DESIGN OF AN ACTIVE SUSPENSION CONTROL FOR A VEHICLE MODEL USING A GENETIC ALGORITHM
Bibliographic record
Abstract
This paper presents the design of an active suspension controller for an automotive vehicle using a genetic algorithm as the optimization technique. A four-degree-of-freedom model is used to represent a vehicle with different front and rear axes characteristics. The suspension deflection, tire deflection, vertical and angular acceleration are the performance criteria optimized. Different filters are used to model the frequency sensitivity of these criteria and the weighting is based on a passive suspension reference system. Independent front and rear controller optimization is performed with a genetic algorithm. The controllers include a linear gain matrix and a single filter. Each controller is designed to work with a minimum number of sensors and a limited order filter. To adapt the passive suspension components to the active system, the stiffness and damping of the suspension are optimized with values limited to a realistic range. Results show the impact of the various filters used to specify the critical frequency range of the inputs and outputs. This is observable for ride and handling criteria that are known to be frequency dependant. There is 38% improvement in the global performance of the active system compared to the baseline passive system.
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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 | 0.001 |
| 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 | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".