Fractional delayed feedback for semi-active suspension control of nonlinear jumping quarter car model
Bibliographic record
Abstract
Off-road vehicles often experience severe vibrations caused by jumping. These impact dynamics demonstrate rich nonlinear behaviors, including chaotic vibrations, which are undesirable in vehicle performances. Semi-active suspensions are a promising method to eliminate chaos because of their lower energy consumption. However, semi-active suspensions impose passivity constraints that require robust controllers. In this study, a fractional delayed feedback (DF) control is proposed for a semi-active suspension. A quarter car model with jumping nonlinearity is considered as a typical off-road vehicle model. The performance and applicability of the proposed fractional DF are numerically analyzed. Performance analysis revealed that fractional DF effectively stabilized chaos into periodic motion in the presence of passivity constraints owing to the semi-active suspension. The stabilization range is affected by the controller parameters, particularly the fractional order. The applicability analysis revealed that the fractional DF is robust to forcing frequency variations and noise contamination. The results demonstrate that fractional DF has higher applicability in semi-active suspensions than conventional DF.
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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) | 0.001 | 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".