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Consensus tracking for nonlinear multi-agent systems with unknown disturbance by using model free adaptive iterative learning control

Author

Listed:
  • Wang, Yingchun
  • Li, Haifeng
  • Qiu, Xiaojie
  • Xie, Xiangpeng

Abstract

In this paper, a distributed disturbance-compensation based model free adaptive iterative learning control (MFAILC) algorithm is proposed to achieve the consensus tracking of nonlinear multi-agent systems (MAS) with unknown disturbance. Here, both fixed and iteration varying topologies are considered. A general dynamic linearization model with disturbance input is first proposed to each agent along the iteration axis for nonlinear MAS. Due to the existence of unknown disturbance, an online disturbance estimation algorithm is proposed to estimate actual disturbance only based on the input/output (I/O) measurement data. Then, a distributed MFAILC method with disturbance compensation is developed such that consensus tracking errors are convergent. Last, the effectiveness of the developed method can be illustrated from the simulation examples.

Suggested Citation

  • Wang, Yingchun & Li, Haifeng & Qiu, Xiaojie & Xie, Xiangpeng, 2020. "Consensus tracking for nonlinear multi-agent systems with unknown disturbance by using model free adaptive iterative learning control," Applied Mathematics and Computation, Elsevier, vol. 365(C).
    • Handle: RePEc:eee:apmaco:v:365:y:2020:i:c:s0096300319306939
    DOI: 10.1016/j.amc.2019.124701
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    Cited by:

    1. Zhao, Huarong & Peng, Li & Yu, Hongnian, 2022. "Quantized model-free adaptive iterative learning bipartite consensus tracking for unknown nonlinear multi-agent systems," Applied Mathematics and Computation, Elsevier, vol. 412(C).
    2. Qi, Yiwen & Qu, Ziyu & Yao, Zhaohui & Zhao, Xiujuan & Tang, Yiwen, 2023. "Event-Triggered iterative learning control for asynchronously switched systems," Applied Mathematics and Computation, Elsevier, vol. 440(C).
    3. Zhu, Lin & Che, Wei-Wei & Jin, Xiao-Zheng, 2022. "Dynamic event-triggered tracking control for model-free networked control systems," Applied Mathematics and Computation, Elsevier, vol. 431(C).
    4. Zhang, Weijian & Du, Haibo & Chu, Zhaobi, 2022. "Robust discrete-time non-smooth consensus protocol for multi-agent systems via super-twisting algorithm," Applied Mathematics and Computation, Elsevier, vol. 413(C).
    5. Hou, Rui & Cui, Lizhi & Bu, Xuhui & Yang, Junqi, 2021. "Distributed formation control for multiple non-holonomic wheeled mobile robots with velocity constraint by using improved data-driven iterative learning," Applied Mathematics and Computation, Elsevier, vol. 395(C).

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