Vibration Control Schemes of Semi- Active Hydro-Pneumatic Dampers for Military Vehicle Suspension
Bibliographic record
Abstract
Hydro-pneumatic dampers are widely used in military and heavy vehicle suspension systems, where large suspension travel (more than 20 inches) is expected. Due to the nonlinear characteristics of these elements, suspension system performance and in particular ride comfort and road handling capabilities of the vehicle are altered. Although these nonlinear characteristics are inherent in nearly all vehicles suspension systems, their effects are dominant in certain maneuvers and especially in off-road conditions where the suspension system experiences rather large displacements. This paper investigates the control of a hydropneumatic damper suspension system, a highly nonlinear system comprised of a pneumatic spring (gas-spring) and a hydraulic damper. First, the hydro-pneumatic damper of a military vehicle is modeled. The model is validated using experiments performed with a dynamometer test machine. Based on the validated model, a 2DOF quarter car model is developed, simulated and analyzed. Secondly, the performance of two well-known semiactive control methods - Skyhook and Rakheja-Sankar (R-S) - are investigated as applied to suspension control in the 2DOF car model. To analyze the performance of these control strategies in the suspension systems where the nonlinear components exist, the method of averaging is deployed. Finally, a new control strategy based on Skyhook and R-S is proposed to address ride comfort and road handling utilizing the variable stiffness gas-spring together with a semiactive damper. The results of this new controller are then compared to that of several well known suspension control methods such as Skyhook to demonstrate the effectiveness of the method.
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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".