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Adaptive neural control of nonlinear periodic time-varying parameterized mixed-order multi-agent systems with unknown control coefficients

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Abstract

In this paper, we first consider the adaptive leader-following consensus problem for a class of nonlinear parameterized mixed-order multi-agent systems with unknown control coefficients and time-varying disturbance parameters of the same period. Neural networks and Fourier series expansions are used to describe the unknown nonlinear periodic time-varying parameterized function. A distributed control protocol is designed based on adaptive control, matrix theory, and Nussbaum function. The robustness of the distributed control protocol is analyzed by combining the stability analysis theory of closed-loop systems. On this basis, this paper discusses the case of time-varying disturbance parameters with non-identical periods, expanding the application scope of this control protocol. Finally, the effectiveness of the algorithm is verified by a simulation example.

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Author information

Authors and Affiliations

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

    JiaXi Chen & JunMin Li

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

    WeiSheng Chen

  3. Science and Technology on Antennas and Microwave Laboratory, Xidian University, Xi’an, 710071, China

    Shuai Zhang

Authors
  1. JiaXi Chen
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  2. WeiSheng Chen
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  3. JunMin Li
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  4. Shuai Zhang
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Corresponding authors

Correspondence to WeiSheng Chen or Shuai Zhang.

Additional information

This work was supported by the National Natural Science Foundation of China (Grant Nos. 62063031, 62106186, 62073254, 62103136), the Fundamental Research Funds for the Central Universities (Grant Nos. XJS18012, QTZX22049, XJS220704, and 20101196862), and the Young Talent Fund of University Association for Science and Technology in Shaanxi, China (Grant No. 20180502).

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Chen, J., Chen, W., Li, J. et al. Adaptive neural control of nonlinear periodic time-varying parameterized mixed-order multi-agent systems with unknown control coefficients. Sci. China Technol. Sci. 65, 1675–1684 (2022). https://doi.org/10.1007/s11431-021-2056-5

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  • DOI: https://doi.org/10.1007/s11431-021-2056-5

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