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Distributed formation control for multiple non-holonomic wheeled mobile robots with velocity constraint by using improved data-driven iterative learning

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  • Hou, Rui
  • Cui, Lizhi
  • Bu, Xuhui
  • Yang, Junqi

Abstract

In this paper, a distributed proportional-integral data-driven iterative learning control (PI-DDILC) algorithm is developed to achieve the formation problem for non-holonomic and velocity constrained wheeled mobile robots (WMRs) under repeatable operation environment. Firstly, the formation problem is transformed into a tracking problem with a certain deviation from the reference trajectory. And a distributed kinematics control algorithm is designed by using leader-follower strategy and graph theory. Then, to solve the problem of unknown dynamic model, the relationship between WMR’s output and input is first derived by utilizing the iteration-domain dynamical linearization technique. After that, the improved data-driven iterative learning dynamics control algorithm is provided, which includes both proportional and integral terms along the iteration axis. This algorithm can ensure a group of WMRs to realize formation and only use I/O data of each WMR. Compared with DDILC, PI-DDILC can significantly enhance the response speed and transient performance of WMR system formation. The excellence of the improved algorithm is certified by simulation, and a performance index is designed to quantify the results of the two on formation performance.

Suggested Citation

  • 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).
    • Handle: RePEc:eee:apmaco:v:395:y:2021:i:c:s0096300320307827
    DOI: 10.1016/j.amc.2020.125829
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    References listed on IDEAS

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    1. 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).
    2. Peng, Zhinan & Hu, Jiangping & Shi, Kaibo & Luo, Rui & Huang, Rui & Ghosh, Bijoy Kumar & Huang, Jiuke, 2020. "A novel optimal bipartite consensus control scheme for unknown multi-agent systems via model-free reinforcement learning," Applied Mathematics and Computation, Elsevier, vol. 369(C).
    3. Xia, Yude & Wang, Jing & Meng, Bo & Chen, Xiangyong, 2020. "Further results on fuzzy sampled-data stabilization of chaotic nonlinear systems," Applied Mathematics and Computation, Elsevier, vol. 379(C).
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    Cited by:

    1. Miranda-Colorado, Roger, 2022. "Observer-based proportional integral derivative control for trajectory tracking of wheeled mobile robots with kinematic disturbances," Applied Mathematics and Computation, Elsevier, vol. 432(C).

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