Beyond Persistent Excitation: Online Experiment Design for Data-Driven Modeling and Control

Beyond Persistent Excitation: Online Experiment Design for Data-Driven Modeling and Control

This letter presents a new experiment design method for data-driven modeling and control. The idea is to select inputs online (using past input/output data), leading to desirable rank properties of data Hankel matrices. In comparison to the classical persistency of excitation condition, this online...

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Veröffentlicht in:IEEE control systems letters 2022, Vol.6, p.319-324
1. Verfasser: van Waarde, Henk J.
Format: Artikel
Sprache:eng
Schlagworte:
Adaptive systems
Data-driven modeling
Design methodology
Identification
Linear systems
Noise measurement
Trajectory
Tuning
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container_title IEEE control systems letters
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creator van Waarde, Henk J.
description This letter presents a new experiment design method for data-driven modeling and control. The idea is to select inputs online (using past input/output data), leading to desirable rank properties of data Hankel matrices. In comparison to the classical persistency of excitation condition, this online approach requires less data samples and is even shown to be completely sample efficient.
doi_str_mv 10.1109/LCSYS.2021.3073860
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subjects Adaptive systems
Data-driven modeling
Design methodology
Identification
Linear systems
Noise measurement
Trajectory
Tuning
title Beyond Persistent Excitation: Online Experiment Design for Data-Driven Modeling and Control
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