Neural Networks: A Comprehensive Foundation
Neural Networks: A Comprehensive Foundation
Model Predictive Control in the Process Industry
Model Predictive Control in the Process Industry
Neural Networks for Modelling and Control of Dynamic Systems: A Practitioner's Handbook
Neural Networks for Modelling and Control of Dynamic Systems: A Practitioner's Handbook
Identification of Nonlinear Systems Using Neural Networks and Polynomial Models: A Block-Oriented Approach (Lecture Notes in Control and Information Sciences)
A Family of Model Predictive Control Algorithms With Artificial Neural Networks
International Journal of Applied Mathematics and Computer Science
Application of fuzzy Wiener models in efficient MPC algorithms
RSCTC'10 Proceedings of the 7th international conference on Rough sets and current trends in computing
Precise and computationally efficient nonlinear predictive control based on neural wiener models
ISMIS'11 Proceedings of the 19th international conference on Foundations of intelligent systems
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This paper describes a nonlinear Model Predictive Control (MPC) scheme in which a neural Wiener model of a multivariable process is used. The model consists of a linear dynamic part in series with a steady-state nonlinear part represented by neural networks. A linear approximation of the model is calculated on-line and used for prediction. Thanks to it, the control policy is calculated from a quadratic programming problem. Good control accuracy and computational efficiency of the discussed algorithm are shown in the control system of a chemical reactor for which the classical MPC strategy based on a linear model is unstable.