True Digital Control

C. James Taylor 지음

Wiley 2013년 05월 29일 출간

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True Digital Control: Statistical Modelling and Nonn-Minimal State Space Designdevelops a true digital control design philosophy that encompasses datan-based model identification, through to control algorithm design, robustness evaluation and implementation. With a heritage from both classical and modern control system synthesis, this book is supported by detailed practical examples based on the authors' research into environmental, mechatronic and robotic systems. Treatment of both statistical modelling and control design under one cover is unusual and highlights the important connections between these disciplines.Starting from the ubiquitous proportionaln-integral controller, and with essential concepts such as pole assignment introduced using straightforward algebra and block diagrams, this book addresses the needs of those students, researchers and engineers, who would like to advance their knowledge of control theory and practice into the state space domain; and academics who are interested to learn more about nonn-minimal state variable feedback control systems. Such nonn-minimal state feedback is utilised as a unifying framework for generalised digital control system design. This approach provides a gentle learning curve, from which potentially difficult topics, such as optimal, stochastic and multivariable control, can be introduced and assimilated in an interesting and straightforward manner.Key features:Covers both system identification and control system design in a unified mannerIncludes practical design case studies and simulation examplesConsiders recent research into timen-variable and staten-dependent parameter modelling and control, essential elements of adaptive and nonlinear control system design, and the deltan-operator (the discreten-time equivalent of the differential operator) systemsAccompanied by a website hosting MA

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Preface xiiiList of Acronyms xv1 Introduction 11.1 Control Engineering and Control Theory 21.2 Classical and Modern Control 51.3 The Evolution of the NMSS Model Form 81.4 True Digital Control 111.5 Book Outline 121.6 Concluding Remarks 13References 142 Discrete-Time Transfer Functions 172.1 Discrete-Time TF Models 182.2 Stability and the Unit Circle 242.3 Block Diagram Analysis 262.4 Discrete-Time Control 282.5 Continuous to Discrete-Time TF Model Conversion 362.6 Concluding Remarks 38References 383 Minimal State Variable Feedback 413.1 Controllable Canonical Form 443.2 Observable Canonical Form 503.3 General State Space Form 533.4 Controllability and Observability 583.5 Concluding Remarks 61References 624 Non-Minimal State Variable Feedback 634.1 The NMSS Form 644.2 Controllability of the NMSS Model 684.3 The Unity Gain NMSS Regulator 694.4 Constrained NMSS Control and Transformations 774.5 Worked Example with Model Mismatch 814.6 Concluding Remarks 85References 865 True Digital Control for Univariate Systems 895.1 The NMSS Servomechanism Representation 935.2 Proportional-Integral-Plus Control 985.3 Pole Assignment for PIP Control 1015.4 Optimal Design for PIP Control 1105.5 Case Studies 1165.6 Concluding Remarks 119References 1206 Control Structures and Interpretations 1236.1 Feedback and Forward Path PIP Control Structures 1236.2 Incremental Forms for Practical Implementation 1316.3 The Smith Predictor and its Relationship with PIP Design 1376.4 Stochastic Optimal PIP Design 1426.5 Generalised NMSS Design 1536.6 Model Predictive Control 1576.7 Concluding Remarks 163References 1647 True Digital Control for Multivariable Syst

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저자(글) C. James Taylor

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James Taylor received his B.Sc. (Hons.) and Ph.D degrees from Lancaster University, UK, before joining the academic staff of the Engineering Department in 2000. His research focuses on control system design and system identification, with applied work spanning robotics, transport, energy, agriculture and the environment. This has led to over 100 publications in the open literature and widespread impact across a variety of academic and industryn-based users. He has pioneered new advances in nonn-minimal state space design, and coordinates development of the welln-known Captain Toolbox for Time Series Analysis and Forecasting. He is a Fellow of the Institution of Engineering and Technology, and supervises students across a spectrum of mechanical, electronic, nuclear and chemical engineering disciplines.Peter Young is Emeritus Professor at Lancaster University, UK, and Adjunct Professor at the Australian National University, Canberra. After an apprenticeship in the Aerospace Industry and B.Tech., MSc. degrees from Loughborough University, he obtained his Ph.D degree from Cambridge University in 1970 and became University Lecturer in Engineering and a Fellow of Clare Hall at Cambridge University. After seven years as Professorial Fellow at the Australian National University, he then moved to Lancaster University in 1981 as Professor and Head of the Environmental Science Department. He is well known for his work on optimal identification, datan-based mechanistic modelling and adaptive forecasting, with applications in areas ranging from the environment, through ecology, biology and engineering to business and macron-economics.Until his recent retirement, Arun Chotai was Senior Lecturer in the Lancaster Environment Centre at Lancaster University, UK. He holds a Ph.D in Systems and Control and a B.Sc. (Hons.) in Mathematics, both from the University of Bath, UK. Following his appointment to an academic position at Lan