Fuzzy Control Systems with Reduced Parametric Sensitivity Design Based on Hybrid Grey Wolf Optimizer–Particle Swarm Optimization
Bibliographic record
Abstract
This paper proposes an optimal tuning method for Takagi-Sugeno-Kang Proportional-Integral fuzzy controllers (TSK PI-FCs) based on a novel hybridization of Grey Wolf Optimizer (GWO) and Particle Swarm Optimization (PSO) algorithms referred to as hybrid GWO-PSO algorithm. The optimization problem defined for servo system processes controlled by TSK PI-FCs is solved by applying the hybrid form of GWO-PSO in the minimization of an objective function that depends on the output sensitivity function of the sensitivity model. The sensitivity analysis applied to these fuzzy control systems produces sensitivity models regarding the parametric variations of the dynamic processes subjected to control (i.e., the servo system). Solving the optimization problem implies the minimization of the objective function by means of the hybrid GWO-PSO algorithm. This hybrid variation of the two nature-inspired algorithms allows an increased control over the exploitation phase by inserting PSO search process features along with the exploration capabilities of GWO. The design method is validated using an experimental setup based on a nonlinear servo system.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".