Sliding Mode Control Algorithm of Upper Limb Exoskeleton Rehabilitation Robot Based on RBF Neural Network

Aiming at the nonlinear and uncertain problems of upper limb exoskeleton rehabilitation robot (ULERR) during passive training, a sliding model controller based on radial basis neural network is designed in this paper. Firstly, a four-degree-of-freedom ULERR is designed for stroke patients in soft paralysis and spasticity, and a kinetic model was established. Secondly, RBF neural network is used to approximate the uncertainty caused by spastic disturbance of patients in the system. The weight in the neural network is replaced by a single parameter, and the adaptive algorithm is easy to adjust and has strong real-time performance. The asymptotic stability of the controller is verified by Lyapunov theorem. Finally, the desired training trajectory of the upper limb is obtained by a three-dimensional motion capture system, and the simulation experiments are carried out with Matlab software to prove that the proposed control method solves the chattering problem of traditional sliding mode control, to meet the control requirements of real-time rehabilitation training.