Control Of Uncertain Systems Modelling Approximation And Design
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Author : Bruce A. Francis Publisher : Taylor & Francis Page : 452 pages File Size : 54,6 Mb Release : 2006-03-07 Category : Language Arts & Disciplines ISBN : 3540317546
Control of Uncertain Systems: Modelling, Approximation, and Design by Bruce A. Francis Pdf
This Festschrift contains a collection of articles by friends, co-authors, colleagues, and former Ph.D. students of Keith Glover, Professor of Engineering at the University of Cambridge, on the occasion of his sixtieth birthday. Professor Glover's scientific work spans a wide variety of topics, the main themes being system identification, model reduction and approximation, robust controller synthesis, and control of aircraft and engines. The articles in this volume are a tribute to Professor Glover's seminal work in these areas.
Control of Uncertain Systems: Modelling, Approximation, and Design by Bruce A. Francis,Malcolm C. Smith,Jan C. Willems Pdf
This is a collection of articles by friends, co-authors, colleagues, and students of Keith Glover, Professor of Engineering at the University of Cambridge, on the occasion of his 60th birthday. Professor Glover's work spans a variety of topics, including system identification, model reduction and approximation, robust controller synthesis, and control of aircraft and engines. The collection is a tribute to Professor Glover's seminal work in these areas.
Adaptive Backstepping Control of Uncertain Systems by Jing Zhou,Changyun Wen Pdf
This book employs the powerful and popular adaptive backstepping control technology to design controllers for dynamic uncertain systems with non-smooth nonlinearities. Various cases including systems with time-varying parameters, multi-inputs and multi-outputs, backlash, dead-zone, hysteresis and saturation are considered in design and analysis. For multi-inputs and multi-outputs systems, both centralized and decentralized controls are addressed. This book not only presents recent research results including theoretical success and practical development such as the proof of system stability and the improvement of system tracking and transient performance, but also gives self-contained coverage of fundamentals on the backstepping approach illustrated with simple examples. Detail description of methodologies for the construction of adaptive laws, feedback control laws and associated Lyapunov functions is systematically provided in each case. Approaches used for the analysis of system stability and tracking and transient performances are elaborated. Two case studies are presented to show how the presented theories are applied.
Robust Control of Uncertain Dynamic Systems by Rama K. Yedavalli Pdf
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.
This book is a comprehensive exposition of the theory, algorithms, and applications of structured low-rank approximation. Local optimization methods and effective suboptimal convex relaxations for Toeplitz, Hankel, and Sylvester structured problems are presented. A major part of the text is devoted to application of the theory with a range of applications from systems and control theory to psychometrics being described. Special knowledge of the application fields is not required. The second edition of /Low-Rank Approximation/ is a thoroughly edited and extensively rewritten revision. It contains new chapters and sections that introduce the topics of: • variable projection for structured low-rank approximation;• missing data estimation;• data-driven filtering and control;• stochastic model representation and identification;• identification of polynomial time-invariant systems; and• blind identification with deterministic input model. The book is complemented by a software implementation of the methods presented, which makes the theory directly applicable in practice. In particular, all numerical examples in the book are included in demonstration files and can be reproduced by the reader. This gives hands-on experience with the theory and methods detailed. In addition, exercises and MATLAB^® /Octave examples will assist the reader quickly to assimilate the theory on a chapter-by-chapter basis. “Each chapter is completed with a new section of exercises to which complete solutions are provided.” Low-Rank Approximation (second edition) is a broad survey of the Low-Rank Approximation theory and applications of its field which will be of direct interest to researchers in system identification, control and systems theory, numerical linear algebra and optimization. The supplementary problems and solutions render it suitable for use in teaching graduate courses in those subjects as well.
