Overview
- 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)
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Table of contents (6 chapters)
Keywords
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.
Authors and Affiliations
About the author
Bibliographic Information
Book Title: Modelling and Control of Dynamic Systems Using Gaussian Process Models
Authors: Juš Kocijan
Series Title: Advances in Industrial Control
DOI: https://doi.org/10.1007/978-3-319-21021-6
Publisher: Springer Cham
eBook Packages: Engineering, Engineering (R0)
Copyright Information: Springer International Publishing Switzerland 2016
Hardcover ISBN: 978-3-319-21020-9Published: 07 December 2015
Softcover ISBN: 978-3-319-79327-6Published: 27 March 2019
eBook ISBN: 978-3-319-21021-6Published: 21 November 2015
Series ISSN: 1430-9491
Series E-ISSN: 2193-1577
Edition Number: 1
Number of Pages: XVI, 267
Number of Illustrations: 100 b/w illustrations, 17 illustrations in colour
Topics: Control and Systems Theory, Industrial Chemistry/Chemical Engineering, Statistics for Engineering, Physics, Computer Science, Chemistry and Earth Sciences