Horizon-Dependent Tube MPC for Elliptical-Orbit Rendezvous Under Mass Uncertainty
arXiv:2608.27659
2026
Dynamics
1 ideas extracted · analyzed Sep 1, 2026
What the math gives to ML
The paper develops tube MPC for a time-varying relative-motion model on an eccentric orbit, with bounded disturbances and uncertainty in spacecraft mass. Its transferable mechanism is horizon-dependent propagation of uncertainty tubes through a nonautonomous linear system, allowing robust constraint margins to vary with prediction depth instead of using one overly conservative bound. This can be transferred to recurrent neural networks, neural state-space models, and learned controllers by propagating interval or zonotope tubes through network Jacobians and penalizing predictions that leave the resulting robust tube. The key falsifiable signature is a computable growth or contraction boundary for rollout uncertainty and a corresponding reduction in long-horizon constraint violations.
Ideas from this paper
✗ Failed on benchmark
2026
Attach a robust, horizon-dependent uncertainty tube to a recurrent neural state-space model or learned policy. Instead of training only the nominal rollout, propagate state-estimation, model, and disturbance uncertainty through local Jacobians and impose a loss that keeps the tube inside task constraints. The method should be especially useful when short-horizon predictions are accurate but small Jacobian gains cause long-horizon divergence.
Useful7/10
Difficulty5/10
Novelty7/10