Research on the enhancement method of vibration damping effect of vehicle fully active suspension system based on genetic algorithm and fuzzy control
Bibliographic record
Abstract
Based on the full active suspension and road input model, this paper introduces the fuzzy control theory and genetic algorithm design theory, adopts the fuzzy control method to control the actuator’s actuation force, creates the fuzzy control system of the automobile active suspension system, and optimizes the fuzzy control rules by using the improved genetic algorithm to ultimately realize the vibration damping effect enhancement in the process of driving the automobile vehicle. Simulation experiments and sample vehicle road experiments are used to verify the performance and utility of the fuzzy controller based on the improved genetic algorithm proposed in this paper. In the simulation experiments carried out with the help of Matlab/Simulink software, the control active suspension body controlled by the fuzzy controller based on the improved genetic algorithm reduces the root mean square value of angular acceleration of pendulum vibration, pitching rotation and lateral tilting rotation by 58.93%, 52.31% and 57.74%, respectively, compared with that of the conventional controller, the root mean square value of the dynamic deflection of the suspension is reduced, and the vehicle driving performance shows good stability and stability. The vehicle traveling shows good smoothness and stability. In the prototype road test, the root mean square value of the corresponding acceleration of the fuzzy-controlled active suspension optimized based on the improved genetic algorithm in this paper is reduced by 42.67%, 39.45% and 37.23%, respectively, compared with that of the passive suspension. Overall, the optimized design of fuzzy controller based on genetic algorithm proposed in this paper greatly improves the vibration damping effect of the active suspension 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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".