Optimal and Robust Estimation

Author: Frank L. Lewis
Publisher: CRC Press
ISBN: 1420008293
Format: PDF, Mobi
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More than a decade ago, world-renowned control systems authority Frank L. Lewis introduced what would become a standard textbook on estimation, under the title Optimal Estimation, used in top universities throughout the world. The time has come for a new edition of this classic text, and Lewis enlisted the aid of two accomplished experts to bring the book completely up to date with the estimation methods driving today's high-performance systems. A Classic Revisited Optimal and Robust Estimation: With an Introduction to Stochastic Control Theory, Second Edition reflects new developments in estimation theory and design techniques. As the title suggests, the major feature of this edition is the inclusion of robust methods. Three new chapters cover the robust Kalman filter, H-infinity filtering, and H-infinity filtering of discrete-time systems. Modern Tools for Tomorrow's Engineers This text overflows with examples that highlight practical applications of the theory and concepts. Design algorithms appear conveniently in tables, allowing students quick reference, easy implementation into software, and intuitive comparisons for selecting the best algorithm for a given application. In addition, downloadable MATLAB® code allows students to gain hands-on experience with industry-standard software tools for a wide variety of applications. This cutting-edge and highly interactive text makes teaching, and learning, estimation methods easier and more modern than ever.

End to End Adaptive Congestion Control in TCP IP Networks

Author: Christos N. Houmkozlis
Publisher: CRC Press
ISBN: 143984058X
Format: PDF, ePub, Mobi
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This book provides an adaptive control theory perspective on designing congestion controls for packet-switching networks. Relevant to a wide range of disciplines and industries, including the music industry, computers, image trading, and virtual groups, the text extensively discusses source-oriented, or end-to-end, congestion control algorithms. The book empowers readers with clear understanding of the characteristics of packet-switching networks and their effects on system stability and performance. It provides schemes capable of controlling congestion and fairness and presents real-world applications to demonstrate the modeling and control techniques.

Classical Feedback Control

Author: Boris Lurie
Publisher: CRC Press
ISBN: 1439897468
Format: PDF
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This second edition textbook describes the design and implementation of high-performance feedback controllers for engineering systems. It emphasizes the frequency-domain design and methods based on Bode integrals, loop shaping, and nonlinear dynamic compensation. The authors include many problems and offer practical applications, illustrations, and plots with MATLAB simulation and design examples. This text contains homework problems accompanied by actual solutions. Examples include case studies and real-world situations.

Synchronization and Control of Multiagent Systems

Author: Dong Sun
Publisher: CRC Press
ISBN: 9781439820483
Format: PDF, Docs
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Multiple intelligent agent systems are commonly used in research requiring complex behavior. Synchronization control provides an advantage in solving the problem of multi-agent coordination. This book focuses on the use of synchronization control to coordinate the group behavior of multiple agents. The author includes numerous real-world application examples from robotics, automation, and advanced manufacturing. Giving a detailed look at cross-coupling based synchronization control, the text covers such topics as adaptive synchronization control, synchronous tracking control of parallel manipulators, and minimization of contouring errors of CNC machine tools with synchronization controls.

Intelligent Diagnosis and Prognosis of Industrial Networked Systems

Author: Chee Khiang Pang
Publisher: CRC Press
ISBN: 1439840598
Format: PDF
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In an era of intense competition where plant operating efficiencies must be maximized, downtime due to machinery failure has become more costly. To cut operating costs and increase revenues, industries have an urgent need to predict fault progression and remaining lifespan of industrial machines, processes, and systems. An engineer who mounts an acoustic sensor onto a spindle motor wants to know when the ball bearings will wear out without having to halt the ongoing milling processes. A scientist working on sensor networks wants to know which sensors are redundant and can be pruned off to save operational and computational overheads. These scenarios illustrate a need for new and unified perspectives in system analysis and design for engineering applications. Intelligent Diagnosis and Prognosis of Industrial Networked Systems proposes linear mathematical tool sets that can be applied to realistic engineering systems. The book offers an overview of the fundamentals of vectors, matrices, and linear systems theory required for intelligent diagnosis and prognosis of industrial networked systems. Building on this theory, it then develops automated mathematical machineries and formal decision software tools for real-world applications. The book includes portable tool sets for many industrial applications, including: Forecasting machine tool wear in industrial cutting machines Reduction of sensors and features for industrial fault detection and isolation (FDI) Identification of critical resonant modes in mechatronic systems for system design of R&D Probabilistic small-signal stability in large-scale interconnected power systems Discrete event command and control for military applications The book also proposes future directions for intelligent diagnosis and prognosis in energy-efficient manufacturing, life cycle assessment, and systems of systems architecture. Written in a concise and accessible style, it presents tools that are mathematically rigorous but not involved. Bridging academia, research, and industry, this reference supplies the know-how for engineers and managers making decisions about equipment maintenance, as well as researchers and students in the field.

