Multi-step Ahead Prediction Analysis for MPC-relevant Models
Model predictive control (MPC) is one of the most successful controllers in industries and widely applied in petroleum refining and petrochemical processes. Its inherent model-based strategy, however, renders it sensitive to changes that occur when the plants operate outside the boundaries of its or...
| Main Authors: | H., Zabiri, M., Ramasamy, Lemma D, Tufa, Maulud, Abdulhalim |
|---|---|
| Format: | Conference or Workshop Item |
| Institution: | Universiti Teknologi Petronas |
| Record Id / ISBN-0: | utp-eprints.10750 / |
| Published: |
2013
|
| Subjects: | |
| Online Access: |
http://eprints.utp.edu.my/10750/1/HZb_paper107.pdf http://eprints.utp.edu.my/10750/ |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| Summary: |
Model predictive control (MPC) is one of the most successful controllers in industries and widely applied in petroleum refining and petrochemical processes. Its inherent model-based strategy, however, renders it sensitive to changes that occur when the plants operate outside the boundaries of its original operating conditions. In this paper, a nonlinear empirical model based on parallel orthonormal basis function-neural networks structure, which has been shown to be able to extend the applicable regions of the model, is evaluated for its multi-step ahead prediction capability and compared to the conventional neural networks models with different scaling procedures. It has been shown that the nonlinear model exhibited sufficient multi-step ahead prediction capability that renders it a promising candidate for MPC applications that can potentially improve the closed-loop control performance in extended regions and this is important in retaining the positive benefits of MPC in industries. |
|---|