Grey Wolf Optimizer-Based Approach to the Tuning of Pi-Fuzzy Controllers with a Reduced Process Parametric Sensitivity
Bibliographic record
Abstract
This paper suggests the use of Grey Wolf Optimizer (GWO) algorithms to tune the parameters of Takagi-Sugeno proportional-integral-fuzzy controllers (PI-FCs) for a class of nonlinear servo systems. The servo systems are modelled by second order dynamics plus a saturation and dead zone static nonlinearity. The GWO algorithms solve the optimization problems that minimize discrete-time objective functions expressed as the weighted sum of the squared control error and of the squared output sensitivity function in order to achieve the parametric sensitivity reduction. The output sensitivity function is derived from the sensitivity model with respect to the modification of the process gain, and fuzzy control systems with a reduced process gain sensitivity are offered. Three parameters of Takagi-Sugeno PI-FCs are obtained by a new cost-effective tuning approach. Experimental results related to the angular position control of a laboratory servo system validate the tuning approach.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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".