Scholarlink

<p>Concentrated on the off-policy reinforcement learning method, this paper explores a model-free algorithm for addressing the robust fault-tolerant tracking problem in discrete-time linear systems with time-varying actuator faults and model uncertainties. Specifically, to determine the feedback control input, a dynamic optimization approach is developed based on measured data rather than exact information from system dynamics. Subsequently, a static optimization approach is established using solutions from the preceding dynamic optimization problem to compute the feedforward control input. Finally, numerical simulations are conducted to illustrate the feasibility and efficiency of the proposed solution.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Robust Fault-tolerant Tracking Control for Linear Discrete-time Systems via Reinforcement Learning Method

  • 期刊论文

  • 发表日期：

    2025年2月5日

查看全文
  • Ngoc Hoai An Nguyen,
  • Sung Hyun Kim

摘要

Concentrated on the off-policy reinforcement learning method, this paper explores a model-free algorithm for addressing the robust fault-tolerant tracking problem in discrete-time linear systems with time-varying actuator faults and model uncertainties. Specifically, to determine the feedback control input, a dynamic optimization approach is developed based on measured data rather than exact information from system dynamics. Subsequently, a static optimization approach is established using solutions from the preceding dynamic optimization problem to compute the feedforward control input. Finally, numerical simulations are conducted to illustrate the feasibility and efficiency of the proposed solution.