An RBF-based neuro-adaptive control scheme to drive a lower limb rehabilitation robot

Abstract

In this paper, a novel robust adaptive control scheme is proposed for a lower limb rehabilitation robot designed by our laboratory. The proposed control strategy is based on the radial basis function (RBF) neural networks and the parameters of the system dynamics are unknown. The weights of the RBF neural networks are updated by an adaptive law according to the Lyapunov stability analysis. The robustness against possible variations of the system dynamics and the external disturbance are considered in the control design. The proposed control strategy can not only avoid the complex procedure of system parameters identification, but also guarantee high robustness, small trajectory tracking errors and the assistance of the patient's voluntary participation. Using this control algorithm, the robot can regulate its exerted torque to adapt to the patient's active torque in real time during rehabilitation. The effectiveness of our control method is demonstrated by a simulation.