---

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.

---

Introduction.- System Identification with Gaussian Process Models.- Incorporation of Prior Knowledge.- Control with Gaussian Process Models.- Fault Diagnosis and Isolation.- Case Studies.

---

Jus 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.

---