【目的】为解决永磁同步电机控制模型参数失配时，模型预测转矩控制(MPTC)算法存在鲁棒性较差的问题，提出了一种基于无权重比例积分微分(PID)型代价函数的MPTC算法。【方法】PID型代价函数通过构建积分误差价值函数消除静态误差，构建微分误差价值函数抑制转矩和磁链误差的振荡变化。由于转矩与磁链量纲不同，PID型代价函数仍然存在权重系数。为解决此问题，又提出一种无权重的双代价函数并行策略，将多目标代价函数转换为单目标代价函数求电压矢量集合，采用电流型代价函数作为评价最优电压矢量的最终依据，从而消除权重系数。最后，通过仿真将文本所提无权重PID型代价函数的MPTC与传统代价函数MPTC进行对比分析。【结果】仿真结果表明，采用无权重PID型代价函数的MPTC能够抑制转矩脉动和磁链脉动，降低MPTC对模型参数的依赖性，解决因存在权重系数而难以适应不同工况条件的问题，同时保留MPTC动态响应快的优点。【结论】本文所提代价函数在永磁同步电机MPTC上具有良好的可行性。

[Objective] To solve the problem of poor robustness in the model predictive torque control (MPTC) algorithm caused by parameter mismatch in the control model of permanent magnet synchronous motors, an MPTC algorithm based on an unweighted factor proportional integral derivative (PID) cost function was proposed. [Methods] The PID cost function eliminated static error by constructing an integral error value function and suppressed the oscillation of torque and flux linkage errors by constructing a differential error value function. However, since torque and flux linkage had different dimensions, the PID cost function still included weight coefficients. To solve this problem, an unweighted dual-cost function parallel strategy was proposed, which transformed the multi-objective cost function into a single-objective cost function to obtain a set of voltage vectors. The current-type cost function was used as the final criterion to evaluate the optimal voltage vector, thus eliminating the need for weight coefficient. Finally, the proposed unweighted factor PID cost function MPTC was compared with the traditional cost function MPTC through simulations. [Results] The results show that MPTC with unweighted factor PID cost function can suppress torque and flux ripple, reduce the dependence of MPTC on model parameters, and solve the difficulty in adapting to different operating conditions because of the existence of weight coefficient, while maintaining the advantage of MPTC’s fast dynamic response. [Conclusion] The proposed cost function demonstrates good feasibility in MPTC for permanent magnet synchronous motors.

安徽省教育厅基金重大项目(2022AH040110);国家自然科学基金青年项目(51905003)