Automation of a Quarter-Car Suspension Test Bench
Bibliographic record
Abstract
This paper aims to describe a quarter-car suspension test bench automation process to be utilized in an academic environment. The project is made up of pneumatic system modeling and control system design. An analysis of the bench’s pneumatic system is carried out. This pneumatic system is composed of a pneumatic actuator and a proportional directional control valve, which are responsible for generating the road profile. It is proposed a model to compensate the non-linearities present in the pneumatic system measurement process and the disturbances caused by the under test suspension system, as well as a control strategy for small displacements of the load through linear control approaches, which provide the necessary flexibility to directly influence the parameters affecting the dynamics of the excitation system platform’s displacement, thereby reducing the complexity of the controller design to be adopted. Furthermore, analyses are conducted on the effectiveness of the control in generating sinusoidal track profiles for a frequency range between 0 and 5 Hz, where the repeatability of this process and the safety of the electrical and mechanical components of the test bench are observed. Numerical results, obtained through the excitation system control of the quarter-car suspension test rig, have shown success in generating sinusoidal road profiles, which allows it to investigate and develop suspension systems based on the quarter-vehicle model.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.004 |
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".