An On-Line Optimal Controller for a Commuter Train
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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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".