Data informativity gives exact conditions for when measured data, rich or not, suffice for certifying controllability, designing stabilizing or deadbeat feedback, or solving LQR from data.
Formulas for Data-driven Control: Stabilization, Optimality and Robustness
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abstract
In a paper by Willems and coauthors it was shown that persistently exciting data can be used to represent the input-output behavior of a linear system. Based on this fundamental result, we derive a parametrization of linear feedback systems that paves the way to solve important control problems using data-dependent Linear Matrix Inequalities only. The result is remarkable in that no explicit system's matrices identification is required. The examples of control problems we solve include the state and output feedback stabilization, and the linear quadratic regulation problem. We also discuss robustness to noise-corrupted measurements and show how the approach can be used to stabilize unstable equilibria of nonlinear systems.
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2019 1verdicts
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Data informativity: a new perspective on data-driven analysis and control
Data informativity gives exact conditions for when measured data, rich or not, suffice for certifying controllability, designing stabilizing or deadbeat feedback, or solving LQR from data.