Design of Semiactive Vehicle Suspension Controls in Frequency Domain
Bibliographic record
Abstract
Through the simulation study of a semiactive quarter car suspension, this paper is to expatiate on the control algorithm documented in the United States Patent 6,873,890 [1]. That patent presents a new method to design semiactive suspension controls in the frequency domain. As is well known, suspension related dynamics has two dominant modes in the working frequency range up to 25Hz. As such, the suspension dynamic system has three distinguishable frequency sections. In order to achieve better performance, different controls have to be applied to each frequency section, respectively. The significant core part of the patented algorithm is to provide an approach to identify the excited frequencies in real time that are transmitted through the vehicle suspension. Then different controls of such as skyhook, groundhook and other damping strategies are combined accordingly to accomplish the best performance overall. Thus through the suspension control the vehicle dynamics (such as ride and handling) is expected to be improved in the broad frequency range in comparison to passive suspensions with a trade-off design.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".