Model Predictive Torque and Force Control for Switched Reluctance Machines Based on Online Optimal Sharing Function
Lefei Ge, Zizhen Fan, Nan Du, Jiale Huang, Dianxun Xiao, Shoujun Song
Abstract
Although the torque and radial force ripples are two important causes of unwelcomed vibration in switched reluctance machines, the suppression of these ripples is usually contradictory. To address this issue, we propose a model predictive torque and force control (MPT&FC) method. First, the torque and force sharing functions are constructed based on the flux-linkage curve, following which the sharing functions are optimized online by tuning the turn- <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">on</small> angle to minimize the torque and force ripple. Finally, the MPT&FC method is applied to complete the sharing function tracking control. For balanced control of the torque and radial force, we optimize the candidate-voltage-vector table. Experiments were done on a three-phase 12/8 switched reluctance machine to verify that the proposed method suppresses vibrations.