Identification of DC motor drive system model using Radial Basis Function (RBF) Neural Network

Author

Yassin, Ihsan Mohd ; Taib, Mohd Nasir ; Aziz, Mohd Zafran Abdul ; Rahim, Norasmadi Abdul ; Tahir, Nooritawati Md ; Johari, Aiman

Author_Institution

Fac. of Electr. Eng., Univ. Teknol. Mara, Shah Alam, Malaysia

fYear

2011

fDate

25-28 Sept. 2011

Firstpage

13

Lastpage

18

Abstract

In this paper, we present a Radial Basis Function Neural Network (RBFNN)-based Nonlinear Auto-Regressive Model with Exegeneous Inputs (NARX) model of a DC motor drive controller model by (Rahim, 2004). Tests were conducted to measure the accuracy of the model (using One Step Ahead (OSA) and its validity (using correlation tests and histogram analysis). The resulting model produced Mean Square Error (MSE) of 8.53 × 10^-3 and 8.82 × 10^-3 on the training set and test set, respectively, while fulfilling all validation tests performed.

Keywords

DC motor drives; machine control; mean square error methods; neurocontrollers; radial basis function networks; DC motor drive controller model; DC motor drive system model; MSE; NARX model; OSA test; RBF neural network; correlation tests; histogram analysis; mean square error; nonlinear autoregressive model-with-exegeneous inputs; one-step ahead test; radial basis function neural network; test set; training set; validation test; Correlation; DC motors; Mathematical model; System identification; Testing; Torque; Training; NARX; radial basis function neural network; system identification;

fLanguage

English

Publisher

ieee

Conference_Titel

Industrial Electronics and Applications (ISIEA), 2011 IEEE Symposium on

Conference_Location

Langkawi

Print_ISBN

978-1-4577-1418-4

Type

conf

DOI

10.1109/ISIEA.2011.6108685

Filename

6108685