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Q-learning based fault estimation and fault tolerant iterative learning control for MIMO systems.

Rui Wang1, Zhihe Zhuang1, Hongfeng Tao1

  • 1Key Laboratory of Advanced Process Control for Light Industry (Ministry of Education), Jiangnan University, Wuxi 214122, China.

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Summary

This study introduces a Q-learning based fault estimation and fault tolerant control method for iterative learning control systems. The approach adapts to changing actuator faults, enhancing control performance in repetitive tasks.

Keywords:
Fault estimationFault tolerant controlIterative learning controlMIMO systemsQ-learning

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Area of Science:

  • Robotics
  • Control Systems Engineering
  • Artificial Intelligence

Background:

  • Iterative learning control (ILC) is sensitive to actuator faults, especially unknown faults that vary over time and trials.
  • These faults significantly challenge the control performance and reliability of ILC systems in repetitive tasks.

Purpose of the Study:

  • To develop a robust fault estimation (FE) and fault tolerant control (FTC) scheme for ILC systems facing actuator faults.
  • To enhance the adaptability and performance of ILC systems through intelligent fault management.

Main Methods:

  • A Q-learning algorithm is employed for adaptive fault estimation (FE), enabling continuous adjustment of the estimator to changing fault dynamics.
  • A norm-optimal iterative learning control (NOILC) framework is utilized for fault tolerant control (FTC).
  • The FTC controller is dynamically adjusted based on the FE results provided by the Q-learning algorithm to counteract fault influences.

Main Results:

  • The proposed Q-learning based FE and FTC scheme effectively adapts to time-varying and trial-varying actuator faults.
  • The integration of Q-learning for FE significantly improves the robustness and performance of the NOILC framework.
  • Simulations on a mobile robot platform demonstrate the practical effectiveness and reliability of the proposed fault management strategy.

Conclusions:

  • The developed Q-learning based FE and FTC scheme provides a powerful solution for addressing actuator fault challenges in ILC systems.
  • This approach enhances the resilience and operational stability of control systems performing repetitive tasks, particularly in dynamic fault scenarios.