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Randomized Algorithms for Analysis and Control of Uncertain Systems
Moving on from earlier stochastic and robust control paradigms, this book introduces the fundamentals of probabilistic methods in the analysis and design of uncertain systems. The use of randomized algorithms, guarantees a reduction in the computational complexity of classical robust control algorithms and in the conservativeness of methods like H-infinity control. Features: • self-contained treatment explaining randomized algorithms from their genesis in the principles of probability theory to their use for robust analysis and controller synthesis; • comprehensive treatment of sample generation, including consideration of the difficulties involved in obtaining independent and identically distributed samples; • applications in congestion control of high-speed communications networks and the stability of quantized sampled-data systems. This monograph will be of interest to theorists concerned with robust and optimal control techniques and to all control engineers dealing with system uncertainties.
Low Rank Approximation
Data Approximation by Low-complexity Models details the theory, algorithms, and applications of structured low-rank approximation. Efficient local optimization methods and effective suboptimal convex relaxations for Toeplitz, Hankel, and Sylvester structured problems are presented. Much of the text is devoted to describing the applications of the theory including: system and control theory; signal processing; computer algebra for approximate factorization and common divisor computation; computer vision for image deblurring and segmentation; machine learning for information retrieval and clustering; bioinformatics for microarray data analysis; chemometrics for multivariate calibration; and psychometrics for factor analysis. Software implementation of the methods is given, making the theory directly applicable in practice. All numerical examples are included in demonstration files giving hands-on experience and exercises and MATLAB® examples assist in the assimilation of the theory.
Control of Singular Systems with Random Abrupt Changes
This book deals with the class of singular systems with random abrupt changes also known as singular Markovian jump systems. Various problems and their robustness are tackled. The book examines both the theoretical and practical aspects of the control problems from the angle of the structural properties of linear systems. It can be used as a textbook as well as a reference for researchers in control or mathematics with interest in control theory.
Constrained Control and Estimation
This book provides a comprehensive treatment of the principles underlying optimal constrained control and estimation. The contents progress from optimisation theory, fixed-horizon discrete optimal control, receding-horizon implementations and stability conditions to explicit solutions and numerical algorithms, moving horizon estimation, and connections between constrained estimation and control. Several case studies and further developments illustrate and expand the core principles. Specific topics covered include: • An overview of optimisation theory. • Links to optimal control theory, including the discrete-minimum principle. • Linear and nonlinear receding-horizon constrained contro...
Digital Control Systems
The extraordinary development of digital computers (microprocessors, microcontrollers) and their extensive use in control systems in all fields of applications has brought about important changes in the design of control systems. Their performance and their low cost make them suitable for use in control systems of various kinds which demand far better capabilities and performances than those provided by analog controllers. However, in order really to take advantage of the capabilities of microprocessors, it is not enough to reproduce the behavior of analog (PID) controllers. One needs to implement specific and high-performance model based control techniques developed for computer-controlled ...
Passivity-based Control of Euler-Lagrange Systems
The essence of this work is the control of electromechanical systems, such as manipulators, electric machines, and power converters. The common thread that links together the results presented here is the passivity property, which is at present in numerous electrical and mechanical systems, and which has great relevance in control engineering at this time. Amongst other topics, the authors cover: Euler-Lagrange Systems, Mechanical Systems, Generalised AC Motors, Induction Motor Control, Robots with AC Drives, and Perspectives and Open Problems. The authors have extensive experience of research and application in the field of control of electromechanical systems, which they have summarised here in this self-contained volume. While written in a strictly mathematical way, it is also elementary, and will be accessible to a wide-ranging audience, including graduate students as well as practitioners and researchers in this field.
Learning and Generalisation
Learning and Generalization provides a formal mathematical theory addressing intuitive questions of the type: • How does a machine learn a concept on the basis of examples? • How can a neural network, after training, correctly predict the outcome of a previously unseen input? • How much training is required to achieve a given level of accuracy in the prediction? • How can one identify the dynamical behaviour of a nonlinear control system by observing its input-output behaviour over a finite time? The second edition covers new areas including: • support vector machines; • fat-shattering dimensions and applications to neural network learning; • learning with dependent samples generated by a beta-mixing process; • connections between system identification and learning theory; • probabilistic solution of 'intractable problems' in robust control and matrix theory using randomized algorithms. It also contains solutions to some of the open problems posed in the first edition, while adding new open problems.
Subspace Methods for System Identification
An in-depth introduction to subspace methods for system identification in discrete-time linear systems thoroughly augmented with advanced and novel results, this text is structured into three parts. Part I deals with the mathematical preliminaries: numerical linear algebra; system theory; stochastic processes; and Kalman filtering. Part II explains realization theory as applied to subspace identification. Stochastic realization results based on spectral factorization and Riccati equations, and on canonical correlation analysis for stationary processes are included. Part III demonstrates the closed-loop application of subspace identification methods. Subspace Methods for System Identification is an excellent reference for researchers and a useful text for tutors and graduate students involved in control and signal processing courses. It can be used for self-study and will be of interest to applied scientists or engineers wishing to use advanced methods in modeling and identification of complex systems.
Controlling Chaos
Controlling Chaos achieves three goals: the suppression, synchronisation and generation of chaos, each of which is the focus of a separate part of the book. The text deals with the well-known Lorenz, Rössler and Hénon attractors and the Chua circuit and with less celebrated novel systems. Modelling of chaos is accomplished using difference equations and ordinary and time-delayed differential equations. The methods directed at controlling chaos benefit from the influence of advanced nonlinear control theory: inverse optimal control is used for stabilization; exact linearization for synchronization; and impulsive control for chaotification. Notably, a fusion of chaos and fuzzy systems theories is employed. Time-delayed systems are also studied. The results presented are general for a broad class of chaotic systems. This monograph is self-contained with introductory material providing a review of the history of chaos control and the necessary mathematical preliminaries for working with dynamical systems.
Robust Control
New results, fresh ideas and new applications in automotive and flight control systems are presented in this second edition of Robust Control. The book presents parametric methods and tools for the simultaneous design of several representative operating conditions and several design specifications in the time and frequency domains. It also covers methods for robustness analysis that guarantee the desired properties for all possible values of the plant uncertainty. A lot of practical application experience enters into the case studies of driver support systems that avoid skidding and rollover of cars, automatic car steering systems, flight controllers for unstable aircraft and engine-out controllers. The book also shows the historic roots of the methods, their limitations and research needs in robust control.
