DESIGN OF AN ACTIVE SUSPENSION CONTROL FOR A VEHICLE MODEL USING A GENETIC ALGORITHM
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 it