Conformal Distributionally-Robust Model Predictive Control with a Budgeted Multi-Step Barrier
Bibliographic record
Abstract
Ensuring safety under model uncertainty and distribution shift remains a central challenge for learning-enhanced model predictive control (MPC). In this study, Conformal Distributionally-Robust MPC with a Budgeted Barrier (CDRMPC) is introduced as a planner-first framework that combines (i) conformal residual calibration for finite-sample coverage, (ii) Wasserstein distributional robustness to hedge against distribution shift, and (iii) a horizon-coupled, budgeted multi-step barrier (BMB) that allocates safety slack over time rather than relying on myopic enforcement. The conformal-DR residual set adapts online to the data stream, and the BMB couples barrier slacks across the horizon via a weighted budget that is optimized and penalized in the MPC. Tractable formulations are obtained using linearization and norm-bounded reachableset over-approximations, probabilistic feasibility arguments are sketched, and validation is performed on an integrator and an inverted pendulum under parametric drift and injected bias. Compared to fixed-tube MPC, chance-constrained MPC, and adaptive-tube+CBF-QP shielding, CDR-MPC reduces safety overrides and violations at comparable control cost.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".