Computationally Efficient Model Predictive Control Algorithms - Couverture rigide

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9783319042282: Computationally Efficient Model Predictive Control Algorithms

Couverture rigide

ISBN 10 : 3319042289 ISBN 13 : 9783319042282

Editeur : Springer, 2014

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This book thoroughly discusses computationally efficient (suboptimal) Model Predictive Control (MPC) techniques based on neural models. The subjects treated include:

- A few types of suboptimal MPC algorithms in which a linear approximation of the model or of the predicted trajectory is successively calculated on-line and used for prediction.

- Implementation details of the MPC algorithms for feed forward perceptron neural models, neural Hammerstein models, neural Wiener models and state-space neural models.

- The MPC algorithms based on neural multi-models (inspired by the idea of predictive control).

- The MPC algorithms with neural approximation with no on-line linearization.

- The MPC algorithms with guaranteed stability and robustness.

- Cooperation between the MPC algorithms and set-point optimization.

Thanks to linearization (or neural approximation), the presented suboptimal algorithms do not require demanding on-line nonlinear optimization. The presented simulation results demonstrate high accuracy and computational efficiency of the algorithms. For a few representative nonlinear benchmark processes, such as chemical reactors and a distillation column, for which the classical MPC algorithms based on linear models do not work properly, the trajectories obtained in the suboptimal MPC algorithms are very similar to those given by the ``ideal'' MPC algorithm with on-line nonlinear optimization repeated at each sampling instant. At the same time, the suboptimal MPC algorithms are significantly less computationally demanding.

Les informations fournies dans la section � Synopsis � peuvent faire r�f�rence � une autre �dition de ce titre.

Pr�sentation de l'�diteur

This book discusses computationally efficient (suboptimal) Model Predictive Control (MPC) techniques based on neural models. Covers feed forward perceptron neural models, neural Hammerstein models and more with high accuracy and computational efficiency.

Les informations fournies dans la section � A propos du livre � peuvent faire r�f�rence � une autre �dition de ce titre.

�diteur: Springer
Date d'�dition: 2014
Langue: anglais
ISBN 10: 3319042289
ISBN 13: 9783319042282
Reliure: Reli�
Nombre de pages: 340
Coordonn�es du fabricant: non disponible

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Computationally Efficient Model Predictive Control Algorithms: A Neural Network Approach: 3 (Studies in Systems, Decision and Control)

?awry?czuk

Edit� par Springer, 2014

ISBN 10 : 3319042289 ISBN 13 : 9783319042282

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Hardcover. Etat : Sehr gut. Unread book in excellent condition. Language - English. Ships from Berlin. N� de r�f. du vendeur ABE-1703364172460

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Computationally Efficient Model Predictive Control Algorithms

Maciej Lawrynczuk

Edit� par Springer International Publishing, 2014

ISBN 10 : 3319042289 ISBN 13 : 9783319042282

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Gebunden. Etat : New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Presents recent research in Computationally Efficient Model Predictive Control AlgorithmsFocuses on a Neural Network Approach for Model Predictive ControlWritten by an expert in the fieldThis book thoroughly discusses computation. N� de r�f. du vendeur 4496917

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Computationally Efficient Model Predictive Control Algorithms : A Neural Network Approach

Maciej �Awry�Czuk

Edit� par Springer International Publishing, 2014

ISBN 10 : 3319042289 ISBN 13 : 9783319042282

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Vendeur : AHA-BUCH GmbH, Einbeck, Allemagne

