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Data Informativity: A New Perspective on Data-Driven Analysis and Control
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DOI：10.1109/TAC.2020.2966717.png)
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Abstract
En 中文
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.
Keywords:
Data models
Control design
Trajectory
State feedback
Predictive models
Tuning
Stability analysis
Data-driven control
system identification
linear systems
optimal control
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