Data-Driven Fractional-Order PID Controller Tuning for Liquid Slosh Suppression Using Marine Predators Algorithm
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Bibliographic record
Abstract
Traditional control system development for liquid slosh problems often relies on modelbased approaches, which are challenging to implement in practice due to the chaotic and complex nature of fluid motion in containers.In response, this study introduces a data-driven fractional-order PID (FOPID) controller designed using the Marine Predators Algorithm (MPA) for suppressing liquid slosh.The MPA serves as a data-driven tuning tool to optimize the FOPID controller parameters based on a fitness function comprising the total norms of tracking error, slosh angle, and control input.A motor-driven liquid container undergoing horizontal motion is employed as a mathematical model to validate the proposed data-driven control methodology.The effectiveness of the MPA-based FOPID controller tuning approach is assessed through the convergence curve of the average fitness function, statistical results, Wilcoxon's rank test, and the ability to track the cart's horizontal position while minimizing the slosh angle and control input energy.The proposed data-driven tuning tool demonstrates superior performance compared to other recent metaheuristic optimization algorithms across the majority of evaluation criteria.
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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 | 0.001 |
| 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