基于龙伯格扰动观测器的PMSM模型预测控制

受工作条件及自身结构影响，永磁同步电机电感参数不是确定的，当采用离线电感参数实现模型预测控制算法时，容易产生参数失配问题，进而导致控制性能下降，为解决该问题，提出了一种基于龙伯格扰动观测器的模型预测电流控制策略。依靠包含扰动的永磁同步电机模型，构建了龙伯格扰动观测器，并根据期望极点配置原理设计了反馈系数，保证观测器的稳定性，创新性体现在利用龙伯格观测器实时估算得到的扰动值补偿由电感参数失配带来的预测精度损失，进而提高系统的控制性能。实验结果表明，提出的基于龙伯格扰动观测器补偿的模型预测控制算法具有较强的鲁棒性，在电感参数失配时，保证电流仍具有良好的稳态性能。

Luenberger Disturbance Observerbased Model Predictive Control for Permanent Magnet Synchronous Motor

Due to the ambient conditions and the intrinsic structure, the inductances of the permanent magnet synchronous machine (PMSM) are not certain in practice. Therefore, when using the offline inductance values to achieve a model predictive control (MPC) strategy, parameter mismatch problem is inclined to occur, leading to control performance degradation. In order to solve the problem, a Luenberger disturbance observer based MPC control strategy is proposed. Relying on the PMSM model containing the disturbance terms, a Luenberger disturbance observer is established. Meanwhile, the expected poles are used to design the feedback coefficients, ensuring the stability of the observer. The novelty reflects in that the disturbances observed by the Luenberger observer are used to compensate the accuracy loss caused by inductance mismatch, which contributes to improving the control performance. The experimental results indicate that the proposed MPC algorithms based on Luenberger observer are robust.When inductance mismatch problem occurs, the proposed MPC method still has good steady-state performance.