Data Informativity: A New Perspective on Data-Driven Analysis and Control

By Henk J. van Waarde, Jaap Eising, Harry L. Trentelman, M. Kanat Camlibel. IEEE Transactions on Automatic Control, 2019

569 citations recorded by the source corpus.

The use of persistently exciting data has recently been popularized in the context of data-driven analysis and control. Such data have been used to assess system-theoretic properties and to construct control laws, without using a system model. Persistency of excitation is a strong condition that also allows unique identification of the underlying dynamical system from the data within a given model class. In this article, we develop a new framework in order to work with data that are not necessarily persistently exciting. Within this framework, we investigate necessary and sufficient conditions on the informativity of data for several data-driven analysis and control problems. For certain analysis and design problems, our results reveal that persistency of excitation is not necessary. In fact, in these cases, data-driven analysis/control is possible while the combination of (unique) system identification and model-based control is not. For certain other control problems, our results justify the use of persistently exciting data, as data-driven control is possible only with data that are informative for system identification.

Subfields: Control and Systems Engineering, Statistical and Nonlinear Physics

View Paper: https://www.semanticscholar.org/paper/6f6958870664a58e98d93af94d80aa9d3409030c

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Citations in the corpus

Data-Driven Dissipativity Analysis: Application of the Matrix S-Lemma (2021, unknown): “This deﬁnition is inpired by [29], and we refer to that paper for a general treatment of data informativity and an application to data-driven controllability analysis, stabilization and optimal control.”
From Noisy Data to Feedback Controllers: Nonconservative Design via a Matrix S-Lemma (2020, methodology): “In fact, instead of working with data-based parameterizations of closed-loop systems [37]–[39], we will work with a representation of all open-loop systems explaining the data, akin to the framework of [3].”
Data-Driven Model Predictive Control With Stability and Robustness Guarantees (2019, methodology): “Further, the result is employed in [17] to design stateand output-feedback controllers and in [18] to verify dissipation inequalities from measured data, whereas [19] investigates data-driven control without requiring persistently exciting data.”