Please use this identifier to cite or link to this item:
Title: Artificial-neural-network-based sensorless nonlinear control of induction motors
Authors: Wlas, Miroslaw 
Krzemin´ski, Zbigniew 
Guzin´ski, Jarosław 
Abu-Rub, Haithem 
Toliyat, Hamid A. 
Keywords: Artificial neural networks (ANNs);Neural networks (Computer science);Pattern recognition systems;Digital signal processor (DSP);Signal processing - Digital techniques;Field programmable gate arrays (FPGAs);Electric motors, Induction;Nonlinear control;Nonlinear control theory;System identification;Observer system;Automatic control;Sensorless control
Issue Date: 2005
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. Feedforward 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.
Appears in Collections:Fulltext Publications

Files in This Item:
File Description SizeFormat
Artificial-neural-network-based sensorless nonlinear control of induction motors.pdf630.36 kBAdobe PDFView/Open
Show full item record

Page view(s)

checked on Jun 27, 2024


checked on Jun 27, 2024

Google ScholarTM


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.