Optimal Control of a Semi-Active Suspension System Collaborated by an Active Aerodynamic Surface Based on a Quarter-Car Model
Bibliographic record
Abstract
This paper addresses the trade-off between ride comfort and road-holding capability of a quarter-car semi-active suspension system, collaborated by an active aerodynamic surface (AAS), using an optimal control policy. The semi-active suspension system is more practical to implement due to its low energy consumption than the active suspension system while significantly improving ride comfort. First, a model of the two-DOF quarter-car semi-active suspension in the presence of an active airfoil with two weighting sets based on ride comfort and road-holding preferences is presented. Then, a comprehensive comparative study of the improved target performance indices with various suspension systems is performed to evaluate the proposed suspension performance. Time-domain and frequency-domain analyses are conducted in MATLAB® (R2024a). From the time-domain analysis, the total performance measure is enhanced by about 50% and 35 to 45%, respectively, compared to passive and active suspension systems. The results demonstrate that a semi-active suspension system with an active aerodynamic control surface simultaneously improves the conflicting target parameters of passenger comfort and road holding. Utilizing the aerodynamic effect, the proposed system enhances the vehicle’s dynamic stability and passenger comfort compared to other suspension systems.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".