Dynamic Modeling and Neural Network Self‐tuning PID Control Design for a Linear Motor Driving Platform
Sheng‐Ping Hsieh, Thong‐Shing Hwang · IEEJ Transactions on Electrical and Electronic Engineering · 2010
Abstract The purpose of this paper is to design a wavelet neural network self‐tuning proportional–integral–derivative (PID) controller for solving the problem of fast changing payload in a linear motor in the 6‐DOF platform system. The wavelet neural network PID controller demonstrates the special capabilities of fast response and self‐tuning. A practical system identification method is proposed to identify the dynamic model of a linear motor by using the balancing realization theorem and inverse discrete Fourier transform. The experimental results of the real‐time software simulation and hardware‐in‐the‐loop simulation demonstrate the novel ability of self‐tuning PID controller for adapting the quick load change in the forcer of linear motor, which activates the platform system. Copyright © 2010 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.