Application of PSO-Optimized generalized CMAC control on linear motor

Authors:
Qiang Zhao;Shaoze Yan
Affiliations:
Department of Precision Instruments and Mechanology, Tsinghua University, Beijing, China;Department of Precision Instruments and Mechanology, Tsinghua University, Beijing, China
Venue:
ISNN'06 Proceedings of the Third international conference on Advnaces in Neural Networks - Volume Part II
Year:
2006

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Abstract

A Gaussian basis function based CMAC (GCMAC) is proposed for the feed-forward control of line motor. It has both the advantages of CMAC and GBF (Gaussian basis function), such as lower memory requirement, faster converging speed and more accurate approximation. Considering that the GCMAC’s parameters selection is crucial for linear motor to get better control performance, we employ a particle swarm optimization algorithm to search for the optimal learning rate of the GCMAC. A numerical example of a linear motor model in wafer stage is preformed. The simulation results verify the effectiveness of the PSO-optimized GCMAC feed-forward controller.