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Data-driven surrogate models for LTI systems via saddle-point dynamics⁎This work was funded by Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) under Germany's Excellence Strategy - EXC 2075 - 390740016. The authors thank the International Max Planck Research School for Intelligent Systems (IMPRS-IS) for supporting Anne Koch. Issue 2 (2020)
Record Type:
Journal Article
Title:
Data-driven surrogate models for LTI systems via saddle-point dynamics⁎This work was funded by Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) under Germany's Excellence Strategy - EXC 2075 - 390740016. The authors thank the International Max Planck Research School for Intelligent Systems (IMPRS-IS) for supporting Anne Koch. Issue 2 (2020)
Main Title:
Data-driven surrogate models for LTI systems via saddle-point dynamics⁎This work was funded by Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) under Germany's Excellence Strategy - EXC 2075 - 390740016. The authors thank the International Max Planck Research School for Intelligent Systems (IMPRS-IS) for supporting Anne Koch.
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.