RBF Neural Network Based Sliding Mode Control of a Lower Limb Exoskeleton Suit

Abstract

A new force tracking control algorithm of partial actuated lower limb exoskeleton suit, which is designed for enhancing human motion is presented in this paper. Firstly, a mathematical model of the electro-hydraulic servo system was created, and equations for the frictions in the hydraulic valve and actuator were obtained. Secondly, the appropriate observer based on the estimated functions and the measurement error equations are presented for the sliding mode control algorithm. Thirdly, a sliding mode controller with applicable surface coefficient has been designed for force tracking control of the servo system. Fourthly, so as to reduce the error caused by the unchangeable surface of the sliding mode control, a radial basis functions (RBF) neural network control algorithm has been introduced to offset the disadvantage of the sliding mode control by moving the sliding surface effectively. Finally, the simulation results under conditions of different frequencies and the trial results based on the human motion of sliding mode control and the RBF based sliding control are presented, which indicate that RBF based sliding control provides a better performance than regular sliding mode control.