Data-driven surrogate models for LTI systems via saddle-point dynamics. Issue 2 (2020)

• Record Type: Journal Article
• Authors: Martin, Tim; Koch, Anne; Allgöwer, Frank
• Is Part Of: IFAC-PapersOnLine. Volume 53:Issue 2(2020)
• Page Start: 953
• Page End: 958
• Publication Date: 2020
• Subjects: iterative methods -- reduced-order models -- input-output methods -- learning algorithms -- gradient methods; Automatic control -- Periodicals; 629.805
• DOI: 10.1016/j.ifacol.2020.12.1261
• Languages: English
• ISSNs: 2405-8963

Abstract

Abstract: For the analysis, simulation, and controller design of large-scale systems, a surrogate model with small complexity is mostly required. A standard approach to determine such a model is given by modelling the system and applying model-order-reduction techniques. Contrary, we propose a data-driven approach, where the surrogate model of the input-output behaviour of an LTI system is determined from data without modelling the system beforehand. Moreover, we provide a guaranteed bound on the maximal error between the system and the surrogate model in case of noise-free measurements. We analyse the stability and convergence of the presented schemes and apply them on a benchmark system from the model-order-reduction literature.