C. James Taylor & Peter C. Young 
True Digital Control [PDF ebook] 
Statistical Modelling and Non-Minimal State Space Design

True Digital Control: Statistical Modelling and
Non-Minimal State Space Designdevelops a true digital
control design philosophy that encompasses data-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 proportional-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 non-minimal state variable
feedback control systems. Such non-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 manner

* Includes practical design case studies and simulation
examples

* Considers recent research into time-variable and
state-dependent parameter modelling and control, essential
elements of adaptive and nonlinear control system design, and the
delta-operator (the discrete-time equivalent of the
differential operator) systems

* Accompanied by a website hosting MATLAB examples

True Digital Control: Statistical Modelling and
Non-Minimal State Space Design is a comprehensive and
practical guide for students and professionals who wish to further
their knowledge in the areas of modern control and system
identification.

表中的内容

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 Design
137

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) Representation
168

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 Algebraic
Pole Assignment 192

7.6 Concluding Remarks 195

References 196

8 Data-Based Identification and Estimation of Transfer Function
Models 199

8.1 Linear Least Squares, ARX and Finite Impulse Response Models
200

8.2 General TF Models 211

8.3 Optimal RIV Estimation 218

8.4 Model Structure Identification and Statistical Diagnosis
231

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 Algorithm
317

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

关于作者

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
industry-based users. He has pioneered new advances in
non-minimal state space design, and coordinates development
of the well-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, data-based mechanistic modelling and
adaptive forecasting, with applications in areas ranging from the
environment, through ecology, biology and engineering to business
and macro-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 Lancaster in
1984, 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 also
joint head (with present co-author Peter Young) of the
Systems and Control Group, which he helped to build into a
successful research unit that became known internationally for its
research in the areas of system identification, time-series
analysis and control system design.