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

Abstract

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.