A comparison between structured low-rank approximation and correlation approach for data-driven output tracking. Issue 15 (2018)

• Record Type: Journal Article
• Authors: Formentin, Simone; Markovsky, Ivan
• Is Part Of: IFAC-PapersOnLine. Volume 51:Issue 15(2018)
• Page Start: 1068
• Page End: 1073
• Publication Date: 2018
• Subjects: data-driven control -- output tracking -- virtual reference feedback tuning -- structured low-rank approximation -- matrix completion; Automatic control -- Periodicals; 629.805
• DOI: 10.1016/j.ifacol.2018.09.052
• Languages: English
• ISSNs: 2405-8963

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.