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Modelling and Control of Dynamic Systems Using Gaussian Process Models

  • Juš Kocijan

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

Table of contents

  1. Front Matter
    Pages i-xvi
  2. Juš Kocijan
    Pages 1-20
  3. Juš Kocijan
    Pages 21-102
  4. Juš Kocijan
    Pages 103-146
  5. Juš Kocijan
    Pages 147-208
  6. Juš Kocijan
    Pages 213-252
  7. Back Matter
    Pages 253-267

About this book

Introduction

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

  • Juš Kocijan
    • 1
  1. 1.Department of Systems and Control, Jožef Stefan Institute, Ljubljana, Slovenia and Centre for Systems and Information TechnologiesUniversity of Nova GoricaNova GoricaSlovenia

Bibliographic information

  • DOI https://doi.org/10.1007/978-3-319-21021-6
  • Copyright Information Springer International Publishing Switzerland 2016
  • Publisher Name Springer, Cham
  • eBook Packages Engineering
  • Print ISBN 978-3-319-21020-9
  • Online ISBN 978-3-319-21021-6
  • Series Print ISSN 1430-9491
  • Series Online ISSN 2193-1577
  • Buy this book on publisher's site
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