DocumentCode :
3604463
Title :
Inductance Surface Learning for Model Predictive Current Control of Switched Reluctance Motors
Author :
Xin Li ; Shamsi, Pourya
Author_Institution :
Missouri Univ. of Sci. & Technol., Rolla, MO, USA
Volume :
1
Issue :
3
fYear :
2015
Firstpage :
287
Lastpage :
297
Abstract :
In this paper, an inductance surface estimation and learning for utilization with a stochastic model predictive control (MPC) scheme for the current control of switched reluctance motors (SRMs) is introduced. This MPC is equipped with state estimators and is implemented as a recursive linear quadratic regulator for practical deployments in hybrid vehicle applications. Additionally, a learning mechanism is developed to dynamically adapt to the inductance profile of the machine and update the MPC and Kalman filter parameters. The introduced control scheme can cope with noise as well as uncertainties within the machine nonlinear inductance surface. The introduced system will benefit from a fixed switching frequency and will offer low current ripples by calculating the optimal duty cycles using the SRM model. Finally, simulations and experimental results are provided to evaluate the proposed method.
Keywords :
Kalman filters; electric current control; learning (artificial intelligence); machine control; power engineering computing; predictive control; reluctance motors; Kalman filter parameters; MPC scheme; SRM; current control; fixed switching frequency; hybrid vehicle applications; inductance surface estimation; inductance surface learning; learning mechanism; optimal duty cycles; practical deployments; recursive linear quadratic regulator; stochastic model predictive control scheme; switched reluctance motors; Adaptation models; Couplings; Delays; Estimation; Inductance; Reluctance motors; Current control; Kalman filter; LQR; MPC; SRM; current control; delay compensation; inductance estimation; inductance surface learning; least quadratic regulator (LQR); model predictive control (MPC); motor drive; predictive control; recursive least squares; switched reluctance; switched reluctance motors (SRMs);
fLanguage :
English
Journal_Title :
Transportation Electrification, IEEE Transactions on
Publisher :
ieee
ISSN :
2332-7782
Type :
jour
DOI :
10.1109/TTE.2015.2468178
Filename :
7192631
Link To Document :
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