NDLI: Modeling of a 6/4 Switched Reluctance Motor Using Adaptive Neural Fuzzy Inference System

Content Provider	IEEE Xplore Digital Library
Author	Wen Ding Deliang Liang
Copyright Year	1965
Abstract	The magnetic saturation and strong nonlinearity of switched reluctance machines (SRMs) makes it very difficult to derive a comprehensive mathematical model for the behavior of the machine. We propose a new method of modeling SRMs based on an adaptive neural fuzzy inference system (ANFIS). First, we use an indirect method to measure the static flux linkage and then use the co-energy method (via the principle of virtual displacement) to calculate the torque characteristics from data on flux linkage versus current and rotor position. A hybrid learning algorithm, which combines the back propagation algorithm and the linear least-squares estimation algorithm, identifies the parameters of the ANFIS. After training, the ANFIS flux linkage model and ANFIS torque model are in excellent agreement with experimental flux linkage measurements and the calculated torque data. Finally, we use an ANFIS current model and an ANFIS torque model to study SRM dynamic performance. The accuracy of the model was evaluated by comparison to laboratory measurements of the machine's current-speed and torque-speed characteristics. The model is quite accurate.
Sponsorship	IEEE Magnetics Society
Starting Page	1796
Ending Page	1804
Page Count	9
File Size	1481319
File Format	PDF
ISSN	00189464
Volume Number	44
Issue Number	7
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2008-07-01
Publisher Place	U.S.A.
Access Restriction	One Nation One Subscription (ONOS)
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Reluctance motors Fuzzy systems Reluctance machines Couplings Torque measurement Current measurement Magnetic flux Saturation magnetization Magnetic switching Mathematical model switched reluctance machine Adaptive neural fuzzy inference system back propagation least-squares
Content Type	Text
Resource Type	Article
Subject	Electronic, Optical and Magnetic Materials Electrical and Electronic Engineering

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
7	National Digital Library of India Club (NDLI Club)	Queries related to NDLI Club formation, support, user awareness program, seminar/symposium, collaboration, social media, promotion, and outreach	clubsupport@ndl.gov.in
8	Digital Preservation Centre (DPC)	Assistance with digitizing and archiving copyright-free printed books	dpc@ndl.gov.in
9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

Modelling of switched reluctance motor based on variable structure fuzzy-neural networks

Back Propagation Neural Network Applied to Modeling of Switched Reluctance Motor

Performance estimations of switched reluctance motor considered with magnetic saturation

Flux Estimation In Switched Reluctance Motors Using a Two Dimensional Least Square Method Approximation

Switched reluctance motor analytical models, comparative analysis

Improved modeling of switched reluctance motor including mutual and saturation effects

Neural network based torque ripple minimisation in a switched reluctance motor

Experimental Investigations on Computer-Based Methods for Determination of Static Electromagnetic Characteristics of Switched Reluctance Motors

Sensorless position estimation of switched reluctance motors using artificial neural networks

Modeling of a 6/4 Switched Reluctance Motor Using Adaptive Neural Fuzzy Inference System

Similar Documents

Modelling of switched reluctance motor based on variable structure fuzzy-neural networks

Back Propagation Neural Network Applied to Modeling of Switched Reluctance Motor

Performance estimations of switched reluctance motor considered with magnetic saturation

Flux Estimation In Switched Reluctance Motors Using a Two Dimensional Least Square Method Approximation

Switched reluctance motor analytical models, comparative analysis

Improved modeling of switched reluctance motor including mutual and saturation effects

Neural network based torque ripple minimisation in a switched reluctance motor

Experimental Investigations on Computer-Based Methods for Determination of Static Electromagnetic Characteristics of Switched Reluctance Motors

Sensorless position estimation of switched reluctance motors using artificial neural networks

Modeling of a 6/4 Switched Reluctance Motor Using Adaptive Neural Fuzzy Inference System