Adaptive sliding mode repetitive learning control of the upper-limb exoskeleton with unknown dynamics

Abstract

This article proposes a new adaptive sliding mode repetitive learning control strategy. The proposed controller can obtain satisfactory position tracking performance in the presence of unknown dynamics and external disturbance. The unknown dynamics parameters of the exoskeleton system can be estimated via an adaptive algorithm, which is used to design the sliding mode control law. Besides, the periodic external disturbance of the system can be compensated by repetitive learning to reduce the tracking error. The stability of the proposed method is demonstrated rigorous by the Lyapunov theory. Using an upper-limb exoskeleton model, simulation results demonstrate the effectiveness of the control strategy. The proposed method has a better control performance than other methods.