Design and Experimental Assessment of a Vibration Control System Driven by Low Inertia Hydrostatic Magnetorheological Actuators for Heavy Equipment
Bibliographic record
Abstract
Active suspension systems for automotive vehicles were developed in the past using hydrostatic, electric, magnetic and magnetorheological (MR) technologies to control road vibrations and vehicle dynamics and thus improve ride comfort and vehicle performance. However, no such systems were developed for heavy equipment, trucks and off-highway vehicles. For instance, agricultural tractors are still equipped with minimal suspension systems causing discomfort and health problems to drivers. The high suspension loads due to the massive weight of these vehicles are a challenge since high forces are needed to achieve efficient active suspension control. This paper presents an experimentally validated feasibility study of a hydrostatic, MR clutch-driven system of actuators. The scope of this paper is to evaluate the preliminary performance of the actuator for future vibration control. The hydraulic system allows the actuators to be remotely located from the wheels or cabin of the heavy vehicle and conveniently placed on the vehicle’s suspended frame. The design includes two MR clutches driven in an antagonistic configuration to push and pull on the end effector. Experiments on a laboratory prototype show that the low-inertia characteristics of the clutches allow a high blocked-output force bandwidth of 20 Hz with peak output forces exceeding 15 kN.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| 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".