Model Predictive Current Control of Switched Reluctance Motors with Inductance Auto-Calibration


This paper investigates application of an unconstrained model predictive controller (MPC) known as a finite horizon linear quadratic regulator (LQR) for current control of a switched reluctance motor (SRM). The proposed LQR can cope with the measurement noise as well as uncertainties within the machine inductance profile. This paper utilizes MPC to generate the optimal duty cycles for drive of SRMs using pulse-width modulation (PWM) in oppose to delta-modulation. In this paper, first a practical MPC scheme for embedded implementation of the system is introduced. Afterward, Kalman filtering is used for state estimation while an adaptive controller is used to dynamically tune and update both MPC and Kalman models. Hence, the overall control structure is considered as a stochastic MPC with adaptive model calibration. Finally, simulation and experimental results are provided to demonstrate the effectiveness of the proposed method.


Electrical and Computer Engineering

Keywords and Phrases

Controllers; Counting Circuits; Delta Modulation; Electric Current Control; Electric Drives; Inductance; Kalman Filters; Model Predictive Control; Modulation; Predictive Control Systems; Pulse Width Modulation; Stochastic Models; Stochastic Systems; Uncertainty Analysis; Voltage Control; Adaptive; Model Predictive Controllers; Motor Drive; Predictive Control; Switched Reluctance Motor; Reluctance Motors; Current Control; Kalman Filter; Model Predictive Controller (MPC); Switched Reluctance Motor (SRM)

International Standard Serial Number (ISSN)


Document Type

Article - Journal

Document Version


File Type





© 2016 Institute of Electrical and Electronics Engineers (IEEE), All rights reserved.

Publication Date

01 Jun 2016