Taylor / Young / Chotai True Digital Control

Statistical Modelling and Non-Minimal State Space Design
1. Auflage 2013
ISBN: 978-1-118-53550-9
Verlag: John Wiley & Sons
Format: PDF
Kopierschutz: Adobe DRM (»Systemvoraussetzungen)

E-Book, Englisch, 360 Seiten, E-Book

True Digital Control: Statistical Modelling andNon-Minimal State Space Designdevelops a true digitalcontrol design philosophy that encompasses data-basedmodel identification, through to control algorithm design,robustness evaluation and implementation. With a heritage from bothclassical and modern control system synthesis, this book issupported by detailed practical examples based on theauthors' research into environmental, mechatronic and roboticsystems. Treatment of both statistical modelling and control designunder one cover is unusual and highlights the important connectionsbetween these disciplines.
Starting from the ubiquitous proportional-integralcontroller, and with essential concepts such as pole assignmentintroduced using straightforward algebra and block diagrams, thisbook addresses the needs of those students, researchers andengineers, who would like to advance their knowledge of controltheory and practice into the state space domain; and academics whoare interested to learn more about non-minimal state variablefeedback control systems. Such non-minimal state feedback isutilised as a unifying framework for generalised digital controlsystem design. This approach provides a gentle learning curve, fromwhich potentially difficult topics, such as optimal, stochastic andmultivariable control, can be introduced and assimilated in aninteresting and straightforward manner.
Key features:
* Covers both system identification and control systemdesign in a unified manner
* Includes practical design case studies and simulationexamples
* Considers recent research into time-variable andstate-dependent parameter modelling and control, essentialelements of adaptive and nonlinear control system design, and thedelta-operator (the discrete-time equivalent of thedifferential operator) systems
* Accompanied by a website hosting MATLAB examples
True Digital Control: Statistical Modelling andNon-Minimal State Space Design is a comprehensive andpractical guide for students and professionals who wish to furthertheir knowledge in the areas of modern control and systemidentification.

Autoren/Hrsg.

Taylor, C. James

Young, Peter C.

Chotai, Arun

Inhaltsverzeichnis

Preface xiii
List of Acronyms xv
1 Introduction 1
1.1 Control Engineering and Control Theory 2
1.2 Classical and Modern Control 5
1.3 The Evolution of the NMSS Model Form 8
1.4 True Digital Control 11
1.5 Book Outline 12
1.6 Concluding Remarks 13
References 14
2 Discrete-Time Transfer Functions 17
2.1 Discrete-Time TF Models 18
2.2 Stability and the Unit Circle 24
2.3 Block Diagram Analysis 26
2.4 Discrete-Time Control 28
2.5 Continuous to Discrete-Time TF Model Conversion 36
2.6 Concluding Remarks 38
References 38
3 Minimal State Variable Feedback 41
3.1 Controllable Canonical Form 44
3.2 Observable Canonical Form 50
3.3 General State Space Form 53
3.4 Controllability and Observability 58
3.5 Concluding Remarks 61
References 62
4 Non-Minimal State Variable Feedback 63
4.1 The NMSS Form 64
4.2 Controllability of the NMSS Model 68
4.3 The Unity Gain NMSS Regulator 69
4.4 Constrained NMSS Control and Transformations 77
4.5 Worked Example with Model Mismatch 81
4.6 Concluding Remarks 85
References 86
5 True Digital Control for Univariate Systems 89
5.1 The NMSS Servomechanism Representation 93
5.2 Proportional-Integral-Plus Control 98
5.3 Pole Assignment for PIP Control 101
5.4 Optimal Design for PIP Control 110
5.5 Case Studies 116
5.6 Concluding Remarks 119
References 120
6 Control Structures and Interpretations 123
6.1 Feedback and Forward Path PIP Control Structures 123
6.2 Incremental Forms for Practical Implementation 131
6.3 The Smith Predictor and its Relationship with PIP Design137
6.4 Stochastic Optimal PIP Design 142
6.5 Generalised NMSS Design 153
6.6 Model Predictive Control 157
6.7 Concluding Remarks 163
References 164
7 True Digital Control for Multivariable Systems 167
7.1 The Multivariable NMSS (Servomechanism) Representation168
7.2 Multivariable PIP Control 175
7.3 Optimal Design for Multivariable PIP Control 177
7.4 Multi-Objective Optimisation for PIP Control 186
7.5 Proportional-Integral-Plus Decoupling Control by AlgebraicPole Assignment 192
7.6 Concluding Remarks 195
References 196
8 Data-Based Identification and Estimation of Transfer FunctionModels 199
8.1 Linear Least Squares, ARX and Finite Impulse Response Models200
8.2 General TF Models 211
8.3 Optimal RIV Estimation 218
8.4 Model Structure Identification and Statistical Diagnosis231
8.5 Multivariable Models 243
8.6 Continuous-Time Models 248
8.7 Identification and Estimation in the Closed-Loop 253
8.8 Concluding Remarks 260
References 261
9 Additional Topics 265
9.1 The delta-Operator Model and PIP Control 266
9.2 Time Variable Parameter Estimation 279
9.3 State-Dependent Parameter Modelling and PIP Control 290
9.4 Concluding Remarks 298
References 298
A Matrices and Matrix Algebra 301
References 310
B The Time Constant 311
Reference 311
C Proof of Theorem 4.1 313
References 314
D Derivative Action Form of the Controller 315
E Block Diagram Derivation of PIP Pole Placement Algorithm317
F Proof of Theorem 6.1 321
Reference 322
G The CAPTAIN Toolbox 323
References 325
H The Theorem of D.A. Pierce (1972) 327
References 328
Index 329

Über Autor(innen)

James Taylor received his B.Sc. (Hons.) and Ph.D degreesfrom Lancaster University, UK, before joining the academic staff ofthe Engineering Department in 2000. His research focuses on controlsystem design and system identification, with applied work spanningrobotics, transport, energy, agriculture and the environment. Thishas led to over 100 publications in the open literature andwidespread impact across a variety of academic andindustry-based users. He has pioneered new advances innon-minimal state space design, and coordinates developmentof the well-known Captain Toolbox for Time Series Analysisand Forecasting. He is a Fellow of the Institution of Engineeringand Technology, and supervises students across a spectrum ofmechanical, electronic, nuclear and chemical engineeringdisciplines.
Peter Young is Emeritus Professor at LancasterUniversity, UK, and Adjunct Professor at the Australian NationalUniversity, Canberra. After an apprenticeship in the AerospaceIndustry and B.Tech., MSc. degrees from Loughborough University, heobtained his Ph.D degree from Cambridge University in 1970 andbecame University Lecturer in Engineering and a Fellow of ClareHall at Cambridge University. After seven years as ProfessorialFellow at the Australian National University, he then moved toLancaster University in 1981 as Professor and Head of theEnvironmental Science Department. He is well known for his work onoptimal identification, data-based mechanistic modelling andadaptive forecasting, with applications in areas ranging from theenvironment, through ecology, biology and engineering to businessand macro-economics.
Until his recent retirement, Arun Chotai was SeniorLecturer in the Lancaster Environment Centre at LancasterUniversity, 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 Lancaster in1984, he taught and developed modules in environmental systems,courses that were then unique to the UK in providing an advanced,quantitative approach to the subject. For many years, he was alsojoint head (with present co-author Peter Young) of theSystems and Control Group, which he helped to build into asuccessful research unit that became known internationally for itsresearch in the areas of system identification, time-seriesanalysis and control system design.