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The book presents recent theoretical and practical information about the field of automation and control. It includes fifteen chapters that promote automation and control in practical applications in the following thematic areas: control theory, autonomous vehicles, mechatronics, digital image processing, electrical grids, artificial intelligence, and electric motor drives. The book also presents and discusses applications that improve the properties and performances of process control with examples and case studies obtained from real-world research in the field. Automation and Control is designed for specialists, engineers, professors, and students.

Control theory. --- Feedback control systems.

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Aeronautical engineers concerned with the analysis of aircraft dynamics and the synthesis of aircraft flight control systems will find an indispensable tool in this analytical treatment of the subject. Approaching these two fields with the conviction that an understanding of either one can illuminate the other, the authors have summarized selected, interconnected techniques that facilitate a high level of insight into the essence of complex systems problems. These techniques are suitable for establishing nominal system designs, for forecasting off-nominal problems, and for diagnosing the root causes of problems that almost inevitably occur in the design process. A complete and self-contained work, the text discusses the early history of aircraft dynamics and control, mathematical models of linear system elements, feedback system analysis, vehicle equations of motion, longitudinal and lateral dynamics, and elementary longitudinal and lateral feedback control. The discussion concludes with such topics as the system design process, inputs and system performance assessment, and multi-loop flight control systems.Originally published in 1974.The Princeton Legacy Library uses the latest print-on-demand technology to again make available previously out-of-print books from the distinguished backlist of Princeton University Press. These editions preserve the original texts of these important books while presenting them in durable paperback and hardcover editions. The goal of the Princeton Legacy Library is to vastly increase access to the rich scholarly heritage found in the thousands of books published by Princeton University Press since its founding in 1905.

Airplanes -- Control systems. --- Feedback control systems. --- Airplanes --- Control systems.

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Intelligent Coordinated Control of Complex Uncertain Systems for Power Distribution and Network Reliability discusses the important topics revolving around the control of complex uncertain systems using the intelligent coordination control mechanism, a topic that has become the research focus of current control and computer fields. The book provides theoretical guidance for power distribution network reliability analysis, focusing on practical problems and algorithms within the field. Provides effective solutions for complex control systems Presents theoretical guidance for power distribution network reliability analysis Focuses on practical problems and algorithms

Electric power distribution --- Reliability. --- Control theory. --- Feedback control systems.

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Unlock the full potential of reinforcement learning (RL), a crucial subfield of Artificial Intelligence, with this comprehensive guide. This book provides a deep dive into RL's core concepts, mathematics, and practical algorithms, helping you to develop a thorough understanding of this cutting-edge technology. Beginning with an overview of fundamental concepts such as Markov decision processes, dynamic programming, Monte Carlo methods, and temporal difference learning, this book uses clear and concise examples to explain the basics of RL theory. The following section covers value function approximation, a critical technique in RL, and explores various policy approximations such as policy gradient methods and advanced algorithms like Proximal Policy Optimization (PPO). This book also delves into advanced topics, including distributed reinforcement learning, curiosity-driven exploration, and the famous AlphaZero algorithm, providing readers with a detailed account of these cutting-edge techniques. With a focus on explaining algorithms and the intuition behind them, The Art of Reinforcement Learning includes practical source code examples that you can use to implement RL algorithms. Upon completing this book, you will have a deep understanding of the concepts, mathematics, and algorithms behind reinforcement learning, making it an essential resource for AI practitioners, researchers, and students. You will: Grasp fundamental concepts and distinguishing features of reinforcement learning, including how it differs from other AI and non-interactive machine learning approaches Model problems as Markov decision processes, and how to evaluate and optimize policies using dynamic programming, Monte Carlo methods, and temporal difference learning Utilize techniques for approximating value functions and policies, including linear and nonlinear value function approximation and policy gradient methods Understand the architecture and advantages of distributed reinforcement learning Master the concept of curiosity-driven exploration and how it can be leveraged to improve reinforcement learning agents Explore the AlphaZero algorithm and how it was able to beat professional Go players.

Reinforcement learning. --- Feedback control systems. --- Python (Computer program language)

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Automatic control --- Relay control systems --- Embedded computer systems --- Feedback control systems --- Artificial intelligence --- Computer. Automation