Real-time model-based control of high-dimensional nonlinear systems faces computational intractability, while traditional reduced-order model (ROM) control requires manual expert tuning without online adaptation. We propose AURORA (\textbf{ A}utonomous \textbf{ U}pdating of \textbf{ RO}M and Controller via \textbf{ R}ecursive \textbf{ A}daptation), a multi-agent LLM framework automating ROM-based controller design with online adaptation. AURORA employs five specialized agents collaborating through iterative generation-judge-revision cycles, with an Evaluation Agent diagnosing degradation sources and routing corrections appropriately. Validated on eight benchmark systems spanning mechanical assemblies, thermal PDEs, and robots. Comparative evaluation across five state-of-the-art LLMs demonstrates high autonomy with minimal intervention, establishing practical viability for autonomous control design.
Categories: eess.SY cs.SY