Improved Model Predictive Current Control for PMSM Motor With Consensus Algorithm: VERSION OF RECORD ONLINE: 17/09/2025 - Trung Tin Lu; Nguyen Dang Khoa Tran; Minh Duc Pham (Journal of Technical Education Science, 2025)
The use of PMSM drives is becoming more prominent in industrial applications due to their benefits of high torque and power density, outstanding efficiency, and excellent reliability. Because of these benefits, various speed control methods for PMSMs have been researched and developed, with each method having its own advantages and limitations. Among these methods, Model predictive control (MPC) is a highly efficient control strategy for permanent magnet synchronous motors (PMSMs) that can manage constrained multi-objective optimization while delivering excellent dynamic performance. However, the accuracy of traditional predictive current control models relies heavily on motor parameters such as inductance and resistance. Additionally, the values of factors in the cost function are often inconsistent, as they are typically determined based on experience. This study introduces a solution to overcome the mentioned drawbacks by integrating MPC with a consensus algorithm and an extended Kalman filter. The consensus algorithm updates the weight values in the cost function for optimal performance, while the extended Kalman filter estimates the motor inductance and resistance, ensuring accurate control for MPC. This improved combined method makes motor speed control more adaptive to changes in resistance and inductance during operation. It ensures a system response with minimal overshoot, reduced steady-state error, and quicker settling time. The simulation results of the scale-down PMSM system are obtained from the Matlab simulation platform.