On Data-Driven Control: Informativity of Noisy Input-Output Data With Cross-Covariance Bounds

In this paper we develop new data informativity based controller synthesis methods that extend existing frameworks in two relevant directions: a more general noise characterization terms of cross-covariance bounds and conditions for control on input-output data. Previous works have derived necessary sufficient noisy input-state with quadratic via an S-procedure. Although these do not capture general, show the S-procedure is still applicable obtaining non-conservative Informativity-conditions stability, $\mathcal{H}_\infty$ $\mathcal{H}_2$ are developed, which also Simulation experiments illustrate can be less conservative informativity, compared to norm typically employed literature.