Experimental Assessment of a Controlled Slippage Magnetorheological Actuator for Active Seat Suspensions
Bibliographic record
Abstract
Passive air springs are the golden standard in heavy vehicle seat suspensions, as they provide economical means to isolate drivers from road disturbances. They are nevertheless likely to expose drivers to vibration levels higher than recommended by the ISO-2631-1 standard over a typical 8-h shift. Although existing commercial active seat suspensions have proven their superiority over passive suspensions, practical limitations such as cost or lack of overall dynamic performance have slowed down their widespread adoption. Controlled slippage magnetorheological (MR) actuators are a promising alternative because they offer a dynamic performance similar to direct-drive motors in a packaging and cost similar to economical geared motors. This paper is the first to experimentally assess the overall closed-loop performance of an active seat suspension powered by a controlled slippage MR actuator including vibration attenuation, power consumption, and seat travel. Unlike semiactive MR actuators such as MR dampers that have been extensively studied, controlled slippage MR actuators are fully active and offer a significantly better performance for rough road conditions. The active seat was tested in a laboratory on a vibrating platform recreating the floor acceleration profile of a dump truck rolling on a quarry road. The seat was also tested on an actual highway truck rolling on a roadway. Results show that with a linear-quadratic-Gaussian controller, the proposed active suspension effectively reduces floor vibrations by a factor of 2-3, while using an average power consumption of 86 W and having an average relative travel range of 1-10 mm root mean square. These results fall in line with commercially available active seat suspensions.
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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) | 0.001 | 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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".