Nonlinear-Observer-Based Neural Fault-Tolerant Control for a Rehabilitation Exoskeleton Joint with Electro-Hydraulic Actuator and Error Constraint

Abstract

The rehabilitation exoskeleton is an effective piece of equipment for stroke patients and the aged. However, this complex human–robot system incurs many problems, such as modeling uncertainties, unknown human–robot interaction, external disturbance, and actuator fault. This paper addresses the adaptive fault-tolerant tracking control for a lower limb rehabilitation exoskeleton joint driven by an electro-hydraulic actuator (EHA). First, the model of the exoskeleton joint is built by considering the principle of the hydraulic cylinder and the servo valve. Then, a novel disturbance-observer-based neural fault-tolerant control scheme is proposed, where the neural network and disturbance observer are incorporated to reduce the influence of the the nonlinear uncertainties and disturbance. Meanwhile, a barrier Lyapunov function is constructed to ensure the stability of the closed-loop system. Finally, comparative simulations on an exoskeleton joint validate the effect of the proposed control scheme.