A Band-Stop Filter-Based LQR Control Method for Semi-Active Seat Suspension to Mitigate Motion Sickness
Bibliographic record
Abstract
This study proposes a novel control framework for semi-active seat suspensions, specifically targeting motion sickness mitigation through precision suppression of vertical vibrations within the 0.1–0.5 Hz frequency range. Firstly, a fractional-order band-stop filter in conjunction with a linear quadratic regulator (LQR) controller under frequency-domain sensitivity constraints (0.1–0.5 Hz) is proposed to achieve frequency-selective vibration attenuation. Secondly, the multi-objective butterfly optimization algorithm (MOBOA) is adopted to optimize the LQR controller’s weighting matrices (Q, R) by balancing conflicting requirements in terms of human body displacement limits, acceleration thresholds, and suspension travel. Finally, experimental validation under concrete pavement excitation and random road profiles demonstrates significant advantages over conventional LQR, i.e., a 41.04% reduction in vertical vibration amplitude and a 55.95% suppression of acceleration peaks within the target frequency band. The combined enhancements offer dual benefits of enhancing ride comfort and motion sickness mitigation in real-world driving scenarios.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".