A thesis on self-organising neuro-control

Dear all,

Just to let you know of the http availability of my thesis (130
pages) entitled "Incremental Polynomial Controller Networks: two
self-organising non-linear controllers" (it is a compressed
file. Please, gunzip the file to view or print it).

Get the file among other publications in 

http://www.mech.gla.ac.uk/~ericr/research.html

or download it directly

http://www.mech.gla.ac.uk/~ericr/pub/thesis.ps.gz


The keywords are: Neural Networks, Control, Modelling,
Self-Organisation.

Abstract: 

A step toward the development of a self-organising approach for the
control of non-linear system has been made by developing two
``incremental polynomial controller networks''. They constitute two
systematic self-organising approaches for the control of non-linear
systems with simple dynamics. Each network is composed of controllers
having a region of activity over the system operating space.  One is
the ``Incremental Clustered Controller Network'' (ICCN) and the other
one is the ``Incremental Model-Controller Network'' (IMCN). The two
controller networks differ by the manner they achieve the selection of
the currently valid local controllers. In the ICCN the controller
selection relies on a spatial clustering of the system operating
space. In the IMCN, each controller is selected according to the
performance of its connected model. Both these controller networks are
using an incremental algorithm to construct automaticly their
architecture. This algorithm is called the ``Incremental Network
Construction'' (INC). It is the INC which makes the ICCN and IMCN
self-organising approaches, since no {\it a priori} knowledge (except
the system order) is required to apply them.

Until now, the controller networks were composed of {\bf linear}
controllers. However, since a high number of linear controllers are
required to accurately control a significantly non-linear system, the
control capabilities of both these controller networks have been
further extended by using {\bf polynomial} controllers as building
block of the networks. An important advantage of polynomial functions
is their capacity to smoothly approximate non-linear systems and yet
have their parameters identifiable using linear regression methods
(e.g. least squares). It has been shown in this study that odd low order
polynomial functions are very suitable to model non-linear systems.
Illustrating examples indicated that the use of such a function as
building block of the controller networks implies an important
decrease of the number of controllers required to control accurately a
system. Moreover an improvement of the control performance was
proportional to the decrease of the number of controllers, with the
smoothness of the input transients being the main area of improvement.

It was clear from various control examples that the incremental
polynomial controller networks have a great potential in the control
of non-linear systems. However, the IMCN is a more satisfactory
approach than the ICCN. This is due to the clustering free approach
applied by the IMCN for the selection of the controllers. It makes the
IMCN insensitive to the number of quantities involving non-linearity
in the system. It is argued that the use of local controllers capable
of handling systems with complex dynamics makes this scheme one of the
most effective self-organising approaches for the control of
non-linear systems.





Best regards,

Eric Ronco
-----------------------------------------------------------------------------
|  Dr Eric Ronco                                                            |
|  Dt of Mechanical Engineering     E.mail : ericr at mech.gla.ac.uk           |
|  James Watt Building              WWW : http://www.mech.gla.ac.uk/~ericr  |
|  Glasgow University               Tel : (44) (0)141 330 4370              |
|  Glasgow G12 8QQ                  Fax : (44) (0)141 330 4343              |
|  Scotland, UK                                                             |
-----------------------------------------------------------------------------