Title :
High-performance current control for switched reluctance motors based on on-line estimated parameters
Author :
Lin, Zhiyun ; Reay, D. ; Williams, Barry ; He, Xiangning
Author_Institution :
Control Tech., Emerson Ind. Autom., Newtown, UK
fDate :
1/1/2010 12:00:00 AM
Abstract :
To reduce torque ripple in a switched reluctance motor (SRM) by current profiling, a high-performance current controller is necessary. This study presents a high-performance current controller for SRM drives. A B-spline neural network is used to model the non-linearity of the SRM and estimate back electromotive force (EMF) and incremental inductance on-line in real time. The on-line modelling scheme does not require a priori knowledge of the machine´s electromagnetic characteristics. Based on the on-line estimated parameters, a current controller with adjustable PI gains and back-EMF decoupling is implemented. The performance of the current controller has been demonstrated in simulation and experimentally using a four-phase 8/6 550´W SRM drive system.
Keywords :
electric current control; electric potential; machine control; neural nets; parameter estimation; reluctance motor drives; B-spline neural network; back electromotive force; current controller; current profiling; on-line parameter estimation; power 550 W; switched reluctance motor drives; torque ripple;
Journal_Title :
Electric Power Applications, IET
DOI :
10.1049/iet-epa.2009.0016