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The book presents a detailed and unified treatment of the theory and applications of optimization applied to control and modeling, focusing on nature-inspired optimization algorithms to optimally tune the parameters of linear and nonlinear controllers and models, with emphasis on tower crane systems and other representative applications. Classical and Modern Optimization Techniques Applied to Control and Modeling combines classical and modern approaches to optimization, based on the authors’ experience in the field, and presents in a unified structure the essential aspects of optimization in control and modeling from a control engineer’s point of view. It covers linear and nonlinear controllers, and neural networks based on reinforcement learning are considered and analyzed because of the need to reduce the complexity of the controllers and their design so that they can be practical to implement as low-cost automation solutions. The chapters are designed to quickly make the concepts of optimization, control, reinforcement learning, and neural networks understandable to readers with limited experience. This book is intended for a broad audience, including undergraduate and graduate students, engineers (designers, practitioners, and researchers), and anyone facing challenging control problems.
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Über die Autorin bzw. den Autor
Dr. Radu‐Emil Precup is a professor with the Department of Automation and Applied Informatics, Politehnica University of Timisoara, Romania, and Senior Researcher (CS I) and the Head of the Data Science and Engineering Laboratory of the Center for Fundamental and Advanced Technical Research, Romanian Academy – Timisoara Branch, Romania.
Dr. Raul‐Cristian Roman is a lecturer with the Department of Automation and Applied Informatics, Politehnica University of Timisoara, Romania. He received a PhD in systems engineering in 2018 from Politehnica University of Timisoara, Timisoara, Romania.
Dr. Elena-Lorena Hedrea is an assistant lecturer with the Department of Automation and Applied Informatics, Politehnica University of Timisoara, Romania.
Dr. Alexandra-Iulia Szedlak-Stinean is a lecturer with the Department of Automation and Applied Informatics, Politehnica University of Timisoara, Romania.
Iuliu Alexandru Zamfirache is a PhD student with the Department of Automation and Applied Informatics, Politehnica University of Timisoara, Romania.
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Paperback. Zustand: new. Paperback. The book presents a detailed and unified treatment of the theory and applications of optimization applied to control and modeling, focusing on nature-inspired optimization algorithms to optimally tune the parameters of linear and nonlinear controllers and models, with emphasis on tower crane systems and other representative applications.Classical and Modern Optimization Techniques Applied to Control and Modeling combines classical and modern approaches to optimization, based on the authors experience in the field, and presents in a unified structure the essential aspects of optimization in control and modeling from a control engineers point of view. It covers linear and nonlinear controllers, and neural networks based on reinforcement learning are considered and analyzed because of the need to reduce the complexity of the controllers and their design so that they can be practical to implement as low-cost automation solutions. The chapters are designed to quickly make the concepts of optimization, control, reinforcement learning, and neural networks understandable to readers with limited experience.This book is intended for a broad audience, including undergraduate and graduate students, engineers (designers, practitioners, and researchers), and anyone facing challenging control problems. The book presents a detailed and unified treatment of the theory and applications of optimization applied to control and modeling, focusing on nature-inspired optimization algorithms to optimally tune the parameters of linear and nonlinear controllers and models, with emphasis on tower crane systems and other representative applications. This item is printed on demand. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability. Bestandsnummer des Verkäufers 9781032785141
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Taylor & Francis Ltd (Sales) Jun 2026, 2026
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ISBN 13: 9781032785141
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