针对Vienna整流器采用传统有限集模型预测控制（FCS-MPC）时存在的权重系数整定困难,以及模型参数失配引起的系统鲁棒性较差的问题,提出虑及参数失配的多目标快速排序模型预测控制策略。首先,对不同权重项排序结果进行求和,将最小排序和对应的开关状态应用于下一控制周期,提出多目标快速排序模型预测控制（MOFR-MPC）,有效消除传统FCS-MPC中的权重系数;其次,分析电感参数失配对控制器的影响,基于电流预测值与电流实际值构建电感观测器,提出虑及参数失配的模型预测控制（CPM-MPC）,改善模型参数失配时的并网电流质量;最后,仿真和实验结果表明,相较于传统FCS-MPC,CPM-MPC不仅无需设计权重系数,且在模型参数失配时具有更优的稳态和动态性能。
Abstract
In order to solve the problems of weight factors tuning,and poor system robustness caused by mismatching of model parameters in Vienna rectifier using traditional FCS-MPC, a multi-objective fast sorting model predictive control strategy was proposed,Firstly,the sorting results of different weight items are summed,and the minimum sorting and corresponding switching state are applied to the next control period. A multi-objective fast rank model predictive control （MOFR-MPC） is proposed,which effectively eliminate the weight factors in traditional FCS-MPC. Secondly,the effect of the inductance mismatch on the controller is analyzed,and an inductance observer is constructed based on the predicted and actual current values,and a model predictive control （CPM-MPC） taking into account parameter mismatch is proposed to improve the grid-connected current quality when the model parameters are mismatched. Finally,the simulation and experimental results show that compared with traditional FCS-MPC,CPM-MPC not only does not need to design weight factors,but also has better steady-state and dynamic performance when the model parameters are mismatched.
关键词
多目标优化 /
模型预测控制 /
鲁棒性 /
Vienna整流器 /
参数失配
Key words
multi-objective optimization /
model predictive control /
robustness /
Vienna rectifier /
parameter mismatch
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基金
电力传输与功率变换控制教育部重点实验室开放课题（2023AC01）
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