Improved model predictive current control of permanent magnet synchronous machines with fuzzy based duty cycle control
Model predictive current control uses a model of the machine and an appropriate cost function to indirectly control electromagnetic torque and reactive power. However, due to sensitivity of model predictive control (MPC) to system parameters, need for high sampling frequency, and high torque and flux ripples, this method is not employed in a wide variety of commercial drives. Incorporating the concept of duty cycle and applying two voltage vectors during a sampling period can reduce the torque and stator current ripples of a model predicative current controlled synchronous machine. In this paper, duty cycles of the voltage vectors are determined effectively using a fuzzy logic modulator. In addition, a full order Luenberger observer is designed for accurate estimation of motor variables in presence of parameter uncertainties. Matlab and real-time simulation results confirm the effectiveness of the proposed MPC.
Publication Source (Journal or Book title)
ECCE 2016 - IEEE Energy Conversion Congress and Exposition, Proceedings
Bozorgi, A., Farasat, M., & Jafarishiadeh, S. (2016). Improved model predictive current control of permanent magnet synchronous machines with fuzzy based duty cycle control. ECCE 2016 - IEEE Energy Conversion Congress and Exposition, Proceedings https://doi.org/10.1109/ECCE.2016.7855191