Simulation experiment of flexible parallel robot control by RBF neural network based on sliding mode robust term

In this paper, RBF neural network control based on sliding mode robust term is proposed to improve the motion precision of wire - driven parallel robot. In the process of the movement for wire-driven parallel robot, a number of uncertain parameters are generated due to the experimental environment, external interference and other factors. According to the uncertainties of these parameters, RBF neural network control is used to calculate the approximation, and the corresponding control law is designed according to the task and nature of the object. Based on the Lyapunov stability theory, the stability of the system is analyzed, and the control law is designed to reduce the approximation error. Finally, through simulation experiments, the simulation results show that the designed control law has good feasibility and reliability.