Data-driven stabilization of continuous-time systems with noisy input-output data
For control theorists and practitioners, this work extends data-driven stabilization to continuous-time systems with noisy data, offering a rigorous condition for data informativity.
The paper provides a necessary and sufficient condition for noisy input-output data to be informative for quadratic stabilization of continuous-time systems, formulated as linear matrix inequalities that yield a stabilizing controller.
We study data-driven stabilization of continuous-time systems in autoregressive form when only noisy input-output data are available. First, we provide an operator-based characterization of the set of systems consistent with the data. Next, combining this characterization with behavioral theory, we establish a necessary and sufficient condition for the noisy data to be informative for quadratic stabilization. This condition is formulated in terms of linear matrix inequalities, whose solutions yield a stabilizing controller. Finally, we characterize data informativity for system identification in the noise-free setting.