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Formalism to design a neural network: Application to an induction machine drive coupled to a non linear mechanical load

Published online by Cambridge University Press:  15 November 2000

C. Forgez ,
B. Lemaire-Semail  and
J. P. Hautier
C. Forgez
Affiliation:
Laboratoire d'Électrotechnique et d'Électronique de Puissance de Lille (L2EP), École Centrale de Lille, BP 48, 59651 Villeneuve d'Ascq Cedex, France
B. Lemaire-Semail*
Affiliation:
Laboratoire d'Électrotechnique et d'Électronique de Puissance de Lille (L2EP), EUDIL, avenue Paul Langevin, 59655 Villeneuve d'Ascq, France
J. P. Hautier
Affiliation:
Laboratoire d'Électrotechnique et d'Électronique de Puissance de Lille (L2EP), ENSAM, bd. Louis XIV, 59046 Lille Cedex, France
*
[email protected]
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Abstract

This search deals with the control of a process in order to take into account non linearities without parameters identification. Neural networks properties are exploited for the modelling of non linear features, and a formalism is proposed to design a neural model which can be used directly as a controller. We apply this formalism to the modelling of a non linear mechanical load torque feature coupled to an induction machine in order to design a speed controller. A partial and a global neural method are presented. In order to overcome modelling errors or any process changes, an adaptive on line method is proposed. At last, simulation and experimental results are presented.

Keywords

Type
Research Article
Information
The European Physical Journal - Applied Physics , Volume 12 , Issue 2 , November 2000 , pp. 133 - 143
Copyright
© EDP Sciences, 2000

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References

Hush, D.R., Horne, B.G., IEEE Signal Process. Mag. 10, 8 (1993). CrossRef
D.E. Rumelhart, G.E. Hinton, R.J. Williams, Learning internal representations by error propagation,edited by D.E. Rumelhart, J.L. McClelland, Parallel Distributed Processing (Cambridge, MA: MIT Press, 1986) Vol. 1, Chap. 8.
Dan W. Patterson, Artificial Neural Networks, Theory and Applications (Prentice Hall, 1996).
B. François, Orthogonal Considerations in the Design of Neural Networks for Function Approximation, Mathematics and Computers in Simulation (Elsevier, July 1996), Vol. 41, pp. 95-108.
B. François, P. Borne, Design and initialization of multilayer neural network applied to function approximation, IMACS Workshop on Qualitative Reasonning and Decision Technologies, 16-18 June 1993, Barcelona, Spain.
C. Forgez, B. Lemaire Semail, J.P. Hautier, Induction machine control with neural network to consider non linear loads, ELECTRIMACS'96, Proceeding IMACS, St Nazaire, France, pp. 375-380.
J.P. Hautier, J. Faucher, Bull. Union Physiciens (n $^{\circ}$ 785, cahier de l'Enseignement Supérieur) 90, 167 (1996).
Y. Iwasaki, H.A. Simon, Causality and Model abstraction, Artificial Intelligence (Elsevier Science, 1994), Vol. 67, pp. 143-194.
Cerruto, E., Consoli, A., Raciti, A., Testa, A., IEEE Trans. Power Electron. 12, 1028 (1997). CrossRef
W.G. da Silva, P.P. Acarnley, Fuzzy logic controlled DC motor driven in the presence of load disturbance, EPE'97, Proceeding EPE, Trondheim, Sweden, pp. 2.386-2.391.
Maussion, P., Hissel, D., Eur. Phys. J. AP 3, 195 (1998). CrossRef
L. Baghli, H. Razik, A. Rezzoug, Neuro-fuzzy controller in a field-oriented control for induction motors, EPE'97, Proceeding EPE, Trondheim, Sweden, pp. 1.096-1.101.
W. Leonhard, Control of electrical drives (Springer Verlag, 1985).
Tzes, A., Pei. Yuan Peng, IEEE Trans. Ind. Electron. 42, 516 (1995). CrossRef
N. Pierlot, B. Lemaire Semail, P. Borne, Application of the neural networks to induction machine control, SPRANN'94, Proceeding IMACS International Symposium on Signal Processing, Robotics and Neural Networks, Lille, France, p. 407.
C. Forgez, Méthodologie de modélisation et de commande par réseaux de neurones pour des dispositifs électrotechniques non linéaires, Ph.D. thesis, 8 December 1998, Université de Lille 1.
N. Pierlot, B. Semail, Utilisation des Réseaux de Neurones dans la Commande du Moteur Asynchrone, Journée SEE, 7 avril 1994, Lille, France.
Dr. Tuan T. Ho, Stochastic fuzzy direct adaptive control, in Proceeding of the third IEEE International Conference on Fuzzy Systems, 1994, Vol. 2, pp. 750-755.