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During the 90s robust control theory has seen major advances and achieved a new maturity, centered around the notion of convexity. The goal of this book is to give a graduate-level course on this theory that emphasizes these new developments, but at the same time conveys the main principles and ubiquitous tools at the heart of the subject. Its pedagogical objectives are to introduce a coherent and unified framework for studying the theory, to provide students with the control-theoretic background required to read and contribute to the research literature, and to present the main ideas and demonstrations of the major results. The book will be of value to mathematical researchers and computer scientists, graduate students planning to do research in the area, and engineering practitioners requiring advanced control techniques.
Self-contained introduction to control theory that emphasizes on the most modern designs for high performance and robustness. It assumes no previous coursework and offers three chapters of key topics summarizing classical control. To provide readers with a deeper understanding of robust control theory than would be otherwise possible, the text incorporates mathematical derivations and proofs. Includes many elementary examples and advanced case studies using MATLAB Toolboxes.
Comprehensive and up to date coverage of robust control theory and its application • Presented in a well-planned and logical way • Written by a respected leading author, with extensive experience in robust control • Accompanying website provides solutions manual and other supplementary material
Robust control originates with the need to cope with systems with modeling uncertainty. There have been several mathematical techniques developed for robust control system analysis. The articles in this volume cover all of the major research directions in the field.
The Robust Control Theory involves powerful methods for analysis and design of control systems in presence of signal and parameter uncertainties. The most frequently used techniques for robust control design are the H? design and the ?-synthesis. In this book, Chapter One reviews issues related to the design and practical implementation of high order robust controllers. Chapter Two deals with multi-objective disturbance attenuation control and filtering problems for disturbances from different classes. Chapter Three discusses a robust control design for general switched affine control systems. Chapter Four presents a PID control scheme for a synchronous motor with permanent magnets.
An operator theoretic approach to robust control analysis for linear time-varying systems, with the emphasis on the conceptual similarity with the H control theory for time-invariant systems. It clarifies the major difficulties confronted in the time varying case and all the necessary operator theory is developed from first principles, making the book as self-contained as possible. After presenting the necessary results from the theories of Toeplitz operators and nest algebras, linear systems are defined as input-output operators and the relationship between stabilisation and the existence of co-prime factorisations is described. Uniform optimal control problems are formulated as model-matching problems and are reduced to four block problems, while robustness is considered both from the point of view of fractional representations and the "time varying gap" metric, as is the relationship between these types of uncertainties. The book closes with the solution of the orthogonal embedding problem for time-varying contractive systems. As such, this book is useful to both mathematicians and to control engineers.
Robust Control in Power Systems deals with the applications of new techniques in linear system theory to control low frequency oscillations in power systems. The book specifically focuses on the analysis and damping of inter-area oscillations in the systems which are in the range of 0.2-1 Hz. The damping control action is injected through high power electronic devices known as flexible AC transmission system (FACTS) controllers. Three commonly used FACTS controllers: controllable series capacitors (CSCs) controllable phase shifters (CPSs) and static var compensators (SVCs) have been used in this book to control the inter-area oscillations. The overview of linear system theory from the perspective of power system control is explained through examples. The damping control design is formulated as norm optimization problem. The H_infinity, H2 norm of properly defined transfer functions are minimized in linear matrix inequalities (LMI) framework to obtain desired performance and stability robustness. Both centralized and decentralized control structures are used. Usually the transmission of feedback signal from a remote location encounters delays making it difficult to control the system. Smith predictor based approach has been successfully explored in this book as a solution to such a problem. Robust Control in Power Systems will be valuable to academicians in the areas of power, control and system theory, as well as professionals in the power industry.
From the researcher who was one of the first to identify and analyze the infamous industrial control system malware "Stuxnet," comes a book that takes a new, radical approach to making Industrial control systems safe from such cyber attacks: design the controls systems themselves to be "robust." Other security experts advocate risk management, implementing more firewalls and carefully managing passwords and access. Not so this book: those measures, while necessary, can still be circumvented. Instead, this book shows in clear, concise detail how a system that has been set up with an eye toward quality design in the first place is much more likely to remain secure and less vulnerable to hacking, sabotage or malicious control. It blends several well-established concepts and methods from control theory, systems theory, cybernetics and quality engineering to create the ideal protected system. The book's maxim is taken from the famous quality engineer William Edwards Deming, "If I had to reduce my message to management to just a few words, I'd say it all has to do with reducing variation." Highlights include: - An overview of the problem of "cyber fragility" in industrial control systems - How to make an industrial control system "robust," including principal design objectives and overall strategic planning - Why using the methods of quality engineering like the Taguchi method, SOP and UML will help to design more "armored" industrial control systems.
This textbook aims to provide a clear understanding of the various tools of analysis and design for robust stability and performance of uncertain dynamic systems. In model-based control design and analysis, mathematical models can never completely represent the “real world” system that is being modeled, and thus it is imperative to incorporate and accommodate a level of uncertainty into the models. This book directly addresses these issues from a deterministic uncertainty viewpoint and focuses on the interval parameter characterization of uncertain systems. Various tools of analysis and design are presented in a consolidated manner. This volume fills a current gap in published works by explicitly addressing the subject of control of dynamic systems from linear state space framework, namely using a time-domain, matrix-theory based approach. This book also: Presents and formulates the robustness problem in a linear state space model framework. Illustrates various systems level methodologies with examples and applications drawn from aerospace, electrical and mechanical engineering. Provides connections between lyapunov-based matrix approach and the transfer function based polynomial approaches. Robust Control of Uncertain Dynamic Systems: A Linear State Space Approach is an ideal book for first year graduate students taking a course in robust control in aerospace, mechanical, or electrical engineering.
Shows readers how to exploit the capabilities of the MATLAB® Robust Control and Control Systems Toolboxes to the fullest using practical robust control examples.