Optimal Control Design for Propeller Pendulum Systems Using Gorilla Troops Optimization
Bibliographic record
Abstract
This study conducts a comprehensive examination of the nonlinear propeller pendulum system's angular position control, utilizing three distinct control strategies: Proportional-Integral-Derivative (PID) controller, State Feedback (SF) controller, and Sliding Mode Control (SMC).In order to optimize the performance of each controller, Gorilla Troops Optimization (GTO) is employed to identify the optimal value of the controllers' design parameters.The dynamics of the system under each controller are simulated via MATLAB software, and the performance of the controlled system is quantitatively assessed utilizing the Integral Time of Absolute Error (ITAE).The resilience of the controllers under uncertainties is evaluated by introducing an external disturbance to the system.Simulation results indicate that the SMC, tuned by GTO, exceeds the performance of the other controllers in reducing the settling time, eliminating maximum overshoot, and minimizing the ITAE index.Moreover, under external disturbance, the SMC tuned by GTO demonstrates superior robustness compared to other controllers.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".