Robustification of nonlinear model predictive control - Tapa blanda

Benattia, Seif Eddine

9783659977411: Robustification of nonlinear model predictive control

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ISBN 10: 3659977411 ISBN 13: 9783659977411

Editorial: LAP LAMBERT Academic Publishing, 2016

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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.

Biograf�a del autor

Seif Eddine Benattia received the Ph.D. degree from CentraleSup�lec, France, in 2016. Since 2013, he has been with the Signal and Systems Laboratory in the Automatic Control Department. His current research deals with robust predictive methods for control and applications to bioprocesses (modelling and control).

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EditorialLAP LAMBERT Academic Publishing
A�o de publicaci�n2016
ISBN 10 3659977411
ISBN 13 9783659977411
Encuadernaci�nTapa blanda
IdiomaIngl�s
N�mero de p�ginas204

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Robustification of nonlinear model predictive control

Seif Eddine Benattia

Publicado por LAP LAMBERT Academic Publishing Okt 2016, 2016

ISBN 10: 3659977411 ISBN 13: 9783659977411

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Librería: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Alemania

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Taschenbuch. Condici�n: 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. N� de ref. del art�culo: 9783659977411

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Robustification of nonlinear model predictive control

Seif Eddine Benattia

Publicado por LAP LAMBERT Academic Publishing, 2016

ISBN 10: 3659977411 ISBN 13: 9783659977411

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Librería: AHA-BUCH GmbH, Einbeck, Alemania

Calificaci�n del vendedor: 5 de 5 estrellas

Taschenbuch. Condici�n: 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. N� de ref. del art�culo: 9783659977411

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