Sliding Mode Control of Electro-hydraulic Servo System for Lower-limb Exoskeleton Based on RBF Neural Network

Exoskeleton has drawn a great deal of attention recently because it can augment human strength and track human's motion. The objective of this paper is to enhance the performance of the electro-hydraulic servo system of the lower-limb exoskeleton, including model uncertainties and load disturbance. To accomplish the objective, a hybrid control method, combining sliding mode controller with RBF neural network, and disturbance observer is presented. The sliding mode controller is the main controller to control the electrohydraulic servo system to track the desired trajectory. The RBF neural network is used to compensate the model uncertainties, and the disturbance observer is designed to deduce the external unknown load force. The simulation results show that the electrohydraulic Servo System of the exoskeleton can achieve a better tracking performance with the proposed controller.