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E-raamat: Modelling and Control of Dynamic Systems Using Gaussian Process Models

Ju Kocijan

Formaat: PDF+DRM
Sari: Advances in Industrial Control
Ilmumisaeg: 21-Nov-2015
Kirjastus: Springer International Publishing AG
Keel: eng
ISBN-13: 9783319210216

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Formaat: PDF+DRM
Sari: Advances in Industrial Control
Ilmumisaeg: 21-Nov-2015
Kirjastus: Springer International Publishing AG
Keel: eng
ISBN-13: 9783319210216

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This monograph opens up new horizons for engineers and researchers in academia and in industry dealing with or interested in new developments in the field of system identification and control. It emphasizes guidelines for working solutions and practical advice for their implementation rather than the theoretical background of Gaussian process (GP) models. The book demonstrates the potential of this recent development in probabilistic machine-learning methods and gives the reader an intuitive understanding of the topic. The current state of the art is treated along with possible future directions for research. Systems control design relies on mathematical models and these may be developed from measurement data. This process of system identification, when based on GP models, can play an integral part of control design in data-based control and its description as such is an essential aspect of the text. The background of GP regression is introduced first with system identification and incorporation of prior knowledge then leading into full-blown control and then fault diagnosis and isolation. The book is illustrated by extensive use of examples, line drawings and graphical presentation of computer-simulation results and plant measurements. The research results presented are applied in real-life case studies drawn from more than 150 successful applications for various industrial users including:

a gas–liquid separator;
urban traffic control;
prediction of atmospheric ozone concentration; and
fault detection in a hydraulic plant.

A MATLAB® toolbox, for identification and simulation of dynamic GP models is provided for download.

1 Introduction

(20)

1.1 Introduction to Gaussian-Process Regression

(13)

1.1.1 Preliminaries

(4)

1.1.2 Gaussian-Process Regression

(9)

1.2 Relevance

(1)

1.3 Outline of the Book

(4)

References

(3)

2 System Identification with GP Models

(82)

2.1 The Model Purpose

(1)

2.2 Obtaining Data---Design of the Experiment, the Experiment Itself and Data Processing

(2)

2.3 Model Setup

(19)

2.3.1 Model Structure

(5)

2.3.2 Selection of Regressors

(2)

2.3.3 Covariance Functions

(12)

2.4 GP Model Selection

(15)

2.4.1 Bayesian Model Inference

(2)

2.4.2 Marginal Likelihood---Evidence Maximisation

(6)

2.4.3 Estimation and Model Structure

(3)

2.4.4 Selection of Mean Function

(2)

2.4.5 Asymptotic Properties of GP Models

(1)

2.5 Computational Implementation

(13)

2.5.1 Direct Implementation

(2)

2.5.2 Indirect Implementation

(6)

2.5.3 Evolving GP Models

(5)

2.6 Validation

(5)

2.7 Dynamic Model Simulation

(7)

2.7.1 Numerical Approximation

(1)

2.7.2 Analytical Approximation of Statistical Moments with a Taylor Expansion

(1)

2.7.3 Unscented Transformation

(1)

2.7.4 Analytical Approximation with Exact Matching of Statistical Moments

(1)

2.7.5 Propagation of Uncertainty

(2)

2.7.6 When to Use Uncertainty Propagation?

(1)

2.8 An Example of GP Model Identification

(16)

References

(8)

3 Incorporation of Prior Knowledge

103

(44)

3.1 Different Prior Knowledge and Its Incorporation

103

(4)

3.1.1 Changing Input--Output Data

104

(2)

3.1.2 Changing the Covariance Function

106

(1)

3.1.3 Combination with the Presumed Structure

106

(1)

3.2 Wiener and Hammerstein GP Models

107

(11)

3.2.1 GP Modelling Used in the Wiener Model

108

(5)

3.2.2 GP Modelling Used in the Hammerstein Model

113

(5)

3.3 Incorporation of Local Models

118

(29)

3.3.1 Local Models Incorporated into a GP Model

122

(10)

3.3.2 Fixed-Structure GP Model

132

(11)

References

143

(4)

4 Control with GP Models

147

(62)

4.1 Control with an Inverse Dynamics Model

150

(5)

4.2 Optimal Control

155

(3)

4.3 Model Predictive Control

158

(28)

4.4 Adaptive Control

186

(2)

4.5 Gain Scheduling

188

(5)

4.6 Model Identification Adaptive Control

193

(5)

4.7 Control Using Iterative Learning

198

(11)

References

203

(6)

5 Trends, Challenges and Research Opportunities

209

(4)

References

211

(2)

6 Case Studies

213

(40)

6.1 Gas--Liquid Separator Modelling and Control

214

(16)

6.2 Faulty Measurements Detection and Reconstruction in Urban Traffic

230

(11)

6.3 Prediction of Ozone Concentration in the Air

241

(12)

References

250

(3)

Appendix A Mathematical Preliminaries

253

(4)

Appendix B Predictions

257

(6)

Appendix C Matlab Code

263

(2)

Index

265

Ju Kocijan is a senior research fellow at the Department of Systems and Control, Jozef Stefan Institute, the leading Slovenian research institute in the field of natural sciences and engineering, and a Professor of Electrical Engineering at the University of Nova Gorica, Slovenia. His past experience in the field of control engineering includes teaching and research at the University of Ljubljana and visiting research and teaching posts at several European universities and research institutes. He has been active in applied research in automatic control through numerous domestic and international research grants and projects, in a considerable number of which he acted as project leader. His research interests include the modelling of dynamic systems with Gaussian process models, control based on Gaussian process models, multiple-model approaches to modelling and control, applied nonlinear control, Individual Channel Analysis and Design. His other experience includes: serving as one of the editors of the Engineering Applications of Artificial Intelligence journal and on the editorial boards of other research journals, serving as a member of IFAC Technical committee on Computational Intelligence in Control, actively participating as a member of numerous scientific-meeting international programme and organising committees. Prof. Kocijan is a member of various national and international professional societies in the field of automatic control, modelling and simulation.

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