Conformal prediction constructs finite-sample confidence sets for closed-loop error trajectories in stochastic linear MPC, relaxing joint-in-time chance constraints into a deterministic indirect-feedback formulation with recursive feasibility and satisfaction guarantees.
A Sampling-and-Discarding Approach to Chance-Constrained Optimization: Feasibility and Optimality,
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Conformal Prediction-Based MPC for Stochastic Linear Systems
Conformal prediction constructs finite-sample confidence sets for closed-loop error trajectories in stochastic linear MPC, relaxing joint-in-time chance constraints into a deterministic indirect-feedback formulation with recursive feasibility and satisfaction guarantees.