Neural network adaptive robust control based on dead time compensation

Abstract

Executive body of the dead nonlinear has greater influence on the system's performance. In this paper, the dead zone compensation of RBF network adaptive robust control were designed by using the RBF neural network instead of classic compensator of BP network. It can greatly reduce the system parameters and also make the network initialization work clear. GL and the GL matrix multiplication operator were introduced and thus mathematically rigorous proof of the n section joint robot system stability. The simulation results show that this method has good tracking performance and strong robustness.