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A comparison between structured low-rank approximation and correlation approach for data-driven output tracking

机译:数据驱动的输出跟踪的结构化低秩近似与相关方法的比较

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Data-driven control is an alternative to the classical model-based control paradigm. The main idea is that a model of the plant is not explicitly identified prior to designing the control signal. Two recently proposed methods for data-driven control—a method based on correlation analysis and a method based on structured matrix low-rank approximation and completion—solve identical control problems. The aim of this paper is to compare the methods, both theoretically and via a numerical case study. The main conclusion of the comparison is that there is no universally best method: the two approaches have complementary advantages and disadvantages. Future work will aim to combine the two methods into a more effective unified approach for data-driven output tracking.
机译:数据驱动控制是经典的基于模型的控制范例的替代方法。主要思想是在设计控制信号之前未明确识别设备的模型。最近提出的两种数据驱动控制方法-一种基于相关性分析的方法和一种基于结构化矩阵低秩逼近和完成的方法-解决了相同的控制问题。本文的目的是在理论上和通过数值案例研究来比较这些方法。比较的主要结论是,没有通用的最佳方法:这两种方法具有互补的优点和缺点。未来的工作旨在将这两种方法结合为一种更有效的统一方法,以进行数据驱动的输出跟踪。

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