Study of Performance of Incorporating Pneumatic Suspension System with the Hydraulic Actuator for Quarter Car and Using Controllers with Genetic Algorithm
Bibliographic record
Abstract
Suspension systems are one of the main parts of the vehicle that provide the passenger comfort and stability, while it is difficult for conventional passive suspension systems to cope with the vibrations to which the vehicle is exposed. Air suspension systems have a dynamic character that allows good handling of the road and a comfortable ride, but in a certain area for this reason the stiffness of the air suspension must be flexibly changed. The air suspension has been developed with the inclusion of a hydraulic actuator to create an additional force that withstands the incoming vibration from the road. The pneumatic suspension system parameters, such as vertical acceleration, road holding, and vertical displacement, are improved continuously based on the controllers that have been used for nonlinear pneumatic suspension systems, fractional order proportional integral derivative (FOPID), and fuzzy logic control (FLC). The genetic algorithm is utilized to tune the controller's parameters to the nonlinear active pneumatic system's 3-DOF. A model's simulation outcomes with controllers improved the suspension performance. The proposed active pneumatic system and pneumatic suspension systems are compared when a vehicle is traveling at a speed of 72 km/h on an ISO level B road to test the ability and efficiency of the system to suppress body vibration to enhance safety and provide a comfortable ride on rough roads. According to simulation results, the improved suspension substantially lowers vertical vibrations and enhances road adaptability.
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How this classification was reachedexpand
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) | 0.000 | 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) | 0.000 | 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 teacher head, 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".