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Ria Christie Collections, Uxbridge, Royaume-Uni
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In. N° de réf. du vendeur ria9783642339462_new
This book presents control of time-delayed, unstable, multivariable processes and modelling approaches for several classes of complex systems. It places special emphasis on hybrid systems with distinct nonlinearities.
Titre : Predictive Approaches to Control of Complex ...
Éditeur : Springer
Date d'édition : 2012
Reliure : Couverture rigide
Etat : New
Vendeur : Buchpark, Trebbin, Allemagne
Etat : Sehr gut. Zustand: Sehr gut | Seiten: 272 | Sprache: Englisch | Produktart: Bücher | Keine Beschreibung verfügbar. N° de réf. du vendeur 22983779/12
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Vendeur : moluna, Greven, Allemagne
Etat : New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Presents control of time-delayed, unstable, multivariable processes and modelling approaches for several classes of complex systems Special emphasis is put on hybrid systems with distinct nonlinearities Treats time-varying systems and adapti. N° de réf. du vendeur 5057448
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Vendeur : preigu, Osnabrück, Allemagne
Buch. Etat : Neu. Predictive Approaches to Control of Complex Systems | Gorazd Karer (u. a.) | Buch | xii | Englisch | 2012 | Springer | EAN 9783642339462 | Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg, juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu Print on Demand. N° de réf. du vendeur 106238910
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Vendeur : Lucky's Textbooks, Dallas, TX, Etats-Unis
Etat : New. N° de réf. du vendeur ABLIING23Mar3113020224605
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Vendeur : BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Allemagne
Buch. Etat : Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -A predictive control algorithm uses a model of the controlled system to predict the system behavior for various input scenarios and determines the most appropriate inputs accordingly. Predictive controllers are suitable for a wide range of systems; therefore, their advantages are especially evident when dealing with relatively complex systems, such as nonlinear, constrained, hybrid, multivariate systems etc. However, designing a predictive control strategy for a complex system is generally a difficult task, because all relevant dynamical phenomena have to be considered. Establishing a suitable model of the system is an essential part of predictive control design. Classic modeling and identification approaches based on linear-systems theory are generally inappropriate for complex systems; hence, models that are able to appropriately consider complex dynamical properties have to be employed in a predictive control algorithm. This book first introduces some modeling frameworks, which can encompass the most frequently encountered complex dynamical phenomena and are practically applicable in the proposed predictive control approaches. Furthermore, unsupervised learning methods that can be used for complex-system identification are treated. Finally, several useful predictive control algorithms for complex systems are proposed and their particular advantages and drawbacks are discussed. The presented modeling, identification and control approaches are complemented by illustrative examples. The book is aimed towards researches and postgraduate students interested in modeling, identification and control, as well as towards control engineers needing practically usable advanced control methods for complex systems. 272 pp. Englisch. N° de réf. du vendeur 9783642339462
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Vendeur : AHA-BUCH GmbH, Einbeck, Allemagne
Buch. Etat : Neu. Druck auf Anfrage Neuware - Printed after ordering - A predictive control algorithm uses a model of the controlled system to predict the system behavior for various input scenarios and determines the most appropriate inputs accordingly. Predictive controllers are suitable for a wide range of systems; therefore, their advantages are especially evident when dealing with relatively complex systems, such as nonlinear, constrained, hybrid, multivariate systems etc. However, designing a predictive control strategy for a complex system is generally a difficult task, because all relevant dynamical phenomena have to be considered. Establishing a suitable model of the system is an essential part of predictive control design. Classic modeling and identification approaches based on linear-systems theory are generally inappropriate for complex systems; hence, models that are able to appropriately consider complex dynamical properties have to be employed in a predictive control algorithm. This book first introduces some modeling frameworks, which can encompass the most frequently encountered complex dynamical phenomena and are practically applicable in the proposed predictive control approaches. Furthermore, unsupervised learning methods that can be used for complex-system identification are treated. Finally, several useful predictive control algorithms for complex systems are proposed and their particular advantages and drawbacks are discussed. The presented modeling, identification and control approaches are complemented by illustrative examples. The book is aimed towards researches and postgraduate students interested in modeling, identification and control, as well as towards control engineers needing practically usable advanced control methods for complex systems. N° de réf. du vendeur 9783642339462
Quantité disponible : 1 disponible(s)
Vendeur : buchversandmimpf2000, Emtmannsberg, BAYE, Allemagne
Buch. Etat : Neu. This item is printed on demand - Print on Demand Titel. Neuware -A predictive control algorithm uses a model of the controlled system to predict the system behavior for various input scenarios and determines the most appropriate inputs accordingly. Predictive controllers are suitable for a wide range of systems; therefore, their advantages are especially evident when dealing with relatively complex systems, such as nonlinear, constrained, hybrid, multivariate systems etc. However, designing a predictive control strategy for a complex system is generally a difficult task, because all relevant dynamical phenomena have to be considered. Establishing a suitable model of the system is an essential part of predictive control design. Classic modeling and identification approaches based on linear-systems theory are generally inappropriate for complex systems; hence, models that are able to appropriately consider complex dynamical properties have to be employed in a predictive control algorithm.This book first introduces some modeling frameworks, which can encompass the most frequently encountered complex dynamical phenomena and are practically applicable in the proposed predictive control approaches. Furthermore, unsupervised learning methods that can be used for complex-system identification are treated. Finally, several useful predictive control algorithms for complex systems are proposed and their particular advantages and drawbacks are discussed. The presented modeling, identification and control approaches are complemented by illustrative examples. The book is aimed towards researches and postgraduate students interested in modeling, identification and control, as well as towards control engineers needing practically usable advanced control methods for complex systems.Springer-Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg 272 pp. Englisch. N° de réf. du vendeur 9783642339462
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Vendeur : Books Puddle, New York, NY, Etats-Unis
Etat : New. pp. 272. N° de réf. du vendeur 2658577094
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Vendeur : Majestic Books, Hounslow, Royaume-Uni
Etat : New. Print on Demand pp. 272 84 Illus. N° de réf. du vendeur 51015449
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Vendeur : Revaluation Books, Exeter, Royaume-Uni
Hardcover. Etat : Brand New. 2013 edition. 286 pages. 9.75x6.50x0.75 inches. In Stock. N° de réf. du vendeur x-3642339468
Quantité disponible : 2 disponible(s)