NDLI: A correction PI-like fuzzy logic controller for SRM drives system

Content Provider	IEEE Xplore Digital Library
Author	Shun-Chung Wang Chia-Cheng Lee Yi-Hua Liu Yuan-Lin Chen
Copyright Year	2010
Description	Author affiliation: Dept. of Mech. Eng., MCUT, Taipei County, Taiwan (Yuan-Lin Chen) \|\| Dept. of Electric Eng., NTUST, Taipei, Taiwan (Yi-Hua Liu) \|\| Dept. of Electric Eng., Lunghwa University of Science and Technology, Taoyuan, Taiwan (Shun-Chung Wang; Chia-Cheng Lee)
Abstract	Based on the rebuilt of control rule base, a modified type PI-like fuzzy logic controller (FLC) with output scaling factor (SF) self-tuning mechanism is proposed in this paper for application in the switched reluctance motor (SRM) drive system. This paper intends to simplify the construction hierarchy and computational complexity of the controller by trimming the number of fuzzy sets in the membership functions (MFs) but still without losing the system performance and stability. For the proposed controller, the output scaling factor can be adjusted continuously by a gain updating factor, whose value is derived from the fuzzy logic reasoning with the error and change of error of the plant as the input variables. Design considerations about the membership function, rule base, and gain tuning strategy are described in detail. Experimental results and comparisons carried out on a four phase 8/6 pole SRM based on the DS1104 control desk are given to show the feasibility and effectiveness of the devised methods.
Starting Page	1346
Ending Page	1351
File Size	716884
Page Count	6
File Format	PDF
ISBN	9781424450459
e-ISBN	9781424450466
DOI	10.1109/ICIEA.2010.5514909
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2010-06-15
Publisher Place	Taiwan
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Fuzzy logic Control systems Reluctance motors Reluctance machines Error correction Computational complexity Fuzzy sets System performance Stability Input variables switched reluctance motor fuzzy logic controller scaling factor
Content Type	Text
Resource Type	Article

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