Willems et al. ’s fundamental lemma asserts that all trajectories of a linear system can be obtained from a single given one, assuming that a persistency of excitation and a controllability condition hold. This result has profound implications for system identification and data-driven control, and has seen a revival over the last few years. The purpose of this letter is to extend Willems’ lemma to the situation where multiple (possibly short) system trajectories are given instead of a single long one. To this end, we introduce a notion of collective persistency of excitation. We will show that all trajectories of a linear system can be obtained from a given finite number of trajectories, as long as these are collectively persistently exciting. We will demonstrate that this result enables the identification of linear systems from data sets with missing samples. Additionally, we show that the result is of practical significance in data-driven control of unstable systems.

Willems’ Fundamental Lemma for State-Space Systems and Its Extension to Multiple Datasets / van Waarde, Henk J.; De Persis, Claudio; Camlibel, M. Kanat; Tesi, Pietro. - In: IEEE CONTROL SYSTEMS LETTERS. - ISSN 2475-1456. - STAMPA. - 4:(2020), pp. 602-607. [10.1109/LCSYS.2020.2986991]

Willems’ Fundamental Lemma for State-Space Systems and Its Extension to Multiple Datasets

Tesi, Pietro
2020

Abstract

Willems et al. ’s fundamental lemma asserts that all trajectories of a linear system can be obtained from a single given one, assuming that a persistency of excitation and a controllability condition hold. This result has profound implications for system identification and data-driven control, and has seen a revival over the last few years. The purpose of this letter is to extend Willems’ lemma to the situation where multiple (possibly short) system trajectories are given instead of a single long one. To this end, we introduce a notion of collective persistency of excitation. We will show that all trajectories of a linear system can be obtained from a given finite number of trajectories, as long as these are collectively persistently exciting. We will demonstrate that this result enables the identification of linear systems from data sets with missing samples. Additionally, we show that the result is of practical significance in data-driven control of unstable systems.
2020
4
602
607
van Waarde, Henk J.; De Persis, Claudio; Camlibel, M. Kanat; Tesi, Pietro
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Utilizza questo identificatore per citare o creare un link a questa risorsa: https://hdl.handle.net/2158/1191837
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