Certifiable Explicit Model Predictive Control for Spacecraft Rendezvous under Bounded Disturbances

What the math gives to ML

The paper provides a concrete pipeline for converting finite-horizon robust MPC into a fixed-time piecewise-affine controller and certifying disturbed closed-loop behavior offline with hybrid-zonotope reachability. Its transferable asset is the combination of region-indexed affine feedback, bounded-disturbance set propagation, and a finite-horizon terminal or invariant-set certificate. A neural policy can use the explicit controller as a hard safety shield, a teacher for region-aware distillation, or a locally affine fallback around an unconstrained learned policy. The key falsifiable prediction is that certified reachable sets remain inside constraints and enter the terminal set after a predicted number of steps, while an unshielded network violates these boundaries when disturbance magnitude or policy gain crosses a measurable threshold.