Dynamic Modeling, Predictive Control and Performance Monitoring : A Data-driven Subspace Approach
A typical design procedure for model predictive control or control performance monitoring consists of: 1. identification of a parametric or nonparametric model; 2. derivation of the output predictor from the model; 3. design of the control law or calculation of performance indices according to the p...
Enregistré dans:
| Auteurs principaux: | , |
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| 格式: | Livre numérique |
| 語言: | Anglais |
| 出版: |
London :
Springer London
2008.
Cham : Springer Nature |
| 叢編: | Lecture Notes in Control and Information Sciences
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| 主題: | |
| 在線閱讀: | Accès sur la plateforme de l'éditeur Accès sur la plateforme Istex Accès Université d'Orléans Accès INSA CVL |
| 提示: |
Archives Springer e-books (Licence nationale) Archives Springer e-books (Licence nationale) |
| Autres localisations: | Voir dans le Sudoc |
| Edition sous un autre format: | • Dynamic Modeling, Predictive Control and Performance Monitoring, Texte imprimé, 9781848007215 • Dynamic modeling, predictive control and performance monitoring, a data-driven subspace approach, Biao Huang, Ramesh Kadali, Berlin, Springer, 2008, 1 vol. (XXIV- 240 p.), Lecture notes in control and information sciences, 978-1-8480-0232-6 |
| 總結: | A typical design procedure for model predictive control or control performance monitoring consists of: 1. identification of a parametric or nonparametric model; 2. derivation of the output predictor from the model; 3. design of the control law or calculation of performance indices according to the predictor. Both design problems need an explicit model form and both require this three-step design procedure. Can this design procedure be simplified? Can an explicit model be avoided? With these questions in mind, the authors eliminate the first and second step of the above design procedure, a data-driven approach in the sense that no traditional parametric models are used; hence, the intermediate subspace matrices, which are obtained from the process data and otherwise identified as a first step in the subspace identification methods, are used directly for the designs. Without using an explicit model, the design procedure is simplified and the modelling error caused by parameterization is eliminated. |
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| Item Description: | Archives Springer e-books (Licence nationale) Archives Springer e-books (Licence nationale) |
| ISBN: | 9781848002333 |
| ISSN: | 1610-7411 |
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