Neural Networks for modelling and control

Dear all,

Below are the description of two recently submitted papers dealing
with Neural networks for modelling and control. Respective drafts are
http available at

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

Any comments would be greatly appreciated


(1)

Eric Ronco and Peter J. Gawthrop, 1997 (submitted). Incremental Model
Reference Adaptive Polynomial Controllers Network. IEEE transaction on
Systems, man and cybernetics.

Abstract: The Incremental Model Reference Adaptive Polynomial
Controllers Network (IMRAPCN) is a completely autonomous adaptive non
linear controller. This algorithm consists of a Polynomial Controllers
Network (PCN) and an Incremental Network Construction (INC). The PCN
is a network of polynomial controllers each one being valid for a
different operating region of the system. The use of polynomial
controllers reduces significantly the number of controllers required
to control a non linear system while improving the control accuracy,
and the whole, without any drawbacks since polynomials are ""linear in
parameters functions''. Such a control system can be used for the
control of a possibly discontinuous non linear system, it is not
affected by the ""stability-plasticity dilemma'' and yet can have a
very clear architecture since it is composed of linear
controllers. The INC aims to resolve the clustering problem that faces
any such multi-controller method. The INC enables a very efficient
construction of the network as well as an accurate determination of
the region of validity of each controller.  Hence, the INC gives to
the PCN a complete autonomy since the clustering of the operating
space can be achieved without any a priori knowledge about the
system. Those advantages make clear the powerful control potential of
the IMRAPCN in the domain of autonomous adaptive control of non linear
systems.


(2)

Eric Ronco and Peter J. Gawthrop, 1997 (submitted). Polynomial Models
Network for system modelling and control. IEEE transaction on Neural
Networks.

Abstract: For the purposes of control, it is essential that the chosen
class of models is {em transparent} in the sense that the model
structure and parameters may be interpreted in the context of control
system design. The unclear representation of the system developed by
most of the neural networks highly restrict their application for
system modelling and control. Local computation tends to give clarity
into the neural representation. The local models network (LMN) applies
this method while adapting different models to different operating
regions of the system. This paper builds on the Local Model Network
approach and makes two main contributions: the network is not of a
fixed structure, but rather is constructed {em incrementally} on line
and the models are not linear but rather {em polynomial} in the
variables. The resulting network is named the incremental polynomial
model network (IPMN).  In this paper we show that the transparency of
the IPMN's representation makes model analysis and control design
straight forward. The many advantages of this approach exposed in
conclusion demonstrate the powerful capability of the IPMN to model
and control non-linear systems.



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|  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                                                             |
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