Reinforcement Learning and Dynamic Programming Using Function Approximators

Author: Lucian Busoniu
Publisher: CRC Press
ISBN: 9781439821091
Format: PDF, Kindle
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From household appliances to applications in robotics, engineered systems involving complex dynamics can only be as effective as the algorithms that control them. While Dynamic Programming (DP) has provided researchers with a way to optimally solve decision and control problems involving complex dynamic systems, its practical value was limited by algorithms that lacked the capacity to scale up to realistic problems. However, in recent years, dramatic developments in Reinforcement Learning (RL), the model-free counterpart of DP, changed our understanding of what is possible. Those developments led to the creation of reliable methods that can be applied even when a mathematical model of the system is unavailable, allowing researchers to solve challenging control problems in engineering, as well as in a variety of other disciplines, including economics, medicine, and artificial intelligence. Reinforcement Learning and Dynamic Programming Using Function Approximators provides a comprehensive and unparalleled exploration of the field of RL and DP. With a focus on continuous-variable problems, this seminal text details essential developments that have substantially altered the field over the past decade. In its pages, pioneering experts provide a concise introduction to classical RL and DP, followed by an extensive presentation of the state-of-the-art and novel methods in RL and DP with approximation. Combining algorithm development with theoretical guarantees, they elaborate on their work with illustrative examples and insightful comparisons. Three individual chapters are dedicated to representative algorithms from each of the major classes of techniques: value iteration, policy iteration, and policy search. The features and performance of these algorithms are highlighted in extensive experimental studies on a range of control applications. The recent development of applications involving complex systems has led to a surge of interest in RL and DP methods and the subsequent need for a quality resource on the subject. For graduate students and others new to the field, this book offers a thorough introduction to both the basics and emerging methods. And for those researchers and practitioners working in the fields of optimal and adaptive control, machine learning, artificial intelligence, and operations research, this resource offers a combination of practical algorithms, theoretical analysis, and comprehensive examples that they will be able to adapt and apply to their own work. Access the authors' website at www.dcsc.tudelft.nl/rlbook/ for additional material, including computer code used in the studies and information concerning new developments.

Feedback Control Theory

Author: John C. Doyle
Publisher: Courier Corporation
ISBN: 0486318338
Format: PDF, ePub
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An excellent introduction to feedback control system design, this book offers a theoretical approach that captures the essential issues and can be applied to a wide range of practical problems. Its explorations of recent developments in the field emphasize the relationship of new procedures to classical control theory, with a focus on single input and output systems that keeps concepts accessible to students with limited backgrounds. The text is geared toward a single-semester senior course or a graduate-level class for students of electrical engineering. The opening chapters constitute a basic treatment of feedback design. Topics include a detailed formulation of the control design program, the fundamental issue of performance/stability robustness tradeoff, and the graphical design technique of loopshaping. Subsequent chapters extend the discussion of the loopshaping technique and connect it with notions of optimality. Concluding chapters examine controller design via optimization, offering a mathematical approach that is useful for multivariable systems.

Nonlinear Stochastic Control and Filtering with Engineering oriented Complexities

Author: Guoliang Wei
Publisher: CRC Press
ISBN: 1315350661
Format: PDF, Docs
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Nonlinear Stochastic Control and Filtering with Engineering-oriented Complexities presents a series of control and filtering approaches for stochastic systems with traditional and emerging engineering-oriented complexities. The book begins with an overview of the relevant background, motivation, and research problems, and then: Discusses the robust stability and stabilization problems for a class of stochastic time-delay interval systems with nonlinear disturbances Investigates the robust stabilization and H? control problems for a class of stochastic time-delay uncertain systems with Markovian switching and nonlinear disturbances Explores the H? state estimator and H? output feedback controller design issues for stochastic time-delay systems with nonlinear disturbances, sensor nonlinearities, and Markovian jumping parameters Analyzes the H? performance for a general class of nonlinear stochastic systems with time delays, where the addressed systems are described by general stochastic functional differential equations Studies the filtering problem for a class of discrete-time stochastic nonlinear time-delay systems with missing measurement and stochastic disturbances Uses gain-scheduling techniques to tackle the probability-dependent control and filtering problems for time-varying nonlinear systems with incomplete information Evaluates the filtering problem for a class of discrete-time stochastic nonlinear networked control systems with multiple random communication delays and random packet losses Examines the filtering problem for a class of nonlinear genetic regulatory networks with state-dependent stochastic disturbances and state delays Considers the H? state estimation problem for a class of discrete-time complex networks with probabilistic missing measurements and randomly occurring coupling delays Addresses the H? synchronization control problem for a class of dynamical networks with randomly varying nonlinearities Nonlinear Stochastic Control and Filtering with Engineering-oriented Complexities describes novel methodologies that can be applied extensively in lab simulations, field experiments, and real-world engineering practices. Thus, this text provides a valuable reference for researchers and professionals in the signal processing and control engineering communities.

Adaptive Control

Author: Karl J. Åström
Publisher: Courier Corporation
ISBN: 0486319148
Format: PDF, ePub
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Suitable for advanced undergraduates and graduate students, this overview introduces theoretical and practical aspects of adaptive control, with emphasis on deterministic and stochastic viewpoints. 1995 edition.

Control Theory

Author: Tamer S. Basar
Publisher: Wiley-IEEE Press
ISBN: 9780780360211
Format: PDF
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Electrical Engineering Control Theory Twenty-Five Seminal Papers (1932-1981) Control theory, developed in the twentieth century, is the subject of this compilation of 25 annotated reprints of seminal papers representing the evolution of the control field. Carefully assembled by a distinguished editorial board to ensure that each paper contributes to the whole, rather than exist as a separate entity, this is the first book to document the research and accomplishments that have driven the practice of control. Control Theory: Twenty-Five Seminal Papers (1932-1981) begins with an introduction describing the major developments in control, linking each to a selected paper. Each paper includes a commentary that lends a contemporary spin and places the contributions of each paper and its impact on the field into proper perspective. The material covers the period between 1932 to 1981 and addresses a broad spectrum of topics. The earliest paper is the famous "Regeneration Theory" by Harry Nyquist, which laid the foundation for a frequency-domain approach to stability analysis of linear control systems and introduced the Nyquist criterion. The most recent paper in the volume, "Feedback and Optimal Sensitivity" by George Zames, marked the beginning of the "robustness" era. This comprehensive volume is a valuable resource for control researchers and engineers worldwide. Also, it will be of great interest to engineers and scientists in related fields, such as communications, signal processing, circuits, power, and applied mathematics.