Publication: Identification and control of brushless DC motors using on-line trained artificial neural networks
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Issued Date
2002
Resource Type
File Type
application/pdf
Other identifier(s)
2-s2.0-84961875402
Rights Holder(s)
Scopus
Bibliographic Citation
Proceedings of the Power Conversion Conference-Osaka 2002, PCC-Osaka 2002. Vol 3, (2002), p.1290-1294
Suggested Citation
Tipsuwanporn V., Piyarat W., Tarasantisuk C. Identification and control of brushless DC motors using on-line trained artificial neural networks. Proceedings of the Power Conversion Conference-Osaka 2002, PCC-Osaka 2002. Vol 3, (2002), p.1290-1294. doi:10.1109/PCC.2002.998159 Retrieved from: https://hdl.handle.net/20.500.14740/6794
Author(s)
Abstract
This paper proposes high performance with simultaneous online identification and control designed for brushless DC motor drives. The dynamics of the motor/load are modeled online and controlled using an artificial neural network (ANN) based identification and control scheme incorporating three multilayer feedforward neural networks that are trained online using the gradient descent training algorithm. The control of the direct and quadrature components of the stator current successfully tracked a wide variety of trajectories. The control strategy adapts to the uncertainties of motor/load dynamics, and, in addition, learns their inherent nonlinearities. The use of feedforward neural networks makes the drives system robust, accurate and insensitive to parameter variations. © 2002 IEEE.
Subject(s)
AC motors
Control nonlinearities
DC motors
Electric drives
Electric machine control
Electric motors
Feedforward neural networks
Multilayer neural networks
Neural networks
Brushless dc motor drives
Control schemes
Control strategies
Gradient descent training
Multilayer feedforward neural networks
On-line identification
Quadrature components
Stator currents
Brushless DC motors
Control nonlinearities
DC motors
Electric drives
Electric machine control
Electric motors
Feedforward neural networks
Multilayer neural networks
Neural networks
Brushless dc motor drives
Control schemes
Control strategies
Gradient descent training
Multilayer feedforward neural networks
On-line identification
Quadrature components
Stator currents
Brushless DC motors