Modelling and Control of Dynamic Systems Using Gaussian Process Models (Advances in Industrial Control) - Tapa blanda

Acerca del autor

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.

De la contraportada

This monograph opens upnew horizons for engineers and researchers in academia and in industry dealingwith or interested in new developments in the field of system identificationand control. It emphasizes guidelines for working solutions and practicaladvice for their implementation rather than the theoretical background ofGaussian process (GP) models. The book demonstrates the potential of thisrecent development in probabilistic machine-learning methods and gives thereader an intuitive understanding of the topic. The current state of the art istreated along with possible future directions for research.

Systems control designrelies on mathematical models and these may be developed from measurement data.This process of system identification, when based on GP models, can play anintegral part of control design in data-based control and its description assuch is an essential aspect of the text. The background of GP regression isintroduced first with system identification and incorporation of priorknowledge then leading into full-blown control. The book is illustrated byextensive use of examples, line drawings, and graphical presentation ofcomputer-simulation results and plant measurements. The research resultspresented are applied in real-life case studies drawn from successfulapplications 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 fordownload.

Advances in IndustrialControl aims to report andencourage the transfer of technology in control engineering. The rapiddevelopment of control technology has an impact on all areas of the controldiscipline. The series offers an opportunity for researchers to present anextended exposition of new work in all aspects of industrial control.