Experimental Assessment of a Controlled Slippage Magnetorheological Actuator for Active Seat Suspensions
Why this work is in the frame
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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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 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 it