Model reference adaptive PID control of hydraulic parallel robot based on RBF neural network

Abstract

In this paper, to improve the control performance of hydraulic parallel robot, we develop a model reference adaptive PID control based on radial basis function (RBF) neural network. To compensate for the asymmetry of the hydraulic actuator, we adopt model reference adaptive control strategy. Moreover, a RBF neural network is used to identify the hydraulic servo system on-line and then regulate the PID parameters on-line, which makes the system more adaptive. Simulation results show the controller has good tracking performance and good robustness, so the control strategy presented in this paper is effective.