Modelling and Control of Dynamic Systems Using Gaussian Process Models
Springer International Publishing (Verlag)
978-3-319-79327-6 (ISBN)
This monograph opens up new horizons for engineers and researchers inacademia and in industry dealing with or interested in new developments in thefield of system identification and control. It emphasizes guidelines forworking solutions and practical advice for their implementation rather than thetheoretical background of Gaussian process (GP) models. The book demonstratesthe potential of this recent development in probabilistic machine-learningmethods and gives the reader an intuitive understanding of the topic. Thecurrent state of the art is treated along with possible future directions forresearch.
Systems control design relies on mathematical models and these may bedeveloped from measurement data. This process of system identification, whenbased on GP models, can play an integral part of control design in data-basedcontrol and its description as such is an essential aspect of the text. Thebackground of GP regression is introduced first with system identification andincorporation of prior knowledge then leading into full-blown control. The bookis illustrated by extensive use of examples, line drawings, and graphicalpresentation of computer-simulation results and plant measurements. Theresearch results presented are applied in real-life case studies drawn fromsuccessful 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 ofdynamic 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.
System Identification with GP Models.- Incorporation of Prior Knowledge.- Control with GP Models.- Trends, Challenges and Research Opportunities.- Case Studies.
| Erscheinungsdatum | 20.12.2018 |
|---|---|
| Reihe/Serie | Advances in Industrial Control |
| Zusatzinfo | XVI, 267 p. 117 illus., 17 illus. in color. |
| Verlagsort | Cham |
| Sprache | englisch |
| Maße | 155 x 235 mm |
| Gewicht | 537 g |
| Themenwelt | Mathematik / Informatik ► Mathematik |
| Naturwissenschaften ► Chemie ► Technische Chemie | |
| Technik ► Elektrotechnik / Energietechnik | |
| Schlagworte | Atmospheric Ozone • fault detection • Fault Diagnosis • Gas-Liquid Separator • Gas–Liquid Separator • Gaussian Process Model • Hydraulic Plant • machine learning applications • Process Control • System Identification • Urban Traffic Control |
| ISBN-10 | 3-319-79327-6 / 3319793276 |
| ISBN-13 | 978-3-319-79327-6 / 9783319793276 |
| Zustand | Neuware |
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