Lingua: Inglese
Editore: LAP LAMBERT Academic Publishing, 2016
ISBN 10: 3659977411 ISBN 13: 9783659977411
Da: moluna, Greven, Germania
EUR 52,90
Quantità: Più di 20 disponibili
Aggiungi al carrelloCondizione: New.
Lingua: Inglese
Editore: LAP LAMBERT Academic Publishing, 2016
ISBN 10: 3659977411 ISBN 13: 9783659977411
Da: Revaluation Books, Exeter, Regno Unito
EUR 104,83
Quantità: 1 disponibili
Aggiungi al carrelloPaperback. Condizione: Brand New. 204 pages. 8.66x5.91x0.46 inches. In Stock.
Lingua: Inglese
Editore: LAP LAMBERT Academic Publishing Okt 2016, 2016
ISBN 10: 3659977411 ISBN 13: 9783659977411
Da: buchversandmimpf2000, Emtmannsberg, BAYE, Germania
EUR 64,90
Quantità: 2 disponibili
Aggiungi al carrelloTaschenbuch. Condizione: Neu. Neuware -This work deals with the problem of trajectory tracking for a nonlinear system with unknown but bounded model parameters uncertainties. First, this work focuses on the design of classical robust nonlinear model predictive control (RNMPC) law subject to model parameters uncertainties implying solving min-max optimization problem. Secondly, a new approach is proposed, consisting in approaching the basic min-max problem into a more tractable optimization problem based on the use of linearization techniques, to ensure a good trade-off between tracking accuracy and computation time. The robust stability of the closed-loop system is addressed. The developed strategy is applied in simulation to a simplified macroscopic continuous photobioreactor model and is compared to the RNMPC controller. Its efficiency is illustrated through numerical results and robustness against parameter uncertainties.Books on Demand GmbH, Überseering 33, 22297 Hamburg 204 pp. Englisch.
Lingua: Inglese
Editore: LAP LAMBERT Academic Publishing Okt 2016, 2016
ISBN 10: 3659977411 ISBN 13: 9783659977411
Da: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Germania
EUR 64,90
Quantità: 2 disponibili
Aggiungi al carrelloTaschenbuch. Condizione: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -This work deals with the problem of trajectory tracking for a nonlinear system with unknown but bounded model parameters uncertainties. First, this work focuses on the design of classical robust nonlinear model predictive control (RNMPC) law subject to model parameters uncertainties implying solving min-max optimization problem. Secondly, a new approach is proposed, consisting in approaching the basic min-max problem into a more tractable optimization problem based on the use of linearization techniques, to ensure a good trade-off between tracking accuracy and computation time. The robust stability of the closed-loop system is addressed. The developed strategy is applied in simulation to a simplified macroscopic continuous photobioreactor model and is compared to the RNMPC controller. Its efficiency is illustrated through numerical results and robustness against parameter uncertainties. 204 pp. Englisch.
Lingua: Inglese
Editore: LAP LAMBERT Academic Publishing, 2016
ISBN 10: 3659977411 ISBN 13: 9783659977411
Da: AHA-BUCH GmbH, Einbeck, Germania
EUR 64,90
Quantità: 1 disponibili
Aggiungi al carrelloTaschenbuch. Condizione: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - This work deals with the problem of trajectory tracking for a nonlinear system with unknown but bounded model parameters uncertainties. First, this work focuses on the design of classical robust nonlinear model predictive control (RNMPC) law subject to model parameters uncertainties implying solving min-max optimization problem. Secondly, a new approach is proposed, consisting in approaching the basic min-max problem into a more tractable optimization problem based on the use of linearization techniques, to ensure a good trade-off between tracking accuracy and computation time. The robust stability of the closed-loop system is addressed. The developed strategy is applied in simulation to a simplified macroscopic continuous photobioreactor model and is compared to the RNMPC controller. Its efficiency is illustrated through numerical results and robustness against parameter uncertainties.