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Formulas for Data-Driven Control: Stabilization, Optimality, and Robustness
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DOI：10.1109/TAC.2019.2959924.png)
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Abstract
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In a paper by Willems et al., 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.
Keywords:
Control design
data-driven control
learning systems
linear matrix inequalities
nonlinear control systems
robust control
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