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
- 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
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.