Parametric Nonlinear System Identification of Brushless DC Motors for Aerobot Drive
(*) Corresponding author
the author of the article can submit here a request for assignment of a DOI number to this resource!
Cost of the service: euros 10,00 (for a DOI)
This paper presents an effective identification framework to build a more accurate nonlinear model for brushless DC motors, which are frequently used as drive systems of micro electromechanical unmanned aerial vehicles. The identification method uses a two-step procedure to obtain a fully parametric model. First, the initial model of system is estimated. Next, a coefficient shrinkage method is used for model structure selection. There are two main highlight processes in this paper. One is the use of Hammerstein series models for identification, which leads to a trade-off between model accuracy and complexity. Another is the use of modified nonnegative garrote method for model reduction and finding the true model order. The proposed method is validated on simulated system and finally used to identify a brushless DC motor system. The obtained nonlinear model of brushless DC motor is of high performance and controllable model complexity
Copyright © 2013 Praise Worthy Prize - All rights reserved.
Shafiei, M., Bahrami Kouhshahi, M., Sharifian, M.B.B., Feyzi, M.R., Position sensorless for controlling Brushless DC motor drives based on sliding mode and RLS estimators using NSGA-II Algorithm optimization, (2011) International Review on Modelling and Simulations (IREMOS), 4 (3), pp. 1121-1131.
S. Bouabdallah, P. Murrieri, R. Siegwart, Design and control of an indoor micro quadrotor, IEEE International Conference on Robotics and Automation, 2004, New Orleans, USA.
L. Besnard, Y. B. Shtessel, B. Landrum, Quadrotor vehicle control via sliding mode controller driven by sliding mode disturbance observer, Journal of the Franklin Institute, vol. 349, 2012, pp. 658-684.
J. C. Ralston, A. M. Zoubir, B. Boashash, Identification of a class of nonlinear systems under stationary non-Gaussian excitation, IEEE Transaction on Signal Processing, vol. 45, no. 3, 1997, pp.719-735.
M. Rebillat, R. Hennequin, E. Corteel, B. Katz, Identification of cascade of Hammerstein models for the description of non-linearities in vibrating devices, Journal of Sound and Vibration, vol. 330, no. 5, 2010, pp. 1018-1038.
M. Schoukens, Y. Rolain, R. Pintelon, J. Schoukens, Semi-parametric identification of parallel Hammerstein systems, the UKACC International Conference on Control, 2010, Coventry, UK.
K. I. Kim, E. J. Powers, A digital method of modeling quadratically nonlinear systems with a general random input, IEEE Transactions on ASSP, vol. 36, no. 11, 1988, pp. 1758-1769.
M. Schoukens, R. Pintelon, Y. Rolain, Parametric identification of parallel Hammerstein systems, IEEE Transaction on Instrumentation and Measurement, vol. 60, no. 12, 2011, pp.3931-3938.
A. M. Zoubir, J. C. Ralston, D. R. Iskander, Optimal selection of model order for a class of nonlinear system using the bootstrap, IEEE International Conference on Acoustics, Speech and Signal Processing, 1997, Munich, German.
L. Breiman, Better subset regression using the nonnegative garrote, Technometrics, vol. 37, no. 4, 1995, pp. 373-384.
C. Lyzell, J. Roll, L. Ljung, The use of nonnegative garrote for order selection of ARX models, the 47th IEEE Conference on Decision and Control, 2008, Cancun, Mexico.
J. Roll, Piecewise linear solution paths with application to direct weight optimization, Automatica, vol. 44, no. 11, 2008, pp. 2745-2753.
Filippetti, F., Franceschini, G., Ometto, A., Meo, S., Survey of neural network approach for induction machine on-line diagnosis, Proceedings of the 1996 31st Universities Power Engineering Conference. Part 1 (of 3);Iraklio, Greece;18 September 1996through20 September 1996, pp. 17-20.
- There are currently no refbacks.
Please send any question about this web site to email@example.com
Copyright © 2005-2023 Praise Worthy Prize