Dynamic Modeling, Predictive Control and Performance Monitoring by Biao Huang,Ramesh Kadali Pdf
A typical design procedure for model predictive control or control performance monitoring consists of: 1. identification of a parametric or nonparametric model; 2. derivation of the output predictor from the model; 3. design of the control law or calculation of performance indices according to the predictor. Both design problems need an explicit model form and both require this three-step design procedure. Can this design procedure be simplified? Can an explicit model be avoided? With these questions in mind, the authors eliminate the first and second step of the above design procedure, a “data-driven” approach in the sense that no traditional parametric models are used; hence, the intermediate subspace matrices, which are obtained from the process data and otherwise identified as a first step in the subspace identification methods, are used directly for the designs. Without using an explicit model, the design procedure is simplified and the modelling error caused by parameterization is eliminated.
Assessment and Future Directions of Nonlinear Model Predictive Control by Rolf Findeisen,Frank Allgöwer,Lorenz Biegler Pdf
Thepastthree decadeshaveseenrapiddevelopmentin the areaofmodelpred- tive control with respect to both theoretical and application aspects. Over these 30 years, model predictive control for linear systems has been widely applied, especially in the area of process control. However, today’s applications often require driving the process over a wide region and close to the boundaries of - erability, while satisfying constraints and achieving near-optimal performance. Consequently, the application of linear control methods does not always lead to satisfactory performance, and here nonlinear methods must be employed. This is one of the reasons why nonlinear model predictive control (NMPC) has - joyed signi?cant attention over the past years,with a number of recent advances on both the theoretical and application frontier. Additionally, the widespread availability and steadily increasing power of today’s computers, as well as the development of specially tailored numerical solution methods for NMPC, bring thepracticalapplicabilityofNMPCwithinreachevenforveryfastsystems.This has led to a series of new, exciting developments, along with new challenges in the area of NMPC.
Realization Theory and Design of Digital Images by Yasumichi Hasegawa,Tatsuo Suzuki Pdf
This monograph offers a thorough examination of the description and design of digital images. Regarding digital images as special input/output relations in the authors’ previous book in the series Lecture Notes in Control and Information Sciences, the description problem of digital images is transformed into the realization problem of digital images. This book is intended for researchers and graduate students who specialize in image processing and system theory.
Recent Advances in Learning and Control by Vincent D. Blondel,Stephen P. Boyd,Hidenori Kimura Pdf
This volume is composed of invited papers on learning and control. The contents form the proceedings of a workshop held in January 2008, in Hyderabad that honored the 60th birthday of Doctor Mathukumalli Vidyasagar. The 14 papers, written by international specialists in the field, cover a variety of interests within the broader field of learning and control. The diversity of the research provides a comprehensive overview of a field of great interest to control and system theorists.
Control Systems Theory and Applications for Linear Repetitive Processes by Eric Rogers,Krzysztof Galkowski,David H. Owens Pdf
After motivating examples, this monograph gives substantial new results on the analysis and control of linear repetitive processes. These include further applications of the abstract model based stability theory which, in particular, shows the critical importance to the dynamics developed of the structure of the initial conditions at the start of each new pass, the development of stability tests and performance bounds in terms of so-called 1D and 2D Lyapunov equations. It presents the development of a major bank of results on the structure and design of control laws, including the case when there is uncertainty in the process model description, together with numerically reliable computational algorithms. Finally, the application of some of these results in the area of iterative learning control is treated --- including experimental results from a chain conveyor system and a gantry robot system.
Advanced Strategies in Control Systems with Input and Output Constraints by Sophie Tarbouriech,Germain Garcia,Adolf H. Glattfelder Pdf
Physical, safety and technological constraints suggest that control actuators can neither provide unlimited amplitude signals nor unlimited speed of reaction. The techniques described in this book are useful for industrial applications in aeronautical or space domains, and in the context of biological systems. Such methods are well suited for the development of tools that help engineers to solve analysis and synthesis problems of control systems with input and output constraints.
