NDLI: Identification of a hybrid maglev system with electromagnets and permanent magnets

Content Provider	IEEE Xplore Digital Library
Author	Xiao Zhang Hu Cheng Yungang Li
Copyright Year	2011
Description	Author affiliation: Research center of Maglev, National University of Defense Technology, Changsha, Hunan, China (Xiao Zhang; Hu Cheng; Yungang Li)
Abstract	The mathematical model of a maglev system with a single hybrid magnet is identified from the experimental data in this paper. Firstly, the model structure of the hybrid maglev system is chosen based on Newton-Second Law and Kirchoff Equation. To simplify the identification procedure, current-loop control algorithm is introduced and the proposed model structure is decomposed into two parts, which are separately the nonlinear kinetics and the linear Electrics. Secondly, the information of the practical hybrid system is acquired by a closed-loop identification experiment, and the controller design to stabilize the system and the reference signal design to persistently excite the system are both addressed in detail. Then the proposed model structure is written as two ARX equations after introducing new states, and the parameters are identified by Least-Square-Method. Finally, the applicability of the identified model is evaluated by another set of experimental data, which illustrates that the obtained model is sufficiently accurate for the purpose of controller design.
Starting Page	454
Ending Page	457
File Size	230321
Page Count	4
File Format	PDF
ISBN	9781424480364
e-ISBN	9781424480395
DOI	10.1109/ICEICE.2011.5778359
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2011-04-15
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Least-square-method Maglev system Magnetic separation Medical services Electromagnets Hybrid magnet Permanent magnets System identification Mathematical model Suspensions Magnetic levitation
Content Type	Text
Resource Type	Article

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