Nonlinear Predictive Control Using Wiener Models: Computationally Efficient Approaches for Polynomial and Neural Structures (Studies in Systems, Decision and Control, Band 389) - Softcover

Buch 346 von 378: Studies in Systems, Decision and Control

Ławryńczuk, Maciej

 
9783030838171: Nonlinear Predictive Control Using Wiener Models: Computationally Efficient Approaches for Polynomial and Neural Structures (Studies in Systems, Decision and Control, Band 389)
Softcover
ISBN 10: 303083817X ISBN 13: 9783030838171
Verlag: Springer, 2022

Inhaltsangabe

This book presents computationally efficient MPC solutions. The classical model predictive control (MPC) approach to control dynamical systems described by the Wiener model uses an inverse static block to cancel the influence of process nonlinearity. Unfortunately, the model's structure is limited, and it gives poor control quality in the case of an imperfect model and disturbances. An alternative is to use the computationally demanding MPC scheme with on-line nonlinear optimisation repeated at each sampling instant.

A linear approximation of the Wiener model or the predicted trajectory is found on-line. As a result, quadratic optimisation tasks are obtained. Furthermore, parameterisation using Laguerre functions is possible to reduce the number of decision variables. Simulation results for ten benchmark processes show that the discussed MPC algorithms lead to excellent control quality. For a neutralisation reactor and a fuel cell, essential advantages ofneural Wiener models are demonstrated.

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Von der hinteren Coverseite

This book presents computationally efficient MPC solutions. The classical model predictive control (MPC) approach to control dynamical systems described by the Wiener model uses an inverse static block to cancel the influence of process nonlinearity. Unfortunately, the model's structure is limited, and it gives poor control quality in the case of an imperfect model and disturbances. An alternative is to use the computationally demanding MPC scheme with on-line nonlinear optimisation repeated at each sampling instant.

A linear approximation of the Wiener model or the predicted trajectory is found on-line. As a result, quadratic optimisation tasks are obtained. Furthermore, parameterisation using Laguerre functions is possible to reduce the number of decision variables. Simulation results for ten benchmark processes show that the discussed MPC algorithms lead to excellent control quality. For a neutralisation reactor and a fuel cell, essential advantages ofneural Wiener models are demonstrated.

„Über diesen Titel“ kann sich auf eine andere Ausgabe dieses Titels beziehen.

Verlag
Springer
Erscheinungsdatum
2022
Sprache
Englisch
ISBN 10 
303083817X
ISBN 13 
9783030838171
Einband
Taschenbuch
Auflage
1
Anzahl der Seiten
368
Kontakt zum Hersteller
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Darmstadt
64293
Deutschland

Weitere beliebte Ausgaben desselben Titels

9783030838140: Nonlinear Predictive Control Using Wiener Models: Computationally Efficient Approaches for Polynomial and Neural Structures (Studies in Systems, Decision and Control, 389, Band 389)

Vorgestellte Ausgabe

ISBN 10:  3030838145 ISBN 13:  9783030838140
Verlag: Springer, 2021
Hardcover