Artificial-neural-network-based sensorless nonlinear control of induction motors Academic Article

abstract

• In this paper, two architectures of artificial neural networks (ANNs) are developed and used to correct the performance of sensorless nonlinear control of induction motor systems. Feed-forward multilayer perception, an Elman recurrent ANN, and a two-layer feedforward ANN is used in the control process. The method is based on the use of ANN to get an appropriate correction for improving the estimated speed. Simulation and experimental results were carried out for the proposed control system. An induction motor fed by voltage source inverter was used in the experimental system. A digital signal processor and field-programmable gate arrays were used to implement the control algorithm. 2005 IEEE.

authors

• Abu-Rub, Haitham
• Toliyat, Hamid

published proceedings

• IEEE TRANSACTIONS ON ENERGY CONVERSION

author list (cited authors)

• Wlas, M., Krzeminski, Z., Guzinski, J., Abu-Rub, H., & Toliyat, H. A.

citation count

• 93

complete list of authors

• Wlas, M||Krzeminski, Z||Guzinski, J||Abu-Rub, H||Toliyat, HA

publication date

• September 2005

publisher

• Institute of Electrical and Electronics Engineers (IEEE)  Publisher

keywords

• Artificial Neural Networks (anns)
• Digital Signal Processor (dsp)
• Field-programmable Gate Arrays (fpgas)
• Induction Motor
• Nonlinear Control
• Observer System
• Sensorless Control

Digital Object Identifier (DOI)

• 10.1109/TEC.2005.847984

start page

• 520

end page

• 528

volume

• 20

issue

• 3

URL

• http%3A%2F%2Fdx.doi.org%2F10.1109%2Ftec.2005.847984