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- W4293370862 abstract "This study shows that the informativity for the identification of partially observable systems is equivalent to that for designing dynamical measurement-feedback stabilizers. This finding is entirely different from the input-state case, where the direct data-driven design of state-feedback stabilizers requires less informativity than system identification. We derive the equivalence between the two types of informativity based on a newly introduced vector autoregressive with exogenous input (VARX) framework, which is suitable for time-domain analyses such as state-space models while directly representing input–output characteristics such as transfer functions. Moreover, we show a duality between the characterization of all VARX models explaining data and that of all VARX controllers stabilizing such VARX models." @default.
- W4293370862 created "2022-08-28" @default.
- W4293370862 creator A5015869545 @default.
- W4293370862 date "2022-01-01" @default.
- W4293370862 modified "2023-09-30" @default.
- W4293370862 title "On Equivalence of Data Informativity for Identification and Data-Driven Control of Partially Observable Systems" @default.
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- W4293370862 doi "https://doi.org/10.1109/tac.2022.3202082" @default.
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