Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/14978
Title: Type-V exponential regression for online sensorless position estimation of switched reluctance motor
Authors: Chang, YT
Cheng, KWE 
Ho, SL 
Keywords: Exponential regression
Position estimation
Sensorless
Startup
Switched reluctance motor (SRM)
Issue Date: 2015
Publisher: Institute of Electrical and Electronics Engineers Inc.
Source: IEEE/ASME transactions on mechatronics, 2015, v. 20, no. 3, p. 1351-1359 How to cite?
Journal: IEEE/ASME Transactions on Mechatronics 
Abstract: The idea of sensorless position sensing of switched reluctance motor (SRM) is attractive to researchers because of the increased reliability, robustness, and cost reduction compared to conventional drives. Sensorless drive is particularly useful in electric transportation applications where the environment is too hostile for physical position sensors, such as inside an electric car or bus. This paper presents a new method to estimate the motor positions during startup or at flying restart. Unlike most of the methods described in the literature, the algorithm, based only on the general magnetic characteristics of an SRM, can provide exact rotor positions without specific motor magnetic information. The calculation is simple and can be implemented easily and efficiently with a microcontroller by users in industry.
URI: http://hdl.handle.net/10397/14978
ISSN: 1083-4435
DOI: 10.1109/TMECH.2014.2343978
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