, Xidian University, Xi’an, 710071, China

    JiaXi Chen, JunMin Li & WeiFeng Gao

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

    WeiSheng Chen

Authors
  1. JiaXi Chen
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  2. JunMin Li
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  3. WeiSheng Chen
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  4. WeiFeng Gao
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Corresponding author

Correspondence to WeiFeng Gao.

Additional information

This work was supported by the National Natural Science Foundation of China (Grant Nos. 62203342, 62073254, 92271101, 62106186, and 62103136), the Fundamental Research Funds for the Central Universities (Grant Nos. XJS220704, QTZX23003, and ZYTS23046), the Project funded by China Postdoctoral Science Foundation (Grant No. 2022M712489), and the Natural Science Basic Research Program of Shaanxi (Grant Nos. 2023-JC-YB-585 and 2020JM-188).

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Chen, J., Li, J., Chen, W. et al. Neural networks-based iterative learning control consensus for periodically time-varying multi-agent systems. Sci. China Technol. Sci. 67, 464–474 (2024). https://doi.org/10.1007/s11431-023-2464-1

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  • DOI: https://doi.org/10.1007/s11431-023-2464-1

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