Simulation and experimental implementation of a neural-network-based internal-model control strategy on a reactor system
The use of inverse-model-based control strategy for nonlinear system has been increasing lately. However it is hampered by the difficulty in obtaining the inverse of nonlinear systems analytically. Since neural networks has the ability to model such inverses, it has become a viable alternative. Alth...
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| Format: | Article |
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Taylor & Francis
1999
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| author | Hussain, Mohd Azlan Kershenbaum, L.S. |
| author_facet | Hussain, Mohd Azlan Kershenbaum, L.S. |
| author_sort | Hussain, Mohd Azlan |
| collection | UM |
| description | The use of inverse-model-based control strategy for nonlinear system has been increasing lately. However it is hampered by the difficulty in obtaining the inverse of nonlinear systems analytically. Since neural networks has the ability to model such inverses, it has become a viable alternative. Although many simulations using neural network inverse models for controls have been reported recently, no actual experimental application has been reported on a reactor system. In this paper we describe a novel experimental application of a neural network inverse-model based control method on a partially simulated pilot plant reactor, exhibiting steady stale parametric sensitivity and designed to test the use of such nonlinear algorithms. The implementation involved the control of the reactor temperature under set point changes, disturbance rejection and set point regulation with plant/model mismatches. Simulation tests on the model of the system were also carried out to enable better design of the neural network models and to highlight the differences between simulation and actual online results. The online implementation results obtained were sufficient to demonstrate the capability of applying these neural-network-based control methods in real systems. |
| first_indexed | 2024-03-06T05:17:56Z |
| format | Article |
| id | um.eprints-7095 |
| institution | Universiti Malaya |
| last_indexed | 2024-03-06T05:17:56Z |
| publishDate | 1999 |
| publisher | Taylor & Francis |
| record_format | dspace |
| spelling | um.eprints-70952019-05-09T04:30:15Z http://eprints.um.edu.my/7095/ Simulation and experimental implementation of a neural-network-based internal-model control strategy on a reactor system Hussain, Mohd Azlan Kershenbaum, L.S. TA Engineering (General). Civil engineering (General) TP Chemical technology The use of inverse-model-based control strategy for nonlinear system has been increasing lately. However it is hampered by the difficulty in obtaining the inverse of nonlinear systems analytically. Since neural networks has the ability to model such inverses, it has become a viable alternative. Although many simulations using neural network inverse models for controls have been reported recently, no actual experimental application has been reported on a reactor system. In this paper we describe a novel experimental application of a neural network inverse-model based control method on a partially simulated pilot plant reactor, exhibiting steady stale parametric sensitivity and designed to test the use of such nonlinear algorithms. The implementation involved the control of the reactor temperature under set point changes, disturbance rejection and set point regulation with plant/model mismatches. Simulation tests on the model of the system were also carried out to enable better design of the neural network models and to highlight the differences between simulation and actual online results. The online implementation results obtained were sufficient to demonstrate the capability of applying these neural-network-based control methods in real systems. Taylor & Francis 1999 Article PeerReviewed Hussain, Mohd Azlan and Kershenbaum, L.S. (1999) Simulation and experimental implementation of a neural-network-based internal-model control strategy on a reactor system. Chemical Engineering Communications, 172. pp. 151-169. ISSN 0098-6445, DOI https://doi.org/10.1080/00986449908912768 <https://doi.org/10.1080/00986449908912768>. http://www.tandfonline.com/doi/pdf/10.1080/00986449908912768 doi:10.1080/00986449908912768 |
| spellingShingle | TA Engineering (General). Civil engineering (General) TP Chemical technology Hussain, Mohd Azlan Kershenbaum, L.S. Simulation and experimental implementation of a neural-network-based internal-model control strategy on a reactor system |
| title | Simulation and experimental implementation of a neural-network-based internal-model control strategy on a reactor system |
| title_full | Simulation and experimental implementation of a neural-network-based internal-model control strategy on a reactor system |
| title_fullStr | Simulation and experimental implementation of a neural-network-based internal-model control strategy on a reactor system |
| title_full_unstemmed | Simulation and experimental implementation of a neural-network-based internal-model control strategy on a reactor system |
| title_short | Simulation and experimental implementation of a neural-network-based internal-model control strategy on a reactor system |
| title_sort | simulation and experimental implementation of a neural network based internal model control strategy on a reactor system |
| topic | TA Engineering (General). Civil engineering (General) TP Chemical technology |
| work_keys_str_mv | AT hussainmohdazlan simulationandexperimentalimplementationofaneuralnetworkbasedinternalmodelcontrolstrategyonareactorsystem AT kershenbaumls simulationandexperimentalimplementationofaneuralnetworkbasedinternalmodelcontrolstrategyonareactorsystem |