Tracking control of a precision stage with NARX neural network for friction compensation

Abstract

The objective of this study is to develop a neural network controller for the friction compensation. The purpose models are used as an inverse model of the frictional force and dynamic behaviour of a system. A proportional-integral-derivative (PID) controller and a neural network system architecture are developed for the Nonlinear Autoregressive with Exogenous inputs (NARX) neural network were proposed to control a precision stage. Firstly, a test signal was used to drive the stage then the derived data was used to train a NARX neural network. This neural network model is the inverse dynamic model of the stages and friction force. An architectural approach of NARX showing promising qualities for dynamic system applications, is analysed in this paper. Utilization of this model is as an estimate of the driving force related with the dynamics of the system against displacement, and is then used as a feed-forward controller to compensate for friction errors. Finally, the experimental systems are established and the result shows that the combination of PID and NARX can improve the tracking performance of the precision stage.