Compensation of parameters variations in induction motor drives using a neural network

Author

Fodor, D. ; Griva, G. ; Profumo, F.

Author_Institution

Fac. of Electron., Politehnic Univ. of Bucharest, Romania

Volume

2

fYear

1995

fDate

18-22 Jun 1995

Firstpage

1307

Abstract

In this paper, the possibility of using a neural network (NN) to compensate parameter variations in an indirect field oriented (IFO) controller is studied and presented. In particular, a three-layer NN has been designed and trained offline with a steady state mathematical model of an IFO control scheme in detuning operations. Thus, the trained NN has been added to the controller as a black box to compensate for motor parameters variations. The motor controller behaviour with the NN black box has been studied in tuning and detuning conditions. Complete simulation results for a 4.0 kW induction motor driven by a CRPWM inverter with IFO controller are shown and discussed

Keywords

PWM invertors; compensation; control system analysis; control system synthesis; induction motor drives; learning (artificial intelligence); machine control; machine theory; neural nets; robust control; 4 kW; PWM inverter; control design; detuning; indirect field oriented control; induction motor drives; neural network; offline training; parameter variations compensation; simulation; three-layer neural net; tuning; Artificial intelligence; Artificial neural networks; Biological neural networks; Induction motor drives; Induction motors; Intelligent networks; Mathematical model; Neural networks; Neurons; Steady-state;

fLanguage

English

Publisher

ieee

Conference_Titel

Power Electronics Specialists Conference, 1995. PESC '95 Record., 26th Annual IEEE

Conference_Location

Atlanta, GA

Print_ISBN

0-7803-2730-6

Type

conf

DOI

10.1109/PESC.1995.474983

Filename

474983