This paper integrates reference governance with Dynamic Mode Adaptive Control (DMAC) to enforce constraints without a prior plant model. DMAC identifies and updates a discrete-time model online from measured state and input data, and the reference governor (RG) uses this identified model, rather than the true plant dynamics, to predict constrained closed-loop behavior. Because the prediction model changes as DMAC adapts, a governed reference that was previously feasible may become infeasible, and the classical interpolation-based RG may be unable to move the reference in the direction required to recover predicted constraint satisfaction. The proposed DMAC-RG framework instead searches directly over the governed reference rather than over an interpolation factor. When feasible references exist, the governed reference is selected to track the desired command, with an optional penalty on deviation from the previously applied reference. When the finite-horizon feasible set is empty, the reference is selected to minimize predicted constraint violation while retaining the same tracking preference. This work is a numerical investigation and does not provide theoretical guarantees of recursive feasibility, constraint satisfaction, or closed- loop stability. Numerical examples on a linear system, a nonlinear Van der Pol oscillator, and a high-dimensional Burgers equation under partial-state measurement demonstrate that the proposed RG enforces constraints using only the model identified online by DMAC, without access to the true plant dynamics or the complete system state.
🌊
