Conference paper
Intelligent Predictive Control of Nonlienar Processes Using
Department of Automation, Technical University of Denmark1
Department of Informatics and Mathematical Modeling, Technical University of Denmark2
Mathematical Statistics, Department of Informatics and Mathematical Modeling, Technical University of Denmark3
Cognitive Systems, Department of Informatics and Mathematical Modeling, Technical University of Denmark4
This paper presents a novel approach to design of generalized predictive controllers (GPC) for nonlinear processes. A neural network is used for modelling the process and a gain-scheduling type of GPC is subsequently designed. The combination of neural network models and predictive control has frequently been discussed in the neural network community.
This paper proposes an approximate scheme, the approximate predictive control (APC), which facilitates the implementation and gives a substantial reduction in the required amount of computations. The method is based on a technique for extracting linear models from a nonlinear neural network and using them in designing the control system.
The performance of the controller is demonstrated in a simulation study of a pneumatic servo system
| Language: | English |
|---|---|
| Publisher: | IEEE |
| Year: | 1996 |
| Pages: | 301-306 |
| Proceedings: | 1996 IEEE International Symposium on Intelligent Control |
| ISBN: | 0780329783 and 9780780329782 |
| ISSN: | 21589879 and 21589860 |
| Types: | Conference paper |
| DOI: | 10.1109/ISIC.1996.556218 |
| ORCIDs: | Poulsen, Niels Kjølstad , Ravn, Ole and Hansen, Lars Kai |
Buildings Control system synthesis Control systems Intelligent control Intelligent networks Neural networks Nonlinear control systems Predictive control Predictive models Servomechanisms approximate predictive control gain-scheduling intelligent predictive control linearization multilayer perceptron nonlinear control systems nonlinear neural networks nonlinear processes pneumatic servo system
