Optimal and Robust FOPID Controller for Overhead Cranes Systems Based PSO
Bibliographic record
Abstract
Recently, the Fraction Order PID (FOPID) used widely instead of the traditional proportional-integral-derivative (PID) due to its important advantages, including simple structure, good tracking, high robustness against external disturbance, and its ability to handle the parameters variations and high nonlinear in the dynamic model of the systems.This paper aims to design an optimal robust control method with a good tradeoff between the transient response specifications and high robustness against external disturbance and system uncertainty.To achieve this aim, multi-objective optimizationbased particle swarm optimization (PSO) has been used to tune the gain parameters of the FOPID.The fractional parameters of the FOPID add flexibility to tune the response of the dynamic system.A new composite objective function has been used to ensure minimum swing with good tracking.Selecting suitable values for the weighting function in the objective function represent an important step in the proposed control method.The simulations are carried out for the presented controller and the results are compared with several controllers.The simulation results illustrated superior performance of the suggested controller with good transient's specifications and high robustness against external disturbance.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".