Neural network based model predictive control for a steel pickling process
A multi-layer feedforward neural network model based predictive control scheme is developed for a multivariable nonlinear steel pickling process in this paper. In the acid baths three variables under controlled are the hydrochloric acid concentrations. The baths exhibit the normal features of an ind...
| Main Authors: | , , , |
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| Format: | Article |
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Elsevier
2009
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| _version_ | 1825719314217435136 |
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| author | Kittisupakorn, P. Thitiyasook, P. Hussain, Mohd Azlan Daosud, W. |
| author_facet | Kittisupakorn, P. Thitiyasook, P. Hussain, Mohd Azlan Daosud, W. |
| author_sort | Kittisupakorn, P. |
| collection | UM |
| description | A multi-layer feedforward neural network model based predictive control scheme is developed for a multivariable nonlinear steel pickling process in this paper. In the acid baths three variables under controlled are the hydrochloric acid concentrations. The baths exhibit the normal features of an industrial system such as nonlinear dynamics and multi-effects among variables. In the modeling, multiple input, single-output recurrent neural network subsystem models are developed using input-output data sets obtaining from mathematical model simulation. The Levenberg-Marquardt algorithm is used to train the process models. In the control (MPC) algorithm, the feedforward neural network models are used to predict the state variables over a prediction horizon within the model predictive control algorithm for searching the optimal control actions via sequential quadratic programming. The proposed algorithm is tested for control of a steel pickling process in several cases in simulation such as for set point tracking, disturbance, model mismatch and presence of noise. The results for the neural network model predictive control (NNMPC) overall show better performance in the control of the system over the conventional PI controller in all cases. |
| first_indexed | 2024-03-06T05:17:47Z |
| format | Article |
| id | um.eprints-7034 |
| institution | Universiti Malaya |
| last_indexed | 2024-03-06T05:17:47Z |
| publishDate | 2009 |
| publisher | Elsevier |
| record_format | dspace |
| spelling | um.eprints-70342020-02-19T01:51:02Z http://eprints.um.edu.my/7034/ Neural network based model predictive control for a steel pickling process Kittisupakorn, P. Thitiyasook, P. Hussain, Mohd Azlan Daosud, W. TA Engineering (General). Civil engineering (General) TP Chemical technology A multi-layer feedforward neural network model based predictive control scheme is developed for a multivariable nonlinear steel pickling process in this paper. In the acid baths three variables under controlled are the hydrochloric acid concentrations. The baths exhibit the normal features of an industrial system such as nonlinear dynamics and multi-effects among variables. In the modeling, multiple input, single-output recurrent neural network subsystem models are developed using input-output data sets obtaining from mathematical model simulation. The Levenberg-Marquardt algorithm is used to train the process models. In the control (MPC) algorithm, the feedforward neural network models are used to predict the state variables over a prediction horizon within the model predictive control algorithm for searching the optimal control actions via sequential quadratic programming. The proposed algorithm is tested for control of a steel pickling process in several cases in simulation such as for set point tracking, disturbance, model mismatch and presence of noise. The results for the neural network model predictive control (NNMPC) overall show better performance in the control of the system over the conventional PI controller in all cases. Elsevier 2009 Article PeerReviewed Kittisupakorn, P. and Thitiyasook, P. and Hussain, Mohd Azlan and Daosud, W. (2009) Neural network based model predictive control for a steel pickling process. Journal of Process Control, 19 (4). pp. 579-590. ISSN 0959-1524, DOI https://doi.org/10.1016/j.jprocont.2008.09.003 <https://doi.org/10.1016/j.jprocont.2008.09.003>. https://doi.org/10.1016/j.jprocont.2008.09.003 doi:10.1016/j.jprocont.2008.09.003 |
| spellingShingle | TA Engineering (General). Civil engineering (General) TP Chemical technology Kittisupakorn, P. Thitiyasook, P. Hussain, Mohd Azlan Daosud, W. Neural network based model predictive control for a steel pickling process |
| title | Neural network based model predictive control for a steel pickling process |
| title_full | Neural network based model predictive control for a steel pickling process |
| title_fullStr | Neural network based model predictive control for a steel pickling process |
| title_full_unstemmed | Neural network based model predictive control for a steel pickling process |
| title_short | Neural network based model predictive control for a steel pickling process |
| title_sort | neural network based model predictive control for a steel pickling process |
| topic | TA Engineering (General). Civil engineering (General) TP Chemical technology |
| work_keys_str_mv | AT kittisupakornp neuralnetworkbasedmodelpredictivecontrolforasteelpicklingprocess AT thitiyasookp neuralnetworkbasedmodelpredictivecontrolforasteelpicklingprocess AT hussainmohdazlan neuralnetworkbasedmodelpredictivecontrolforasteelpicklingprocess AT daosudw neuralnetworkbasedmodelpredictivecontrolforasteelpicklingprocess |