An On-Line Optimal Controller for a Commuter Train
Why this work is in the frame
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Bibliographic record
Abstract
This paper proposes an on-board optimal controller that drives a train between two stations in an optimal time efficient, energy efficient, or mixed-objective manner, while adhering to a set of system-specific constraints. To this end, at each step along the track, the train control problem is formulated and solved as a constrained optimization problem over the remainder of the trip, while utilizing the latest train sensor data. The optimization problem is a convex second-order cone program. It incorporates knowledge of the track profile and relevant velocity and propulsion/braking constraints in the computation of the optimal propulsion/braking commands. It features an option to enforce a safety buffer between the train and another leading train on the track. The resulting convex optimization problem can be efficiently solved using a simple numerical solver, ensuring global optimality and robustness of the solution. Real-time performance and simulated closed-loop control results are presented, for a realistic vehicle and advanced trip model on desktop and embedded computer architectures.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it