By Topic

Modelling and control of bearingless switched reluctance motor based on artificial neural network

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

3 Author(s)
Jianbo Sun ; Sch. of Electr. & Electron. Eng., Huazhong Univ. of Sci. & Technol., Hubei, China ; Qionghua Zhan ; Liming Liu

Bearingless switched reluctance motors have combined advantages of switched reluctance motors (SRM) and magnetic bearings. An accurate model of radial force and torque is the basis of precise and fast rotor position control in bearingless SRM. This paper presents a new non-linear modeling method of bearingless SRM using finite element method (FEM) and artificial neural network (ANN). The new method is superior to the previous ones because of its consideration of the non-linearity of magnetic field in bearingless SRM. Furthermore, a novel instantaneous radial force control scheme direct radial force control (DRFC), is proposed in this paper. The new model and DRFC are proved to be more effective than the original control scheme by the simulation results.

Published in:

Industrial Electronics Society, 2005. IECON 2005. 31st Annual Conference of IEEE

Date of Conference:

6-10 Nov. 2005