NDLI: Application of Spatial Iterative Learning Control for Direct Torque Control of Switched Reluctance Motor Drive

Content Provider	IEEE Xplore Digital Library
Author	Sahoo, S.K. Panda, S.K. Xu, J.X.
Copyright Year	2007
Description	Author affiliation: Dept. of Electr. & Comput. Eng., Nat. Univ. of Singapore, Singapore (Sahoo, S.K.; Panda, S.K.; Xu, J.X.)
Abstract	In this paper, a novel direct torque controller for switched reluctance motor (SRM) is proposed using spatial iterative learning control (ILC). SRM magnetization characteristics are highly non-linear, and torque is a complex and coupled function of phase current and rotor position. Direct torque control (DTC) scheme avoids the complexity of torque-to- current conversion as required in indirect torque control scheme. Traditional DTC scheme uses a hysteresis controller and leads to large amount of torque ripples when implemented using a digital controller. Advanced non-linear control methods can be used to improve the performance of DTC in SRM. However, such methods are often too complex for real-time implementation or require an accurate model of SRM magnetization characteristics. As shown here, ILC only uses a linearized magnetization characteristics and a simple learning law to obtain the desired control signal. An ILC based DTC scheme for SRM torque control for constant motor torque, has been developed and experimentally verified on a 1-hp, 4-phase SRM. Experimental results show the effectiveness of the proposed scheme in terms of average torque control and ripple minimization.
Starting Page	1
Ending Page	7
File Size	785911
Page Count	7
File Format	PDF
ISBN	142441296X
ISSN	19325517
DOI	10.1109/PES.2007.385538
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2007-06-24
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Torque control Reluctance motors Reluctance machines Hysteresis motors Magnetization Table lookup Artificial neural networks Digital control Voltage control Sampling methods iterative learning control switched reluctance motor direct torque control torque ripple minimization
Content Type	Text
Resource Type	Article

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