Open-loop linear modeling method for unstable flow utilizing built-in data-driven feedback controllers

Low-order linear models serve as a foundational tool for flow analysis and control design for unstable flow systems. Conventional modeling approaches impose strict requirements on initial base flow and training process to ensure input-output identifiability. To address these limitations, this study presents a novel open-loop input-output modeling framework based on closed-loop identification principles, which leverages real-time control feedback generated by an adaptive data-driven, model-free controller to dynamically balance external excitation and inherent flow disturbances. This framework demonstrates enhanced modeling effectiveness and operational adaptability.