Modelling and Control of Dynamic Systems Using Gaussian Process Models
Buch in deiner Nähe kaufen
Beschreibung
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.
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.
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
Autor*in
Juš Kocijan
Themen in »Modelling and Control of Dynamic Systems Using Gaussian Process Models«
Atmospheric Ozone Fault Detection Fault Diagnosis Gas–Liquid Separator Gaussian Process Model Hydraulic Plant Machine Learning Applications Process Control System Identification Urban Traffic Control