Fengxiang Wang received a Ph.D. degree in Electrical Engineering at the Institute for Electrical Drive Systems and Power Electronics, Technische Universitaet Muenchen, Munich, Germany. Currently, He is selected as a national youth talent program, and working as a full professor, doctoral supervisor, and director of Quanzhou Institute of Equipment Manufacturing, Haixi Institutes, Chinese Academy of Sciences.

He serves as an IET Fellow, IEEE Senior Member, Associate Editor for IEEE Transactions on Industrial Electronics and IEEE Transactions on Energy Conversion. As General Chair, he organized the IEEE 5th International Symposium on Predictive Control of Electrical Drives and Power Electronics (PRECEDE).

His research interests include predictive control and sensorless control for electrical drives and power electronics.

Model Predictive Control (MPC) is an advanced control strategy for electrical drives, that utilizes a plant model to predict future operating states within a finite horizon, ensuring the stability and reliability of the system. By discretizing motor dynamics and solving an optimization problem at each sampling period, MPC effectively manages nonlinear terms, constraints, and multiple objectives, and has rolling optimization capability and excellent dynamic performance when dealing with complex motor operations. This report introduces the implementation of predictive torque control, and explores research topics further satisfying the requirement for high-quality electrical drives, such as weighting factor selection and elimination, robustness enhancement, and model-free predictive control.