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Adaptive neural consensus tracking control for multi-agent systems with unknown state and input hysteresis

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Abstract

An indirect adaptive consensus control method is presented for multi-agent systems (MASs) with unknown hysteresis states and input. All system states that can be utilized to design the controller are measured by the sensors subjected to hysteresis, and thus, the system state values are inaccurate. Meanwhile, it is difficult to compensate the input hysteresis for it is coupled with the state hysteresis. The unknown function from agent’s neighbors also increases the difficulty of controller design. To eliminate the influence of unknown input hysteresis, an inverse adaptive compensated method is presented. The problem of state hysteresis is addressed by designing two adaptive laws to approximate the upper and lower bounds of unknown hysteresis coefficient. Neural networks are introduced to handle the unknown dynamics of agent and its neighbors. The proposed control scheme can guarantee that the consensus errors of followers converge to a predefined interval of zero asymptotically. In addition, the transient performance of MASs can be further ensured. The simulation examples are included to verify the effectiveness of the presented control approach.

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Acknowledgements

This work was supported in part by Guangdong Province Universities and Colleges Pearl River Scholar Funded Scheme, and in part by the National Key Research and Development Program of China under Project 2020AAA0108303.

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Authors and Affiliations

  1. School of Automation, Guangdong University of Technology, Guangzhou, Guangdong, 510006, China

    Zhuangbi Lin, Zhi Liu & Yun Zhang

  2. Faculty of Computer Science and Engineering, South China University of Technology, Guangzhou, Guangdong, 510006, China

    C. L. Philip Chen

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  1. Zhuangbi Lin
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Correspondence to Zhi Liu.

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Lin, Z., Liu, Z., Zhang, Y. et al. Adaptive neural consensus tracking control for multi-agent systems with unknown state and input hysteresis. Nonlinear Dyn 105, 1625–1641 (2021). https://doi.org/10.1007/s11071-021-06652-4

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