Nonlinear predictive control using par yousuf muhammad (4 résultats)
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Vendeur : preigu, Osnabrück, Allemagnepreigu
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Taschenbuch. Etat : Neu. Nonlinear Predictive Control Using Particle Swarm Optimization | Application to Power Systems | Muhammad Salman Yousuf | Taschenbuch | Englisch | VDM Verlag Dr. Müller | EAN 9783639249668 | Verantwortliche Person für die EU: VDM Verlag Dr. Müller, Brivibas Gatve 197, 1039 RIGA, LETTLAND, customerservice[…at]vdm-vsg[dot]de | Anbieter: preigu.
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Vendeur : Mispah books, Redhill, SURRE, Royaume-UniMispah books
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Paperback. Etat : Like New. LIKE NEW. SHIPS FROM MULTIPLE LOCATIONS. book.
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Vendeur : moluna, Greven, Allemagnemoluna
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Kartoniert / Broschiert. Etat : New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Autor/Autorin: Yousuf Muhammad SalmanThe author belongs to the Department of Electrical Engineering at King Fahd University of Petroleum and Minerals (KFUPM), Dhahran, Saudi Arabia. He received hi…s Bachelor in Electronic Engineering from NED Univer.
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Vendeur : AHA-BUCH GmbH, Einbeck, AllemagneAHA-BUCH GmbH
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Taschenbuch. Etat : Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - It is well recognized that linear control methods are not always the optimum way to deal with typical nonlinear plants. Due to continual increment in the complexity of systems and tighter product specifications, the quality requirements from… automatic control have increased. Recently, the available computing power has rose to fantastic levels as well. Consequently, computationally intensive control methods can now be applied to complex systems. Model Predictive Control (MPC) techniques were developed to obtain tighter control and were applied successfully to several industrial applications. In this book, a new implementation of MPC is proposed using Particle Swarm Optimization (PSO). The proposed method formulates the MPC as an optimization problem and PSO is used to minimize it. This gives advantages like adaptability, possibility of varying control objectives, and enhanced capability of handling constraints. The proposed method is applied to an area of industrial systems that has been relatively unexplored by MPC, i.e. power systems. Both SISO and MIMO nonlinear systems are considered. Three practical Power System problems are taken and the proposed technique is applied to them.
