Adaptive neural network control for a hydraulic knee exoskeleton with valve deadband and output constraint based on nonlinear disturbance observer

Abstract

This paper presents a novel disturbance observer-based adaptive neural network control for a hydraulic knee exoskeleton with valve deadband and output constraint. Adaptive neural networks are employed to approximate the unknown nonlinearities of the hydraulic actuator, i.e., the valve deadband and the unmodeled dynamics caused by the valve leakage. A disturbance observer is designed and integrated into the controller to compensate for the external disturbance and the equivalent interactive force acted on the piston rod of the hydraulic actuator. Under the framwork of backstepping technique, both the state feedback and output feedback controllers of the exoskeleton are designed. The velocity of the piston rod is estimated via a high gain observer in the output feedback control design. By utilizing the barrier Lyapunov function method and the proposed control, the output constraints are handled and the semi-globally uniformly boundedness of the closed-loop system is also guaranteed. Comparative simulation results demonstrate the tracking performance of the proposed control approach.