Model Reference Sliding Mode Control for RPMTM with Neural Network Load Torque Observer

Unpredictable plant parameter variations, external load disturbances and nonlinear dynamics which exist in ring permanent magnet torque motors (RPMTM) seriously deteriorate the drive performance of system at low speeds. A model reference sliding mode control scheme which features good robustness aga...

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Bibliographic Details
Published in2012 Asia-Pacific Power and Energy Engineering Conference pp. 1 - 4
Main Authors Bing Peng, Chengyuan Wang, Jiakuan Xia, Ting Dong
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.03.2012
Subjects
Load modeling
Mathematical model
Neural networks
Observers
Sliding mode control
Synchronous motors
Torque
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Summary:Unpredictable plant parameter variations, external load disturbances and nonlinear dynamics which exist in ring permanent magnet torque motors (RPMTM) seriously deteriorate the drive performance of system at low speeds. A model reference sliding mode control scheme which features good robustness against parameter variations is proposed in this paper firstly. Then a neural network load torque observer is presented to observe and compensate external load disturbances. The analysis, design and simulation of the proposed model reference sliding mode control scheme controller and neural network load torque observer are described. Simulation results show that good control performance, both in the command-tracking and the load-regulating characteristics of the rotor position, is achieved.
ISBN:9781457705458
1457705451
ISSN:2157-4839
DOI:10.1109/APPEEC.2012.6307495