Nonlinear predictive control based on wavelet neural network applied to polypropylene process

Authors:
Xiaohua Xia;Zhiyan Luan;Dexian Huang;Yihui Jin
Affiliations:
Process Control Lab, Automation Department, Tsinghua University, Beijing, China;Process Control Lab, Automation Department, Tsinghua University, Beijing, China;Process Control Lab, Automation Department, Tsinghua University, Beijing, China;Process Control Lab, Automation Department, Tsinghua University, Beijing, China
Venue:
ISNN'05 Proceedings of the Second international conference on Advances in Neural Networks - Volume Part III
Year:
2005

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Abstract

A nonlinear adaptive predictive control strategy using orthogonal wavelet network model is presented. Based on a set of orthogonal wavelet functions, wavelet neural network performs a nonlinear mapping from the network input space to the wavelons output space in hidden layer. Its weight coefficients can be simply estimated by a linear least-square estimation algorithm. The excellent statistic properties of the weight parameters of wavelet network also can be obtained. A single input single output (SISO) nonlinear predictive control strategy is implemented in the simulation of a Polypropylene process.