Optimal Torque Sharing Function Control for Switched Reluctance Motors Based on Active Disturbance Rejection Controller

Abstract

In this article, an optimal torque sharing function (TSF) control method incorporated with improved linear active disturbance rejection control (LADRC) and modified coyote optimization algorithm (MCOA) is introduced and applied in switched reluctance motors (SRMs). First, the piecewise TSF is employed to reduce the torque ripple, which exhibits little effects on the rate of current change and the current peak value. The improved linear extended state observer with the variable gain in speed controller using LADRC is designed to guarantee better antidisturbance ability and torque ripple reduction simultaneously. Then, the MCOA is introduced to automatically tune the parameters including the turn- <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">on</small> angle, conduction angle, and coefficients for better comprehensive performances. Finally, comparisons among the conventional TSF with LADRC, the piecewise TSF with LADRC, and the proposed TSF with improved LADRC are conducted both in the simulations and experiments. The results indicate that the proposed method behaves best in aspects of torque ripple suppression and anti-interference ability among the three methods.