Optimized PID Controller for Quarter-Car Active Suspension System Under Various Road Profiles
Bibliographic record
Abstract
The main limitation of passive suspension system lies in their inherent compromise between ride comfort and car handling, resulting from their inability to dynamically adjust to varying road conditions. Efforts to enhance riding comfort often led to trade-offs that may compromise safety, and vice versa. This duality necessitates a more adaptable and flexible solution. Active suspension systems emerge as a transformative methodology, allowing real-time adjustments and dynamic modifications to damping characteristics. This capability effectively separates the compromise between ride enjoyment and safety, enabling an optimal equilibrium by adaptively responding to fluctuations in road conditions. This paper presents a quarter-car active suspension system to improve comfort under various road conditions. A PSO optimized PID controller is implemented to minimize both the sprung mass displacement, and the sprung mass acceleration subjected to single bump and dual bump road profile. The performance of the PSO-based PID controller is illustrated by simulation results in MATLAB, demonstrating significant improvements in body displacement and body acceleration, thereby enhancing the ride comfort by adaptively responding to road conditions in real time.
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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 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".