Reinforcement Learning and Approximate Dynamic Programming for Feedback Control by Frank L. Lewis,Derong Liu Pdf
Reinforcement learning (RL) and adaptive dynamic programming (ADP) has been one of the most critical research fields in science and engineering for modern complex systems. This book describes the latest RL and ADP techniques for decision and control in human engineered systems, covering both single player decision and control and multi-player games. Edited by the pioneers of RL and ADP research, the book brings together ideas and methods from many fields and provides an important and timely guidance on controlling a wide variety of systems, such as robots, industrial processes, and economic decision-making.
PID Control for Multivariable Processes by Qing-Guo Wang,Zhen Ye,Wen-Jian Cai,Chang-Chieh Hang Pdf
Thereare richtheories and designs for generalcontrolsystems,but usually, they will not lead to PID controllers. Noting that the PID controller has been the most popular one in industry for over ?fty years, we will con?ne our discussion hereto PIDcontrolonly. PID controlhasbeenanimportantresearchtopicsince 1950’s, and causes remarkable activities for the last two decades. Most of the existing works have been on the single variable PID control and its theory and design are well established, understood and practically applied. However, most industrial processes are of multivariable nature. It is not rare that the overall multivariable PID control system could fail although each PID loop may work well. Thus,demandforaddressingmultivariableinteractionsishighforsuccessful applicationofPIDcontrolinmultivariableprocessesanditisevidentfrommajor leading control companies who all rankedthe couplings of multivariable systems as the principal common problem in industry. There have been studies on PID control for multivariable processes and they provide some useful design tools for certaincases. But itis notedthat the existing worksaremainlyfor decentralized form of PID control and based on ad hoc methodologies. Obvious, multivariable PID control is much less understood and developed in comparison with the single variable case and actual need for industrial applications. Better theory and design have to be established for multivariable PID control to reach the same maturity and popularity as the single variable case. The present monograph puts together, in a single volume, a fairly comp- hensive, up-to-date and detailed treatment of PID control for multivariable p- cesses, from paring, gain and phase margins, to various design methods and applications.
Approximate and Noisy Realization of Discrete-Time Dynamical Systems by Yasumichi Hasegawa Pdf
This monograph deals with approximation and noise cancellation of dynamical systems which include linear and nonlinear input/output relations. It will be of special interest to researchers, engineers and graduate students who have specialized in ?ltering theory and system theory. From noisy or noiseless data, reductionwillbemade.Anewmethodwhichreducesnoiseormodelsinformation will be proposed. Using this method will allow model description to be treated as noise reduction or model reduction. As proof of the e?cacy, this monograph provides new results and their extensions which can also be applied to nonlinear dynamical systems. To present the e?ectiveness of our method, many actual examples of noise and model information reduction will also be provided. Using the analysis of state space approach, the model reduction problem may have become a major theme of technology after 1966 for emphasizing e?ciency in the ?elds of control, economy, numerical analysis, and others. Noise reduction problems in the analysis of noisy dynamical systems may havebecomeamajorthemeoftechnologyafter1974foremphasizinge?ciencyin control.However,thesubjectsoftheseresearcheshavebeenmainlyconcentrated in linear systems. In common model reduction of linear systems in use today, a singular value decompositionofaHankelmatrixisusedto?ndareducedordermodel.However, the existence of the conditions of the reduced order model are derived without evaluationoftheresultantmodel.Inthecommontypicalnoisereductionoflinear systems in use today, the order and parameters of the systems are determined by minimizing information criterion. Approximate and noisy realization problems for input/output relations can be roughly stated as follows: A. The approximate realization problem. For any input/output map, ?nd one mathematical model such that it is similar totheinput/outputmapandhasalowerdimensionthanthegivenminimalstate spaceofadynamicalsystemwhichhasthesamebehaviortotheinput/outputmap. B. The noisy realization problem.
Robot Motion and Control 2007 by Krzysztof R. Kozlowski Pdf
Robot Motion Control 2007 presents very recent results in robot motion and control. Forty-one short papers have been chosen from those presented at the sixth International Workshop on Robot Motion and Control held in Poland in June 2007. The authors of these papers have been carefully selected and represent leading institutions in this field.
