Fault tolerant control of electric power steering using H-infinity filter-simulation study
Bibliographic record
Abstract
In a physical system, faults could occur time to time and, for safety purpose, measures have to be implemented to keep the physical system continuously operating if a fault does not result in global shut-down. In this paper, a Fault tolerant Control (FTC) design is proposed for the electric power steering (EPS) system in an automobile to address the case of electric connection fault incurred in the torsion bar torque sensor. In particular, the total torque that acts on the assisting motor shaft is the sum of the torsion bar torque and road reaction torque. An H¿filter is designed to estimate the total torque on the motor shaft and the road reaction torque can be calculated out using the available signals from the vehicle stability system. The torque sensor signal can then be deduced and used to replace the torsion bar sensor signal when the electric connection is broken. It is claimed that the proposed robust filter design yields huge advantage over a Luenberger type estimator.
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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 | 0.001 |
| 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 | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".