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Open-Closed-Loop PD Iterative Learning Control with a Variable Forgetting Factor for a Two-Wheeled Self-Balancing Mobile Robot

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  • Jian Dong
  • Bin He
  • Chenghong Zhang
  • Gang Li

Abstract

A novel iterative learning control (ILC) algorithm for a two-wheeled self-balancing mobile robot with time-varying, nonlinear, and strong-coupling dynamics properties is presented to resolve the trajectory tracking problem in this research. A kinematics model and dynamic model of a two-wheeled self-balancing mobile robot are deduced in this paper, and the combination of an open-closed-loop PD-ILC law and a variable forgetting factor is presented. The open-closed-loop PD-ILC algorithm adopts current and past learning items to drive the state variables and input variables, and the output variables converge to the bounded scope of their desired values. In addition, introducing a variable forgetting factor can enhance the robustness and stability of ILC. Numerous simulation and experimental data demonstrate that the proposed control scheme has better feasibility and effectiveness than the traditional control algorithm.

Suggested Citation

  • Jian Dong & Bin He & Chenghong Zhang & Gang Li, 2019. "Open-Closed-Loop PD Iterative Learning Control with a Variable Forgetting Factor for a Two-Wheeled Self-Balancing Mobile Robot," Complexity, Hindawi, vol. 2019, pages 1-11, August.
    • Handle: RePEc:hin:complx:5705126
    DOI: 10.1155/2019/5705126
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