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A Behavioral Approach to Data-Driven Control With Noisy Input-Output Data
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DOI：10.1109/TAC.2023.3275014.png)
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Abstract
En 中文
This article deals with data-driven stability analysis and feedback stabilization of linear input-output systems in autoregressive (AR) form. We assume that noisy input-output data on a finite time-interval have been obtained from some unknown AR system. Data-based tests are then developed to analyze whether the unknown system is stable, or to verify whether a stabilizing dynamic feedback controller exists. If so, stabilizing controllers are computed using the data. In order to do this, we employ the behavioral approach to systems and control, meaning a departure from existing methods in data driven control. Our results heavily rely on a characterization of asymptotic stability of systems in AR form using the notion of quadratic difference form as a natural framework for Lyapunov functions of autonomous AR systems. We introduce the concepts of informative data for quadratic stability and quadratic stabilization in the context of input-output AR systems and establish necessary and sufficient conditions for these properties to hold. In addition, this article will build on results on quadratic matrix inequalities and a matrix version of Yakubovich's S-lemma.
Keywords:
Linear matrix inequalities
Noise measurement
Behavioral sciences
Mathematical models
Adaptive control
Lyapunov methods
Asymptotic stability
Behavioral approach
data-driven control
quadratic matrix inequalities (QMIs)
robust control
S-procedure
Journal
IF:
7
Papers:
1.3W
Citations:
6.7W
