Abstract

Aiming at the issues of large current ripple and significant torque pulsation in switched reluctance motor (SRM) model predictive current control (MPCC) under varying operating conditions, this paper innovatively proposes a novel SRM model predictive current control method integrating an Extended State Observer (ESO) and the Lehuy model. By constructing a nonlinear current prediction framework based on the Lehuy model, the data dependency on traditional Look-Up Table (LUT) methods is significantly reduced. Meanwhile, the real-time compensation of system disturbances is achieved by introducing the ESO, resolving parameter mismatch issues under dynamic operating conditions. Simulated and experimental results demonstrate that this method, implemented on a 12/8-pole SRM prototype, achieves a current ripple reduction of 41.5% and torque pulsation suppression of 32.7% compared to traditional LUT-MPCC. This research provides new insights into the robust control of SRMs in high-precision servo scenarios.

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Dr. Xiong Su

Professor Aide Xu

Dr. Shining Lin

301 Moved Permanently