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Optimal Sliding Mode Fault-Tolerant Control for Multiple Robotic Manipulators via Critic-Only Dynamic Programming.

Xiaoguang Zhang1,2, Zhou Yang1,2, Haitao Liu1,2,3

  • 1School of Mechanical Engineering, Guangdong Ocean University, Zhanjiang 524088, China.

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|September 13, 2025
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Summary
This summary is machine-generated.

This study introduces optimal sliding mode fault-tolerant control for robotic manipulators, enhancing performance and efficiency. The new method significantly reduces position errors and control torque, improving dynamic response in complex environments.

Keywords:
adaptive gain integral terminal sliding mode control (AGITSMC)critic-only neural network optimal dynamic programming (CNNODP)fault-tolerant controlmultiple robotic manipulatorsquantitative prescribed performance control (QPPC)

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Area of Science:

  • Robotics and Control Systems
  • Artificial Intelligence

Background:

  • Robotic manipulators face challenges from external disturbances and actuator faults.
  • Existing control methods may have limitations in performance and computational efficiency.

Purpose of the Study:

  • To develop an optimal sliding mode fault-tolerant control strategy for multiple robotic manipulators.
  • To enhance robustness against disturbances and actuator faults while minimizing computational cost.

Main Methods:

  • Quantitative Prescribed Performance Control (QPPC) to manage trajectory constraints.
  • Adaptive Gain Integral Terminal Sliding Mode Control (AGITSMC) for improved anti-interference.
  • Critic-Only Neural Network Optimal Dynamic Programming (CNNODP) for efficient learning of optimal control policies.

Main Results:

  • Reduced maximum position error by up to 25%.
  • Decreased peak control torque by up to 16.5%.
  • Demonstrated system boundedness and stability using Lyapunov stability theorem.

Conclusions:

  • The proposed control strategy effectively improves dynamic response accuracy and energy efficiency.
  • CNNODP offers optimal control with lower computational expenses.
  • The method provides a robust solution for fault-tolerant control of robotic systems.