Aspects on Robust Control and Identification

G5 Doctoral dissertation (article)


Internal Authors/Editors


Publication Details

List of Authors: Stefan Tötterman
Publisher: Åbo Akademi
Place: Åbo
Publication year: 2019
ISBN: 978-952-12-310-9
eISBN: 978-952-12-3811-6


Abstract

This thesis treats different aspects on robust controller design and
model identification techniques. The controller design technique
proposes frequency-domain specifications for achieving a fixed-structure
controller with user specified optimality criteria. The optimization
based design method is iterative and it is based on direct shaping of
the frequency responses without a need to explicitly design any
weighting filters in contrast to classic loop-shaping methods.
Computational efficiency has been taken into account by utilizing linear
matrix equations for characterizing the frequency responses in the
time-domain. The proposed controller design method can be used for
designing any type of linear controllers, e.g. PID-type controllers, for
identified linear systems. Support vector regression (SVR) has several
inherent excellent features that can with advantage be utilized in
robust system identification. One of these is the usage of Vapnik’s
İ-insensitive loss function that gives robustness and insensitivity to
overtraining. Other features are the automatic computing of the
parameters used in SVR and the convex optimization that guarantees to
always find the global optimum. SVR has in this thesis been tailored by
modifying the kernel function to better fit several common model
identification problems. These are identification of state-dependent
parameter models or quasi-ARX models, smoothness priors models of linear
systems and nonlinear Wiener models. All these proposed identification
methods have been applied to examples of different systems. The results
have been either as good or even better compared to other corresponding
methods.


Last updated on 2019-20-10 at 03:35