On Equivalence of Data Informativity for Identification and Data-Driven Control of Partially Observable Systems

This study shows that the informativity for identification of partially observable systems is equivalent to designing dynamical measurement-feedback stabilizers. finding entirely different from input-state case, where direct data-driven design state-feedback stabilizers requires less than system identification. We derive equivalence between two types based on a newly introduced vector autoregressive with exogenous input (VARX) framework, which suitable time-domain analyses such as state-space models while directly representing input–output characteristics transfer functions. Moreover, we show duality characterization all VARX explaining data and controllers stabilizing models.