Learning estimator‐based fault‐tolerant control for robot manipulators with partial loss of actuator effectiveness

Abstract This paper proposes a fault‐tolerant control approach for robot manipulators, addressing challenges such as parameter uncertainties, external disturbances, control input limitations, and partial loss of actuator effectiveness. As an essential step, an improved learning estimator is designed to identify actuator faults by introducing a sign function. Based on the estimated fault information, a fault‐tolerant control law is subsequently developed. To address the challenge of control input saturation, a saturation compensation mechanism is integrated into the control law. Finally, numerical simulations are performed to verify the performance and efficacy of the proposed learning estimator and control strategy, confirming its feasibility and robustness under various fault conditions.