Ph.D. thesis available

Hans Andersen (andersen@elec.uq.edu.au)
Sun, 1 Nov 1998 23:35:31 +0100 (MET)

Hi,

My recently awarded Ph.D. thesis on adaptive fuzzy and neural control is
now available for download from the following URL:
http://www.elec.uq.edu.au/~annis/papers/HansThesis/theCOEM.html
The abstract and other details are included below.

Regards,
Hans Andersen,
Brisbane, Australia.

-------------------------------------------------------------------------

Title: The Controller Output Error Method

Ph.D. Thesis by Hans Christian Asminn Andersen
Supervised by Dr Louis Westphal
in the field of Electrical Engineering
at the Department of Computer Science
and Electrical Engineering,
University of Queensland,
Brisbane, Australia.
Awarded August, 1998.

Abstract

This thesis proposes the Controller Output Error Method (COEM)
for adaptation of neural and fuzzy controllers. Most existing methods
of neural adaptive control employ some kind of plant model which is
used to infer the error of the control signal from the error at the
plant
output. The error of the control signal is used to adjust the controller
parameters such that some cost function is optimized. Schemes of this
kind are generally described as being indirect.

Unlike these, COEM is direct since it does not require a plant model
in order to calculate the error of the control signal. Instead it
calculates
the control signal error by performing input matching. This entails
generating two control signals; the first control signal is applied to
the
plant and the second is inferred from the plantís response to the first
control signal. The controller output error is the difference between
these two control signals and is used by the COEM to adapt the
controller.

The method is shown to be a viable strategy for adaptation of
controllers based on nonlinear function approximation. This is done by
use of mathematical analysis and simulation experiments. It is proven
that, provided a given controller is sufficiently close to optimal at
the
commencement of COEM-adaptation, its parameters will converge,
and the control signal and the output of the plant being controlled will
be both bounded and convergent. Experiments demonstrate that the
method yields performance which is comparable or superior to that
yielded by other neural and linear adaptive control paradigms. In
addition to these results, this thesis shows the following:

- The convergence time of the COEM may be greatly reduced
by performing more than one adaptation during each sampling
period.

- It is possible to filter a reference signal in order to help ensure
that reachable targets are set for the plant.
An adaptive fuzzy system may be prevented from corrupting
the intuitive inter-pretation upon which it was originally
designed.

- Controllers adapted by COEM will perform best if a suitable
sampling rate is selected.

- The COEM may be expected to work as well on fuzzy
controllers as it does on neural controllers. Furthermore, the
extent of the functional equivalence between certain types of
neural networks and fuzzy inference systems is clarified, and a
new approach to the matrix formulation of a range of fuzzy
inference systems is proposed.

############################################################################
This message was posted through the fuzzy mailing list.
(1) To subscribe to this mailing list, send a message body of
"SUB FUZZY-MAIL myFirstName mySurname" to listproc@dbai.tuwien.ac.at
(2) To unsubscribe from this mailing list, send a message body of
"UNSUB FUZZY-MAIL" or "UNSUB FUZZY-MAIL yoursubscription@email.address.com"
to listproc@dbai.tuwien.ac.at
(3) To reach the human who maintains the list, send mail to
fuzzy-owner@dbai.tuwien.ac.at
(4) WWW access and other information on Fuzzy Sets and Logic see
http://www.dbai.tuwien.ac.at/ftp/mlowner/fuzzy-mail.info
(5) WWW archive: http://www.dbai.tuwien.ac.at/marchives/fuzzy-mail/index.html