Fuzzy Neural Network Compensator for Gantry Stage Synchronous Motion

Abstract

In this paper, a fuzzy neural network (FNN) compensator is proposed for the synchronous motion control of a gantry position stage. Firstly, the cascade control strategy is applied to reduce the single axis position tracking error. However, the synchronous error between dual servo systems is inevitable due to their inequality in characteristics and the environmental uncertainties. The FNN compensator and an online learning algorithm perform a fuzzy reasoning with two inputs of synchronous position and velocity errors between dual drive servo systems and generate the compensated force; the compensated force is fed back to the controller of each axis. The online learning algorithm adjusts the connected weighting of the neural network by using a supervised gradient descent methods, such that the define error function can be minimized. Finally, two kinds of position commands with high and low frequency are designed for the experiments, and the experimental results show that the proposed FNN compensator is feasible to improve the synchronous error of gantry stage.