NDLI: Implementation of speed sensorless induction motor drives by fast learning neural network using recursive least squares approach

Content Provider	IEEE Xplore Digital Library
Author	Yoon-Sang Kook Yoon-Ho Kim Byung-Song Lee
Copyright Year	1998
Description	Author affiliation: Dept. of Electr. Eng., Chungang Univ., Seoul, South Korea (Yoon-Sang Kook; Yoon-Ho Kim; Byung-Song Lee)
Abstract	This paper presents a newly developed speed sensorless drive using RLS (recursive least squares) based on neural network training algorithm. The proposed algorithm based on the RLS has just the time-varying learning rate, while the well-known backpropagation (or generalized delta rule) algorithm based on gradient descent has a constant learning rate. The number of iterations required by the new algorithm to converge is less than that of the backpropagation algorithm. The RLS based on NN is used to adjust the motor speed so that the neural model output follows the desired trajectory. This mechanism forces the estimated speed to follow precisely the actual motor speed. In this paper, a flux estimation strategy using filter concept is discussed. The theoretical analysis and experimental results to verify the effectiveness of the proposed analysis and the proposed control strategy are described.
Starting Page	745
Ending Page	750
File Size	282922
Page Count	6
File Format	PDF
ISBN	0780348796
DOI	10.1109/PEDES.1998.1330694
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	1998-12-01
Publisher Place	Australia
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Induction motor drives Neural networks Least squares methods Voltage Rotors Low pass filters Stators State estimation Iterative algorithms Resonance light scattering
Content Type	Text
Resource Type	Article

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