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Classical and Modern Optimization Techniques Applied to Control and Modeling [Kietas viršelis]

  • Formatas: Hardback, 354 pages, aukštis x plotis: 234x156 mm, weight: 830 g, 4 Tables, black and white; 84 Line drawings, black and white; 3 Halftones, black and white; 87 Illustrations, black and white
  • Išleidimo metai: 24-Mar-2025
  • Leidėjas: CRC Press
  • ISBN-10: 103278511X
  • ISBN-13: 9781032785110
  • Formatas: Hardback, 354 pages, aukštis x plotis: 234x156 mm, weight: 830 g, 4 Tables, black and white; 84 Line drawings, black and white; 3 Halftones, black and white; 87 Illustrations, black and white
  • Išleidimo metai: 24-Mar-2025
  • Leidėjas: CRC Press
  • ISBN-10: 103278511X
  • ISBN-13: 9781032785110

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.



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.

Chapter 1- Introduction

Chapter 2- One-step Optimization

Chapter 3- Discrete-time Optimization

Chapter 4- Numerical Solving of Optimization Problems

Chapter 5- Metaheuristic Optimization Algorithms

Chapter 6- Optimization Algorithms in Artificial Neural Network Training

Chapter 7- Introduction to Data Mining

Chapter 8- Reinforcement Learning Applied to Optimal Control

Dr. RaduEmil 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. RaulCristian 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.