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

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.