Abstract of PhD thesis about NN and electrical drives

[Maurizio Cirrincione]

Dear Connectionists:

Please find herein the abstract of my PhD thesis
Diagnosis and Control of Electrical Drives Using Neural Networks
PhD in Electrical Engineering, University of Palermo, Italy.
This thesis has been successfully defended on the 3rd December 1996.
On the 23rd May 1997 at Vietri sul Mare (Salerno) the SIREN (Societa'
Italiana Reti Neuronali Italian Society of Neural Network) and the IIASS
(Istituto Internazionale per gli Alti Studi Scientifici International
Institute for High Scientific Studies) awarded it the prize "Edoardo R.
Caianello '97" for the best Italian PhD thesis on neural networks.
It is not yet available in the web, but I hope it will be soon. Meanwhile
if you want a copy I can send you one by ordinary mail.

ABSTRACT:
Diagnosis and Control of Electrical Drives Using Neural Networks
by Maurizio Cirrincione

It is deeply known that the diagnosis of a system is a process consisting
of the execution of suitable measurements and tests and, as a result,  the
recognition of the operating state and the behaviour of the system itself
in order to fix the possible course of action to undertake for correcting
this behaviour. The technique that develops the diagnosis is called
diagnostics, while the one which develops the corrective actions is called
maintenance or also control. In particular in an electrical drive connected
with a load, the automatic operation may require an on-line closed-loop
control where an artificial-based block interprets the load conditions and
decides, on the basis of the recognition of the operating condition and
behaviour, the control actions to undertake on the motor through the power
converter. Both the processing of the measured data and the control action
can contain neural network parts.

This thesis therefore deals with use of neural networks for controlling and
diagnosing an electrical drive by describing some original applications.
More importance has been given to the engineering and experimental aspects
of these applications than to a deep theoretical approach, in order to
prove the suitability of these neural network techiques in a particular
domain of industrial applications.

In chapter 1 the general problems of control in electrical drives are
discussed. The need of adaptive on-line control is emphasized and a brief
overview of innovative techiques, such as those based on expert systems,
fuzzy logic and neural networks, is then presented.

In chapter 2 the two neural networks used by the author for the control of
electrical drives are described. The first is the well-known
backpropagation neural network (BPN) and the second is a new neural
network, called PLN (Progressive Learning Network). In particular the
latter is presented and compared with the former and it is highlighted that
the PLN is more suitable than BPN for adaptive on-line real-time control as
it requires no separate training and production phases.

Chapter 3 deals with the main neuro-control techniques and their problems.
The complementarity and continuity of these methods with the traditional
techniques is emphasized.

Chapter 4 describes the BPN-based supervised control of a stepper motor .
It is shown here that a BPN can work as a robust controller of a stepper
motor and this result has been verified experimentally. A suitable test-bed
has been set up where the electrical drive is supervised by a neural
network hosted on a PC. Moreover a comparison with a traditional algorithm
is carried out. In the end the reliability of such neural controller is
verified in the presence of faults of some of its components. It is
remarked that hardly any test-beds for verifying neuro-controllers of
electrical drives have been realised, since most applications in this
domain of electrical drives have beed mostly carried out in simulation.

Chapter 5 deals with the use of neural networks for realising a controller
for high-performance dc drives. The target is the control of the rotational
speed so as to follow the speed reference accurately. The innovation which
is presented concerns the use of the direct inverse control with
generalised and specialised learning for identifying the inverse model of a
DC motor with separate excitation through a BPN and a PLN. The suitability
of the BPN  is verified both in simulation and on an experimental test-bed
even in presence of a speed variable load, resulting in a non-linear
controlled system. Subsequently the PLN is applied for the on-line control
based on specialised learning. It is shown that this approach can control
the electrical drive without a persistent excitation, in presence even of
variations of the load or of the parameters of the drive, with a noisy
environment. This new neuro-controller ia capable of adapting on-line to
any new working condition as it is based on a neural network varying the
number of its hidden neurons to learn situations non previously encountered
or to forget rare ones.

Chapter 6 gives an overview of the diagnosis of electrical drives for fault
protection, maintenance, fault detection and evalution of performances.
After showing traditional diagnosis techniques for each component of the
drive, a brief survey of future trends in this field is described. The
target of this chapter is to place the technique of neural networks in the
framework of the diagnosis of electrical drives.

Chapter 7 describes the self-organising neural networks used for the
diagnosis, that is the well-known SOM of prof. Kohonen and the more recent
VQP algorithm of prof. Herault. The diagnosis is considered as a particular
case of pattern recognition.

Chapter 8 is dedicated to the application and implementation of the above
neural networks in the diagnosis of electrical drives. In particular it is
original the use of these networks for the real-time diagnosis of the
working conditions of a three-phase converter and an induction motor (ac
drive). In this application the VQP proved to be more suitable that
Kohonen's SOM for the projection of high dimensional input data onto a
reduced dimension output space, also for visualisation.

The conclusions present new problems that should be faced in the future.

Best Regards

Maurizio Cirrincione, PhD, C.Eng.
CERISEP - CNR
c/o Department of Electrical Engineering
University of Palermo
Viale delle Scienze
90128 PALERMO
ITALY

tel. 0039 91 484686
fax 0039 91 485555

http://wwwcerisep.diepa.unipa.it/