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Recurrent Neural Network Based Identification and Parameters Estimation of Induction Motor

Abdelhamid Aamoud(1*), Ahmed Hammouch(2)

(1) LGE Lab, ENSET, Mohammed V University in Rabat, Morocco
(2) LGE Lab, ENSET, Mohammed V University in Rabat, Morocco
(*) Corresponding author


DOI: https://doi.org/10.15866/iremos.v10i2.8360

Abstract


A nonlinear autoregressive network with exogenous inputs (NARX) model of induction motor (IM) based on neural network (NN) is built. The developed artificial neural network (ANN) is trained based only on the inputs-outputs (I-O) data obtained from an elaborated simulation model with a good input design. Levenberg-Maquart (LM) and least square (LS) algorithms are used for the estimation of parameters, and this, for different network structures. The optimal model providing the best compromise between accuracy and complexity is selected based on the Akaike (AIC) criterion. The built model takes into consideration saturation and transient state. This model is also the most recommended for predictive control. Simulation results including model order selection and inputs design are shown and discussed in an automotive application.
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Keywords


Nonlinear; NARX; Induction Motor; Neural Network; Saturation; Predictive Control; Levenberg-Maquart; Akaike; Automotive Application

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