Efficient robust model predictive control via convex optimization: Efficiently incorporating robustness using linear - Couverture souple

Wan, Zhaoyang ; V. Kothare, Mayuresh

9786202203906: Efficient robust model predictive control via convex optimization: Efficiently incorporating robustness using linear

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ISBN 10 : 6202203900 ISBN 13 : 9786202203906

Editeur : AV Akademikerverlag, 2017

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�diteur: AV Akademikerverlag
Date d'�dition: 2017
Langue: anglais
ISBN 10: 6202203900
ISBN 13: 9786202203906
Reliure: Broch�
Nombre de pages: 168
Coordonn�es du fabricant: non disponible
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9783639086584: EFFICIENT ROBUST MODEL PREDICTIVE CONTROL VIA CONVEX OPTIMIZATION: EFFICIENTLY INCORPORATING ROBUSTNESS USING LINEAR MATRIX INEQUALITIES

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ISBN 10 : 3639086589 ISBN 13 : 9783639086584
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Efficient robust model predictive control via convex optimization

Zhaoyang Wan

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ISBN 10 : 6202203900 ISBN 13 : 9786202203906

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Taschenbuch. Etat : Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -Revision with unchanged content. This research monograph develops a systematic approach to synthesize efficient robust MPC for constrained LTV systems and nonlinear systems. Specifically, (a) by using the concept of invariant sets, robustness is achieved without online computation; (b) by using a two-level control structure, optimization is separated from stabilization; (c) by constructing a continuum of terminal sets, both large operating regions and local optimality can be achieved without large number of control decision variables; (d) by decomposing a nonlinear control problem into a sequence of linear control problems, a nonlinear non-convex optimization problem is reduced to a convex optimization problem. Algorithms developed in this monograph have been formulated into linear objective minimisations subject to linear matrix inequality constraints. This optimization is convex and can be solved efficiently using interior point methods. Since state and decision variables appear linearly in the objective function and the matrix inequality constraints, linear combination of off-line MPC solutions provides a feasible solution, which can potentially replace online optimization in MPC. 168 pp. Englisch. N� de r�f. du vendeur 9786202203906

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Efficient robust model predictive control via convex optimization

Zhaoyang Wan|Mayuresh V. Kothare

Edit� par AV Akademikerverlag, 2017

ISBN 10 : 6202203900 ISBN 13 : 9786202203906

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Etat : New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Autor/Autorin: Wan ZhaoyangZhaoyang Wan is currently at GE Water Technology. He received hisBS and MS degrees at Tsinghua University, China in 1994 and 1997,and his PhD at Lehigh University, USA in 2003.Mayuresh V. Kothare is the R. L. McCann Profe. N� de r�f. du vendeur 173503020

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Efficient robust model predictive control via convex optimization

Wan, Zhaoyang

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Efficient robust model predictive control via convex optimization

Wan, Zhaoyang

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Efficient robust model predictive control via convex optimization

Wan, Zhaoyang

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Efficient robust model predictive control via convex optimization

Zhaoyang Wan

Edit� par AV Akademikerverlag Sep 2017, 2017

ISBN 10 : 6202203900 ISBN 13 : 9786202203906

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Vendeur : buchversandmimpf2000, Emtmannsberg, BAYE, Allemagne

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Taschenbuch. Etat : Neu. Neuware -Revision with unchanged content. This research monograph develops a systematic approach to synthesize efficient robust MPC for constrained LTV systems and nonlinear systems. Specifically, (a) by using the concept of invariant sets, robustness is achieved without online computation; (b) by using a two-level control structure, optimization is separated from stabilization; (c) by constructing a continuum of terminal sets, both large operating regions and local optimality can be achieved without large number of control decision variables; (d) by decomposing a nonlinear control problem into a sequence of linear control problems, a nonlinear non-convex optimization problem is reduced to a convex optimization problem. Algorithms developed in this monograph have been formulated into linear objective minimisations subject to linear matrix inequality constraints. This optimization is convex and can be solved efficiently using interior point methods. Since state and decision variables appear linearly in the objective function and the matrix inequality constraints, linear combination of off-line MPC solutions provides a feasible solution, which can potentially replace online optimization in MPC.VDM Verlag, Dudweiler Landstra�e 99, 66123 Saarbr�cken 168 pp. Englisch. N� de r�f. du vendeur 9786202203906

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Efficient robust model predictive control via convex optimization | Efficiently incorporating robustness using linear

Zhaoyang Wan (u. a.)

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Efficient robust model predictive control via convex optimization : Efficiently incorporating robustness using linear

Zhaoyang Wan

Edit� par AV Akademikerverlag

ISBN 10 : 6202203900 ISBN 13 : 9786202203906

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Vendeur : AHA-BUCH GmbH, Einbeck, Allemagne

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Taschenbuch. Etat : Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - Revision with unchanged content. This research monograph develops a systematic approach to synthesize efficient robust MPC for constrained LTV systems and nonlinear systems. Specifically, (a) by using the concept of invariant sets, robustness is achieved without online computation; (b) by using a two-level control structure, optimization is separated from stabilization; (c) by constructing a continuum of terminal sets, both large operating regions and local optimality can be achieved without large number of control decision variables; (d) by decomposing a nonlinear control problem into a sequence of linear control problems, a nonlinear non-convex optimization problem is reduced to a convex optimization problem. Algorithms developed in this monograph have been formulated into linear objective minimisations subject to linear matrix inequality constraints. This optimization is convex and can be solved efficiently using interior point methods. Since state and decision variables appear linearly in the objective function and the matrix inequality constraints, linear combination of off-line MPC solutions provides a feasible solution, which can potentially replace online optimization in MPC. N� de r�f. du vendeur 9786202203906

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