模型预测控制
词典序
控制理论(社会学)
类型(生物学)
计算机科学
相(物质)
控制(管理)
数学
人工智能
物理
生态学
量子力学
生物
组合数学
作者
Shengwei Chen,Yong Yang,Rong Chen,Jiefeng Hu,Huiqing Wen,Yiwang Wang,Weimin Wu,Gang Fang,José Rodríguez
标识
DOI:10.1109/tpel.2024.3490654
摘要
Due to the existence of the neutral point (NP) voltage, controlling the three-level inverters has essentially become a multiobjective optimization problem (MOOP) that needs to provide stable output voltages for the load and maintain the NP voltage simultaneously. Traditionally, this MOOP is converted into a single-objective optimization problem by weighting factors. However, since the physical dimensions of the two control objectives are usually different, it is challenging to choose proper weighting factors to obtain a satisfactory performance according to a specific theory. To address this issue, a tolerant sequential model predictive control (TSMPC) utilizing a lexicographic optimization method is proposed in this article. This method establishes two distinct layers for the output voltage and NP voltage, arranging them in sequence according to the importance of control objectives to evaluate all voltage vectors. By using an explainable tolerance value rather than conventional weighting factors, the proposed TSMPC algorithm presents superior performance over traditional MPC approaches. Finally, the feasibility and effectiveness of the proposed TSMPC algorithm have been verified through relevant experiments and the stability of this algorithm has also been analyzed.
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