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
Recommended Citation
Tan, Y.; Van Cauwenberghe, A. R.; and Saif, M.. (2001). Neural Network Based Direct Optimizing Predictive Control With On-Line Pid Gradient Optimization. Intelligent Automation and Soft Computing, 7 (2), 107-123.
https://scholar.uwindsor.ca/electricalengpub/419