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.

Design of Model-Based and Model-Free Robust Control Strategies for Lower Limb Rehabilitation Exoskeletons

“…This desired trajectory is basically a clinical gait pattern provided by the physician, and the exoskeleton as a rehabilitation equipment is used and controlled to ensure that the patients repetitively follow the pattern to train or exercise the muscles. The control strategies used in the literature for trajectory tracking are designed either using the model of the system [19][20][21][22][23][24][25][26][27][28][29][30][31][32][33], the approximated function [34][35][36][37][38][39][40][41][42][43][44], or the combination of both [6]. To ensure tracking, various types of controllers are used.…”

“…Model-free control strategies for efficient tracking performance have been designed in various studies, most of which used neural networks to approximate the function of the system and the control law design. The MF controllers designed for passive rehabilitation include an intelligent adaptive controller [34,41,42], an intelligent robust controller [35,40], and a second-order robust sliding mode controller [39].…”