The design of control mechanisms is impeded by the obstacle of accurately depicting the behavior of nonlinear systems with backlash input. A data-driven form of iterative learning control algorithm based on sliding mode feedback is proposed. To overcome the problem of prolonged convergence time of conventional model-free adaptive iterative learning control algorithms due to the open-loop control of on time axis, we introduce a sliding mode feedback control algorithm, which can achieve double-closed-loop operation on both the time and iteration axes. The robustness of the presented control algorithm has been explored and the feasibility and superiority of the algorithm is validated.

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Model-Free Adaptive Iterative Learning Control Based on Sliding Mode Feedback for Nonlinear Systems with Backlash Input

  • Xingxing Zhou,
  • Yanhao Zhang,
  • Jianguo Dong,
  • Xiaona Liu,
  • Tianyu Yu,
  • Yumeng Zhong

摘要

The design of control mechanisms is impeded by the obstacle of accurately depicting the behavior of nonlinear systems with backlash input. A data-driven form of iterative learning control algorithm based on sliding mode feedback is proposed. To overcome the problem of prolonged convergence time of conventional model-free adaptive iterative learning control algorithms due to the open-loop control of on time axis, we introduce a sliding mode feedback control algorithm, which can achieve double-closed-loop operation on both the time and iteration axes. The robustness of the presented control algorithm has been explored and the feasibility and superiority of the algorithm is validated.