An extended-horizon model predictive torque control with computationally efficient implementation for PMSM drives

In this paper, an extended-horizon predictive torque control (PTC) method is proposed with computationally efficient implementation. Predictive control with extended horizon brings important advantages compared to single-horizon methods, such as better steady-state performance, reduced current THD, and lower ripples in torque and stator flux. However, the computational burden is a serious obstacle because the required calculations rise exponentially with the extension of the prediction horizon. This is due to a high number of candidate voltage vectors and also predicting the values of several machine variables for these vectors. Different approaches are employed in this work to make the control method computationally tractable. A voltage vector reduction technique is utilised that significantly decreases the total number of enumerated vectors. Moreover, the stator current prediction is eliminated in the proposed method, based on the inherent features of the PMSM. The proposed method is experimentally implemented and its good performance and advantages are verified.

Automated summary

In this paper, an extended-horizon predictive torque control method is proposed with computationally tractable implementation. The method reduces computational burden and optimizes control strategy, resulting in efficient computation and superior performance.