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Efficient robust model predictive control via convex optimization: Efficiently incorporating robustness using linear - Softcover
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Efficient robust model predictive control via convex optimization
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AV Akademikerverlag Sep 2017, 2017
ISBN 10: 6202203900
ISBN 13: 9786202203906
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Seller: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Germany
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Taschenbuch. Condition: 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. Seller Inventory # 9786202203906
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Efficient robust model predictive control via convex optimization
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Condition: 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. Seller Inventory # 173503020
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Efficient robust model predictive control via convex optimization
ISBN 10: 6202203900
ISBN 13: 9786202203906
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Efficient robust model predictive control via convex optimization
ISBN 10: 6202203900
ISBN 13: 9786202203906
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Efficient robust model predictive control via convex optimization
ISBN 10: 6202203900
ISBN 13: 9786202203906
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Efficient robust model predictive control via convex optimization
Published by
AV Akademikerverlag Sep 2017, 2017
ISBN 10: 6202203900
ISBN 13: 9786202203906
New
Taschenbuch
Seller: buchversandmimpf2000, Emtmannsberg, BAYE, Germany
Seller rating 5 out of 5 stars
Taschenbuch. Condition: Neu. This item is printed on demand - Print on Demand Titel. 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 Landstraße 99, 66123 Saarbrücken 168 pp. Englisch. Seller Inventory # 9786202203906
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Efficient robust model predictive control via convex optimization : Efficiently incorporating robustness using linear
Print on DemandSeller: AHA-BUCH GmbH, Einbeck, Germany
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Taschenbuch. Condition: 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. Seller Inventory # 9786202203906
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Efficient robust model predictive control via convex optimization | Efficiently incorporating robustness using linear
Seller: preigu, Osnabrück, Germany
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Taschenbuch. Condition: Neu. Efficient robust model predictive control via convex optimization | Efficiently incorporating robustness using linear | Zhaoyang Wan (u. a.) | Taschenbuch | Englisch | 2017 | AV Akademikerverlag | EAN 9786202203906 | Verantwortliche Person für die EU: preigu GmbH & Co. KG, Lengericher Landstr. 19, 49078 Osnabrück, mail[at]preigu[dot]de | Anbieter: preigu. Seller Inventory # 113379443