感应电机无差拍模型预测转矩控制研究

传统模型预测控制（MPC）在三相两电平逆变器驱动感应电机系统中需对所有电压矢量进行滚动优化预测，计算成本高，实施难度大。为此，提出一种无差拍（DB）MPC，将滚动优化预测电压矢量从8个减少到3个。通过求解DB电压矢量和对DB扇区的划分，根据空间电压矢量位置确定扇区，只将扇区内矢量进行单步滚动优化预测，输出相对应的最优开关状态。仿真结果表明:DB预测转矩控制在减轻系统计算负担情况下，达到与传统MPC几乎相同的稳态跟踪性能。

Deadbeat Model Predictive Torque Control for Induction Motor Drive

Conventional model predictive control (MPC) requires receding horizon optimization and prediction of all voltage vectors in a three phase two level inverter driven induction motor system, and has disadvantages of high computational cost and difficulty in implementation. To solve these problems, a deadbeat MPC is proposed, which reduces the receding horizon optimization prediction voltage vector from 8 to 3. The deadbeat voltage vector is calculated and the deadbeat sector is divided. The sector is determined based on the space voltage vector position. A single step receding horizon optimization prediction of the vectors in the sector is performed, and the corresponding optimal switch state is output. The simulation results show that the deadbeat MPC can achieve almost the same steady state tracking performance as the traditional MPC while reducing the computational burden of the system.