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- W2897597924 abstract "Abstract 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." @default.
- W2897597924 created "2018-10-26" @default.
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- W2897597924 date "2018-01-01" @default.
- W2897597924 modified "2023-09-23" @default.
- W2897597924 title "A comparison between structured low-rank approximation and correlation approach for data-driven output tracking" @default.
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- W2897597924 doi "https://doi.org/10.1016/j.ifacol.2018.09.052" @default.
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