Data-driven surrogate models for LTI systems via saddle-point dynamics

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.