Igor ¿krjanc (13 results)
- Softcover
Seller: Revaluation Books, Exeter, United KingdomRevaluation Books
Contact seller5-star sellerCondition: New
£ 99.70
£ 12.50 shippingShips from United Kingdom to U.S.A.Quantity: 2 available
Paperback. Condition: Brand New. 502 pages. 8.75x5.75x1.25 inches. In Stock.
- Hardcover
Seller: Books Puddle, New York, NY, U.S.A.Books Puddle
Contact seller4-star sellerCondition: New
£ 124.89
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Condition: New. pp. 272.
- Softcover
Seller: AHA-BUCH GmbH, Einbeck, GermanyAHA-BUCH GmbH
Contact seller5-star sellerCondition: New
£ 94.43
£ 53.20 shippingShips from Germany to U.S.A.Quantity: 1 available
Taschenbuch. Condition: 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.
- Hardcover
Seller: Buchpark, Trebbin, GermanyBuchpark
Contact seller5-star sellerCondition: Used - Fine
£ 73.79
£ 89.97 shippingShips from Germany to U.S.A.Quantity: 1 available
Condition: Sehr gut. Zustand: Sehr gut | Seiten: 272 | Sprache: Englisch | Produktart: Bücher | 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 ran…ge 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.
- Hardcover
Seller: Mispah books, Redhill, SURRE, United KingdomMispah books
Contact seller4-star sellerCondition: Used - As new
£ 152.00
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Hardcover. Condition: Like New. LIKE NEW. SHIPS FROM MULTIPLE LOCATIONS. book.
- Softcover
Seller: Mispah books, Redhill, SURRE, United KingdomMispah books
Contact seller4-star sellerCondition: Used - As new
£ 166.00
£ 25.00 shippingShips from United Kingdom to U.S.A.Quantity: 1 available
Paperback. Condition: Like New. LIKE NEW. SHIPS FROM MULTIPLE LOCATIONS. book.
- Softcover
- Print on Demand
Seller: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, GermanyBuchWeltWeit Ludwig Meier e.K.
Contact seller5-star sellerCondition: New
£ 94.43
£ 19.71 shippingShips from Germany to U.S.A.Quantity: 2 available
Taschenbuch. Condition: 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 r…ange 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.
- Hardcover
- Print on Demand
Seller: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, GermanyBuchWeltWeit Ludwig Meier e.K.
Contact seller5-star sellerCondition: New
£ 94.43
£ 19.71 shippingShips from Germany to U.S.A.Quantity: 2 available
Buch. Condition: 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.
- Hardcover
- Print on Demand
Seller: moluna, Greven, Germanymoluna
Contact seller5-star sellerCondition: New
£ 81.44
£ 41.98 shippingShips from Germany to U.S.A.Quantity: Over 20 available
Condition: 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-va…rying systems and adapti.
- Softcover
- Print on Demand
Seller: moluna, Greven, Germanymoluna
Contact seller5-star sellerCondition: New
£ 81.44
£ 41.98 shippingShips from Germany to U.S.A.Quantity: Over 20 available
Condition: 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-va…rying systems and adapti.
- Hardcover
- Print on Demand
Seller: Majestic Books, Hounslow, United KingdomMajestic Books
Contact seller4-star sellerCondition: New
£ 128.53
£ 6.50 shippingShips from United Kingdom to U.S.A.Quantity: 4 available
Condition: New. Print on Demand pp. 272 84 Illus.
- Hardcover
- Print on Demand
Seller: Biblios, frankfurt am main, HESSE, GermanyBiblios
Contact seller4-star sellerCondition: New
£ 135.35
£ 8.53 shippingShips from Germany to U.S.A.Quantity: 4 available
Condition: New. PRINT ON DEMAND pp. 272.
- Hardcover
- Print on Demand
Seller: AHA-BUCH GmbH, Einbeck, GermanyAHA-BUCH GmbH
Contact seller5-star sellerCondition: New
£ 99.68
£ 53.88 shippingShips from Germany to U.S.A.Quantity: 1 available
Buch. Condition: Neu. nach der Bestellung gedruckt 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 syst…ems; 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.
