Adaptive Robust of RBF Neural Network Control Based on Model Local Approximation Method for Upper Limb Rehabilitation Robotic Arm

Abstract

For the problem of patient spasm disturbance and random disturbance of external environment in rehabilitation process of upper limb rehabilitation arm, Considering the approximation ability of neural network for arbitrary functions, an adaptive robust of radial basis function (RBF) neural network control algorithm based on model local approximation is proposed. This control algorithm introduces robust term to reduce the approximation error of neural network and the robustness of the rehabilitation manipulator control system is improved. The system can also obtain good track tracking performance under the condition of patient spasm disturbance and random disturbance of external environment. The asymptotic stability of the control system is proved by the stability theory of Lyapunov. Simulation results show that the proposed control algorithm has good control performance.