NDLI: Study on suspension rigidity control of electromagnetic suspension system based on NN-PID

Content Provider	IEEE Xplore Digital Library
Author	Xia Yang Xinye Li Yan Gao Bo Yang
Copyright Year	2008
Description	Author affiliation: Sch. of Electr. Eng., Shenyang Univ. of Technol., Shenyang (Xia Yang; Xinye Li; Yan Gao; Bo Yang)
Abstract	Permanent magnets have inherent magnetism. Power consumption of suspension system can be reduced and large air-gap can be reached when permanent magnets are applied to maglev system. But permanent magnets cannot be well controlled and the load of the electromagnetic suspension system (hereafter, EMSS) is not constant, so electromagnets should be added. Therefore a hybrid structure made of permanent magnets and electromagnets is adopted. In addition, EMSS is a typical non-linear and hysteresis system, so itpsilas difficult to obtain its precise mathematic model and itpsilas also difficult to cater to its requirement of rapidity, stability and robustness by applying general PID or other control algorithms separately. Aimed at the characteristics of EMSS, the paper discusses the possibility of applying artificial neural network PID controller (hereafter, NN-PID) to maglev suspension system based on the induced mathematic model, and a composite control model, NN-PID, is introduced by applying P part to improve the systempsilas rapidity, applying NN part to improve the systempsilas dynamic characteristics and applying PI part which can erase the systempsilas static error to improve the systempsilas steady characteristics. The simulation proves that this composite controller can make the system have an excellent stability and control accuracy.
Starting Page	6118
Ending Page	6122
File Size	122584
Page Count	5
File Format	PDF
ISBN	9781424421138
DOI	10.1109/WCICA.2008.4592873
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2008-06-25
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Suspensions Artificial neural networks Permanent magnets Mathematical model Magnetic levitation Electromagnets Magnetic circuits NN-PID electromagnetic suspension Hybrid suspension
Content Type	Text
Resource Type	Article

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