Neural Network Based Direct Optimizing Predictive Control With On-Line Pid Gradient Optimization

Document Type

Article

Publication Date

1-1-2001

Publication Title

Intelligent Automation and Soft Computing

Volume

7

Issue

2

First Page

107

Keywords

Neural networks, Nonlinear system, Optimisation, PID control, Predictive control

Last Page

123

Abstract

In this paper, a neural network model-based predictive control has been developed to solve problems of nonlinear process control. In the proposed control scheme, a neural network model with recurrent connections is employed to describe nonlinear dynamic processes. Based on the neural network model, a nonlinear d-step-ahead predictor is constructed. The nonlinear predictive control is directly formulated as an on-line nonlinear programming problem (NLP). To improve the performance of the back-propagation algorithm, a PID instantaneous gradient descent optimisation algorithm, as motivated by the Proportional-Integral-Differential (PID) control strategy, is proposed for the on-line NLP. The applications of the nonlinear predictive control scheme to nonlinear processes including a continuous-stirred-tank-reactor (CSTR) is finally presented. © 2001 TSI® Press.

DOI

10.1080/10798587.2000.10642810

ISSN

10798587

E-ISSN

2326005X

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