Enhancing quarter car active suspension performance using walrus optimization algorithm-based PID controller
Bibliographic record
Abstract
The suspension system of a vehicle is one of the main components of security and comfort for the driver and the car passengers. Therefore, the use of precise controller is vital to ensure the reliability of these vehicles. This paper presents a novel approach to tuning the proportional-integral-derivative (PID) controller of an active suspension system using the walrus optimization algorithm (WOA). This bioinspired meta-heuristic technique is used to find the optimal gains for the PID controller considering the minimization of the mean absolute tracking error. Systematic rules are defined to drive the optimization procedure of the controller. The system is simulated in three scenarios: a flat road, a speed bump, and a pothole, allowing to evaluate the adaptability of the controller in average traffic conditions. The results of 50 experiments demonstrate the effectiveness of the proposed method in finding the best gains for the controller, resulting in satisfactory performance and minimizing the tracking error to a residual value. A comparison with the classical closed-loop Ziegler & Nichols method is provided, where the proposed method ensures a tracking error reduction of ${9 4. 7 6 \%}$ and a root mean square error reduction of ${9 4. 3 3 \%}$. Additionally, the overshoot during the startup transient regime was reduced by ${9 0. 6 6 \%}$, while also eliminating the settling time of the system when evaluated utilizing the $2 \%$ criteria.
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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".