AURORA: Autonomous Updating of ROM and Controller via Recursive Adaptation

Abstract

Real time model based control of high dimensional nonlinear systems presents severe computational challenges. Conventional reduced order model control relies heavily on expert tuning or parameter adaptation and seldom offers mechanisms for online supervised reconstruction. We introduce AURORA, Autonomous Updating of ROM and Controller via Recursive Adaptation, a supervisory framework that automates ROM based controller design and augments it with diagnostic triggered structural adaptation. Five specialized agents collaborate through iterative generate judge revise cycles, while an Evaluation Agent classifies performance degradation into three operationally distinct categories, subspace inadequacy, parametric drift, and control inadequacy, and routes corrective action to the responsible agent. For linear ROMs, we analytically prove that this classification is correct under mild assumptions and that the supervisory switching cycle preserves exponential stability subject to a dwell time condition. For nonlinear systems, the absence of a universal Lyapunov construction for autonomously discovered ROM structures precludes analogous analytical guarantees, so we validate the same classification empirically. Experiments on eight benchmark systems with state dimensions up to 5177 compare AURORA against expert tuned baselines, gain scheduled control, and online RLS adaptive alternatives. Controlled fault injection experiments confirm 91 percent diagnostic routing accuracy. AURORA achieves 6 to 12 percent tracking improvement over expert baselines and 4 to 5 percent over classical adaptive alternatives.