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Buch. Etat : Neu. Druck auf Anfrage Neuware - Printed after ordering - This book thoroughly discusses computationally efficient (suboptimal) Model Predictive Control (MPC) techniques based on neural models. The subjects treated include: A few types of suboptimal MPC algorithms in which a linear approximation of the model or of the predicted trajectory is successively calculated on-line and used for prediction. Implementation details of the MPC algorithms for feed forward perceptron neural models, neural Hammerstein models, neural Wiener models and state-space neural models. The MPC algorithms based on neural multi-models (inspired by the idea of predictive control). The MPC algorithms with neural approximation with no on-line linearization. The MPC algorithms with guaranteed stability and robustness. Cooperation between the MPC algorithms and set-point optimization.Thanks to linearization (or neural approximation), the presented suboptimal algorithms do not require demanding on-line nonlinear optimization. The presented simulation results demonstrate high accuracy and computational efficiency of the algorithms. For a few representative nonlinear benchmark processes, such as chemical reactors and a distillation column, for which the classical MPC algorithms based on linear models do not work properly, the trajectories obtained in the suboptimal MPC algorithms are very similar to those given by the ``ideal'' MPC algorithm with on-line nonlinear optimization repeated at each sampling instant. At the same time, the suboptimal MPC algorithms are significantly less computationally demanding. N� de r�f. du vendeur 9783319042282

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Computationally Efficient Model Predictive Control Algorithms

Maciej �Awry�Czuk

Edit� par Springer International Publishing Feb 2014, 2014

ISBN 10 : 3319042289 ISBN 13 : 9783319042282

Neuf Couverture rigide

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Vendeur : BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Allemagne

�valuation du vendeur 5 sur 5 �toiles

Buch. Etat : Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -This book thoroughly discusses computationally efficient (suboptimal) Model Predictive Control (MPC) techniques based on neural models. The subjects treated include: A few types of suboptimal MPC algorithms in which a linear approximation of the model or of the predicted trajectory is successively calculated on-line and used for prediction. Implementation details of the MPC algorithms for feed forward perceptron neural models, neural Hammerstein models, neural Wiener models and state-space neural models. The MPC algorithms based on neural multi-models (inspired by the idea of predictive control). The MPC algorithms with neural approximation with no on-line linearization. The MPC algorithms with guaranteed stability and robustness. Cooperation between the MPC algorithms and set-point optimization.Thanks to linearization (or neural approximation), the presented suboptimal algorithms do not require demanding on-line nonlinear optimization. The presented simulation results demonstrate high accuracy and computational efficiency of the algorithms. For a few representative nonlinear benchmark processes, such as chemical reactors and a distillation column, for which the classical MPC algorithms based on linear models do not work properly, the trajectories obtained in the suboptimal MPC algorithms are very similar to those given by the ``ideal'' MPC algorithm with on-line nonlinear optimization repeated at each sampling instant. At the same time, the suboptimal MPC algorithms are significantly less computationally demanding. 340 pp. Englisch. N� de r�f. du vendeur 9783319042282

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Computationally Efficient Model Predictive Control Algorithms

Maciej �Awry�Czuk

Edit� par Springer International Publishing, Springer International Publishing Feb 2014, 2014

ISBN 10 : 3319042289 ISBN 13 : 9783319042282

Neuf Couverture rigide

Vendeur : buchversandmimpf2000, Emtmannsberg, BAYE, Allemagne

�valuation du vendeur 5 sur 5 �toiles

Buch. Etat : Neu. Neuware -This book thoroughly discusses computationally efficient (suboptimal) Model Predictive Control (MPC) techniques based on neural models. The subjects treated include: A few types of suboptimal MPC algorithms in which a linear approximation of the model or of the predicted trajectory is successively calculated on-line and used for prediction. Implementation details of the MPC algorithms for feed forward perceptron neural models, neural Hammerstein models, neural Wiener models and state-space neural models. The MPC algorithms based on neural multi-models (inspired by the idea of predictive control). The MPC algorithms with neural approximation with no on-line linearization. The MPC algorithms with guaranteed stability and robustness. Cooperation between the MPC algorithms and set-point optimization.Thanks to linearization (or neural approximation), the presented suboptimal algorithms do not require demanding on-line nonlinear optimization. The presented simulation results demonstrate high accuracy and computational efficiency of the algorithms. For a few representative nonlinear benchmark processes, such as chemical reactors and a distillation column, for which the classical MPC algorithms based on linear models do not work properly, the trajectories obtained in the suboptimal MPC algorithms are very similar to those given by the ``ideal'' MPC algorithm with on-line nonlinear optimization repeated at each sampling instant. At the same time, the suboptimal MPC algorithms are significantly less computationally demanding.Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg 340 pp. Englisch. N� de r�f. du vendeur 9783319042282

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