Real Time Software-in-the-Loop Simulation for Control Performance Validation
Bibliographic record
Abstract
This paper illustrates an effective real-time software-in-the-loop (SIL) simulation technique for control design performance validation through two case studies in automotive systems: electric power steering (EPS) control and drive control for a switch reluctance motor (SRM) powered by a fuel cell. This technique, if implemented appropriately, will be able to shorten the prototyping time for control system research and development in both academic and industrial areas. The two cases presented involve complicated dynamics: road/tire steering dynamics and chemical/electrical dynamics in a fuel cell, for which software packages are available to simulate. Therefore, for the purpose of steering and SRM drive control performance validation, successful real-time simulation is desired through interfacing with these software, i.e. making software package in the loop. The case studies presented in this paper demonstrate the effectiveness of this concept. The presented real-time SIL simulation is conducted on a two-node computer platform engineered by RT-Lab, operating in fixed-step real time. Comparison between real-time and off-line simulation is also presented.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 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".