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| Content Provider | Springer Nature Link |
|---|---|
| Author | M’Sahli, F. Matlaya, R. |
| Copyright Year | 2004 |
| Abstract | Neural networks can be considered to be new modelling tools in process control and especially in non-linear dynamical systems cases. Their ability to approximate non-linear functions has been very often demonstrated and tested by simulation and experimental studies. In this paper, a predictive control strategy of a semi-batch reactor based on neural network models is proposed. Results of a non-linear control of the reactant temperature of a semi-batch reactor are presented. The process identification is composed of an off-line phase that consists in training the network, and of an on-line phase that corresponds to the neural model adaptation so that it fits any modification of the process dynamics. Experimental results when using this method to control a semi-batch reactor are reported and show the great potential of this strategy in controlling non-linear processes. |
| Starting Page | 161 |
| Ending Page | 168 |
| Page Count | 8 |
| File Format | |
| ISSN | 02683768 |
| Journal | The International Journal of Advanced Manufacturing Technology |
| Volume Number | 26 |
| Issue Number | 1-2 |
| e-ISSN | 14333015 |
| Language | English |
| Publisher | Springer-Verlag |
| Publisher Date | 2004-03-29 |
| Publisher Place | Berlin, Heidelberg |
| Access Restriction | One Nation One Subscription (ONOS) |
| Subject Keyword | Neural networks Non-linear control Predictive control Reactor Computer-Aided Engineering (CAD, CAE) and Design Mechanical Engineering Industrial and Production Engineering Production/Logistics |
| Content Type | Text |
| Resource Type | Article |
| Subject | Industrial and Manufacturing Engineering Control and Systems Engineering Mechanical Engineering Computer Science Applications Software |
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