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Consensus control for heterogeneous uncertain multi-agent systems with hybrid nonlinear dynamics via iterative learning algorithm

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Abstract

In this study, We propose a compensated distributed adaptive learning algorithm for heterogeneous multi-agent systems with repetitive motion, where the leader’s dynamics are unknown, and the controlled system’s parameters are uncertain. The multi-agent systems are considered a kind of hybrid order nonlinear systems, which relaxes the strict requirement that all agents are of the same order in some existing work. For theoretical analyses, we design a composite energy function with virtual gain parameters to reduce the restriction that the controller gain depends on global information. Considering the stability of the controller, we introduce a smooth continuous function to improve the piecewise controller to avoid possible chattering. Theoretical analyses prove the convergence of the presented algorithm, and simulation experiments verify the effectiveness of the algorithm.

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Authors and Affiliations

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

    Jin Xie, 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. Jin Xie
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  2. JiaXi Chen
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  3. JunMin Li
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  4. WeiSheng Chen
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  5. Shuai Zhang
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Corresponding author

Correspondence to JiaXi Chen.

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 No. 2023-JC-YB-585).

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Xie, J., Chen, J., Li, J. et al. Consensus control for heterogeneous uncertain multi-agent systems with hybrid nonlinear dynamics via iterative learning algorithm. Sci. China Technol. Sci. 66, 2897–2906 (2023). https://doi.org/10.1007/s11431-023-2411-2

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  • DOI: https://doi.org/10.1007/s11431-023-2411-2

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