Multistep Model Predictive Torque Control for Induction Motor via Imitation Learning

This article proposes a novel approach utilizing imitation learning to address the computational challenge of multistep model predictive torque control (MPTC). The long prediction horizon of multistep MPTC usually contributes to better performance for induction motor control. Nevertheless, the computational burden increases exponentially with the length of the prediction horizon, leading to considerable difficulties in its real time implementation. MPTC essentially solves an optimization problem, which is time-consuming for a long prediction horizon. To overcome the computational difficulty, we replace the expensive numerical solver with a cheap deep neural network (DNN) following the idea of imitation learning. The DNN's output approximates the optimal solution after training. The proposed method achieves comparable steady-state performance to the ideal multistep MPTC, with lower computational complexity, especially when the switching frequency is limited. Moreover, this strategy demonstrates notable control performance improvements in contrast to the conventional one-step MPTC. Overall, the proposed method has great potential for real-time multistep MPTC implementation in typical induction motor drives.