Data Informativity: A New Perspective on Data-Driven Analysis and Control
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 als...
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| Veröffentlicht in: | IEEE transactions on automatic control 2020-11, Vol.65 (11), p.4753-4768 |
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| container_end_page | 4768 |
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| container_title | IEEE transactions on automatic control |
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| creator | van Waarde, Henk J. Eising, Jaap Trentelman, Harry L. Camlibel, M. Kanat |
| description | 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. |
| doi_str_mv | 10.1109/TAC.2020.2966717 |
| format | Article |
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Kanat</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Data Informativity: A New Perspective on Data-Driven Analysis and Control</atitle><jtitle>IEEE transactions on automatic control</jtitle><stitle>TAC</stitle><date>2020-11-01</date><risdate>2020</risdate><volume>65</volume><issue>11</issue><spage>4753</spage><epage>4768</epage><pages>4753-4768</pages><issn>0018-9286</issn><eissn>1558-2523</eissn><coden>IETAA9</coden><abstract>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. 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| source | IEEE Electronic Library (IEL) |
| subjects | Control design Control systems Control theory Data analysis Data models Data-driven control Excitation linear systems optimal control Predictive models Stability analysis State feedback System identification Systems theory Trajectory Tuning Uniqueness |
| title | Data Informativity: A New Perspective on Data-Driven Analysis and Control |
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