Skip to main content
  • Book
  • © 2016

Modelling and Control of Dynamic Systems Using Gaussian Process Models

Authors:

(view affiliations)
  • Explains how theoretical work in Gaussian process models can be applied in the control of real industrial systems

  • Provides the engineer with practical guidance is not unduly encumbered by complicated theory

  • Shows the academic researcher the potential for real-world application of a recent branch of control theory

  • Includes supplementary material: sn.pub/extras

Part of the book series: Advances in Industrial Control (AIC)

Buying options

eBook
USD 119.00
Price excludes VAT (USA)
  • ISBN: 978-3-319-21021-6
  • Instant PDF download
  • Readable on all devices
  • Own it forever
  • Exclusive offer for individuals only
  • Tax calculation will be finalised during checkout
Softcover Book
USD 159.99
Price excludes VAT (USA)
Hardcover Book
USD 159.00
Price excludes VAT (USA)

This is a preview of subscription content, access via your institution.

Table of contents (6 chapters)

  1. Front Matter

    Pages i-xvi
  2. Introduction

    • Juš Kocijan
    Pages 1-20
  3. System Identification with GP Models

    • Juš Kocijan
    Pages 21-102
  4. Incorporation of Prior Knowledge

    • Juš Kocijan
    Pages 103-146
  5. Control with GP Models

    • Juš Kocijan
    Pages 147-208
  6. Case Studies

    • Juš Kocijan
    Pages 213-252
  7. Back Matter

    Pages 253-267

About this book

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-traffic signal modelling and reconstruction; and
  • prediction of atmospheric ozone concentration.

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

Advances in Industrial Control aims to report and encourage the transfer of technology in control engineering. The rapid development of control technology has an impact on all areas of the control discipline. The series offers an opportunity for researchers to present an extended exposition of new work in all aspects of industrial control.

Keywords

  • Atmospheric Ozone
  • Fault Detection
  • Fault Diagnosis
  • Gas–Liquid Separator
  • Gaussian Process Model
  • Hydraulic Plant
  • Machine Learning Applications
  • Process Control
  • System Identification
  • Urban Traffic Control

Authors and Affiliations

  • Department of Systems and Control, Jožef Stefan Institute, Ljubljana, Slovenia and Centre for Systems and Information Technologies, University of Nova Gorica, Nova Gorica, Slovenia

    Juš Kocijan

About the author

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.

Bibliographic Information

Buying options

eBook
USD 119.00
Price excludes VAT (USA)
  • ISBN: 978-3-319-21021-6
  • Instant PDF download
  • Readable on all devices
  • Own it forever
  • Exclusive offer for individuals only
  • Tax calculation will be finalised during checkout
Softcover Book
USD 159.99
Price excludes VAT (USA)
Hardcover Book
USD 159.00
Price excludes VAT (USA)