Active Roll Control of Heavy Single Unit Vehicles Employing MEMS Angular Rate Sensors
Bibliographic record
Abstract
The present paper is concerned with the use of active roll control to improve the roll stability of heavy road-vehicles and the application of Micro-electro-mechanical System (MEMS) angular rate sensors in the feedback monitoring. For this purpose, mathematical models that represent the roll/yaw dynamics for a torsionally rigid Single Unit Vehicle (SUV) is presented. The state-space models that represent the vehicle dynamics are also developed for the purpose of performing numerical simulations. A linear Quadratic Gaussian (LQG) based controller, using Kalman estimator to estimate certain states, is employed to design a full-state active roll control system. A mathematical model that represents the dynamic behavior of a low-cost MEMS gyroscope is derived for the purpose of investigating the suitability of applying this class of angular rate sensor in the roll control of heavy vehicles. Some reliability issues related to MEMS sensors, such as noise and drift, are introduced and included in vehicle dynamic models.
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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 | 0.000 |
| 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 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".