Data-driven stabilization of continuous-time systems with noisy input-output data

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.