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Date of Publication:2011-01-01

Journal:化工学报

Issue:8

Page Number:2275-2280

ISSN No.:0438-1157

Abstract:A model predictive control strategy based on neural network is presented for a continuous stirred tank reactor(CSTR). A segmentation method was adopted to identify Hammerstein-Wiener model coefficient by least squares support vector machines and then to construct a nonlinear predictive controller which was by a linear optimal component and radial basis function neural networks in series. A nonlinear predictive control algorithm based on least support vector machines Hammerstein-Wiener model was realized by using BP neural network to train predictive input sequences and to solve nonlinear predictive control rules by Quasi-Newton method. The simulation results of CSTR illustrate that this approach is effective tracking and controlling product concentration. © All Rights Reserved.

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