Improved Adaptive Neural Network Control of Rehabilitation Robot with Variable Load

Abstract

This article presents an adaptive-adaptive radial basis function (RBF) neural network (AANNRBF) control strategy for lower limb rehabilitation robot (LLRR), which to adapts the uncertainty of the physical parameters caused by different patients or the same patient's muscle tension changes in different training periods. The load disturbance changes and the uncertainty of the system physical parameters generated in the human-machine interaction are controlled by parameter adaptive control, which solves the problem that the neural net¬work approximation domain of the traditional purely adaptive RBF neural network (ANNRBF) control algorithm is easily a¬ffected by the change of the load. ADAMS (automatic dynamic analysis of mechanical system)-MATLAB co-simulation results show that the control method proposed can achieve accurate gait trajectory tracking training, and has good adaptability and robustness to system load fluctuations caused by changes in muscle tension during patient training.