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

• 成果类型: Article
• 署名作者: Sadamoto, Tomonori
• 署名单位: University of Electro-Communications - Japan
• 刊物名称: IEEE TRANSACTIONS ON AUTOMATIC CONTROL
• ISSN/ISSBN: 0018-9286
• DOI: 10.1109/TAC.2022.3202082
• 发表日期: 2023
• 页码: 4289-4296
• 关键词: Index Terms-Data informativity; Data-driven control; partially observable systems; System identification

摘要

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.