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Modelling and Control of Dynamic Systems Using Gaussian Process Models

:	Juš Kocijan
:	Modelling and Control of Dynamic Systems Using Gaussian Process Models
:	Springer-Verlag
:	9783319210216
:	Advances in Industrial Control
:	1
:	CHF 124.70
:

:	Elektronik, Elektrotechnik, Nachrichtentechnik
:	English

:	281
:	Wasserzeichen/DRM
:	PC/MAC/eReader/Tablet
:	PDF

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. 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 successful applications including:

a gas-liquid separator control;
urban-traffi signal modelling and reconstruction; and
prediction of atmospheric ozone concentration.

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

Juš 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.

	Series Editors’ Foreword	6
	Preface	8
	Contents	11
	Symbols and Notation	13
	Acronyms	15
	1 Introduction	17
	1.1 Introduction to Gaussian-Process Regression	19
	1.1.1 Preliminaries	19
	1.1.2 Gaussian-Process Regression	23
	1.2 Relevance	32
	1.3 Outline of the Book	33
	References	34
	2 System Identification with GP Models	37
	2.1 The Model Purpose	41
	2.2 Obtaining Data---Design of the Experiment ƒ	42
	2.3 Model Setup	44
	2.3.1 Model Structure	44
	2.3.2 Selection of Regressors	49
	2.3.3 Covariance Functions	51
	2.4 GP Model Selection	63
	2.4.1 Bayesian Model Inference	64
	2.4.2 Marginal Likelihood---Evidence Maximisation	66
	2.4.3 Estimation and Model Structure	72
	2.4.4 Selection of Mean Function	75
	2.4.5 Asymptotic Properties of GP Models	77
	2.5 Computational Implementation	78
	2.5.1 Direct Implementation	78
	2.5.2 Indirect Implementation	80
	2.5.3 Evolving GP Models	86
	2.6 Validation	91
	2.7 Dynamic Model Simulation	96
	2.7.1 Numerical Approximation	97
	2.7.2 Analytical Approximation of Statistical Moments with a Taylor Expansion	97
	2.7.3 Unscented Transformation	98
	2.7.4 Analytical Approximation with Exact Matching of Statistical Moments	99
	2.7.5 Propagation of Uncertainty	100
	2.7.6 When to Use Uncertainty Propagation?	102
	2.8 An Example of GP Model Identification	103
	References	111
	3 Incorporation of Prior Knowledge	119
	3.1 Different Prior Knowledge and Its Incorporation	119
	3.1.1 Changing Input--Output Data	120
	3.1.2 Changing the Covariance Function	122
	3.1.3 Combination with the Presumed Structure	122
	3.2 Wiener and Hammerstein GP Models	123
	3.2.1 GP Modelling Used in the Wiener Model	124
	3.2.2 GP Modelling Used in the Hammerstein Model	129
	3.3 Incorporation of Local Models	134
	3.3.1 Local Models Incorporated into a GP Model	138
	3.3.2 Fixed-Structure GP Model	148
	References	159
	4 Control with GP Models	163
	4.1 Control with an Inverse Dynamics Model	166
	4.2 Optimal Control	171
	4.3 Model Predictive Control	174
	4.4 Adaptive Control	202
	4.5 Gain Scheduling	204
	4.6 Model Identification Adaptive Control	209
	4.7 Control Using Iterative Learning	214
	References	219
	5 Trends, Challenges and Research Opportunities	225
	References	227
	6 Case Studies	229
	6.1 Gas--Liquid Separator Modelling and Control	230
	6.2 Faulty Measurements Detection and Reconstruction in Urban Traffic	246
	6.3 Prediction of Ozone Concentration in the Air	257
	References	266
	Appendix A Mathematical Preliminaries	269
	Appendix B Predictions	273
	Appendix C Matlab Code	278
	Index	279