Handling Output Constraints in Control of Nonlinear Systems Using Koopman-Operator-Based Robust Reference Governor

We propose a data-driven robust reference governor (DRRG) framework to enforce output constraints in nonlinear systems with unknown dynamics and uncertainties. Leveraging the Koopman operator, DRRG maps complex dynamics into a higher-dimensional linear space for effective data-driven prediction. A data-driven maximal admissible set (MAS) and a min-max optimization problem are then formulated to compute robust reference signals. The proposed approach enhances robustness against uncertainties while preserving existing control architectures and simplifying controller tuning. Finally, a numerical simulation demonstrates its effectiveness.