Real-Time Hardware-in-the-Loop in Railway
Bibliographic record
Abstract
The increasing complexity of modern ground vehicles is making crucial the role of control for improving energetic efficiency, comfort and performance. At the same time, the control software must be frequently updated in order to let the vehicle respond safely and efficiently within more sophisticated environments and to optimize the operations when new vehicle components are integrated. In this framework real-time hardware-in-the-loop simulations represent a fundamental tool for supporting the verification and validation processes of the control software and hardware. In this chapter a railway case study will be presented. The mathematical models of the most relevant electromechanical components of the vehicle powertrain are presented: the pantograph connected to an ideal overhead line with continuous voltage; the electrical components of a pre-charge circuit, the line filter and the braking chopper; the three-phase voltage source inverter and the induction motor; and, finally, the mechanical transmission system, including its interactions with the rail. Then the issues related to the real-time simulation of the locomotive components models are discussed, concentrating on challenges related to the stiff nature of the dynamic equations and on their numerical integration by combining field programmable gate array (FPGA) and central processing unit (CPU) boards. The usefulness of the real-time hardware-in-the-loop simulations for the analysis of railway control software will be demonstrated by considering the powertrains of two real metropolitan trains under complex scenarios, i.e., stator winding disconnection of the induction motor, pantograph missing contact, wheel-rail slipping phenomenon.
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How this classification was reachedexpand
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) | 0.001 | 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 | 0.002 |
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 teacher head, 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".