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Adaptive fuzzy leader-following consensus for nonlinear multi-agent systems via state-constraint impulsive control

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Abstract

This paper investigates the adaptive fuzzy leader-following consensus problem of multi-agent systems (MASs) with unknown nonlinear dynamics via state-constraint impulsive control. The fuzzy logic systems(FLSs) are established to estimate the unknown nonlinear dynamics. Meanwhile, the purpose of designing one adaptive parameter is to reduce the impact on uncertain factors of FLSs, where this parameter is constantly adjusted by exchanging required information between follower agents and its neighbors. The impulsive control theory is applied to reduce the cost of continuous communication due to the achievement of discontinuous control, where follower agents only communicate with leader agent at fixed impulsive instants. To consider the physical or environmental constraints in real control systems, the state-constraint based on saturation function is introduced to MASs. Then, both adaptive fuzzy control and state-constraint impulsive control are employed to guarantee that total agents can converge to consensus. Finally, some numerical simulations are given to illustrate the feasibility of the theoretical results.

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Acknowledgements

This work was supported by National Natural Science Foundation of China (61633011,61873213), and in part by National Key Research and Development Project (2018AAA0100101) and Chongqing Graduate Research and Innovation Project (No. CYS19076).

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Authors and Affiliations

  1. Chongqing Key Laboratory of Nonlinear Circuits and Intelligent Information Processing, College of Electronic and Information Engineering, Southwest University, Chongqing, 400715, China

    Can Ke, Chuandong Li & Le You

  2. School of Artificial Intelligence and Automation, Huazhong University of Science and Technology, Hubei, 430074, China

    Yiyan Han

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  1. Can Ke
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  2. Chuandong Li
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  3. Yiyan Han
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  4. Le You
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Correspondence to Chuandong Li.

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Ke, C., Li, C., Han, Y. et al. Adaptive fuzzy leader-following consensus for nonlinear multi-agent systems via state-constraint impulsive control. Int. J. Mach. Learn. & Cyber. 12, 3011–3022 (2021). https://doi.org/10.1007/s13042-021-01392-8

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  • DOI: https://doi.org/10.1007/s13042-021-01392-8

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