Slime Mold Algorithm-Based Performance Improvement of PD-Type Indirect Iterative Learning Fuzzy Control of Tower Crane Systems
Bibliographic record
Abstract
This current paper proposes to improve the performance of three Single Input-Single Output (SISO) fuzzy control systems of controlling every position of tower crane systems using Proportional-Derivative (PD)-type indirect iterative learning rules at the higher hierarchical levels in each SISO control loop. The lower hierarchical levels in the three SISO control loops are built upon three low-cost Takagi-Sugeno Proportional-Integral (PI)-fuzzy controllers tuned by the initial application of Extended Symmetrical Optimum (ESO) method to the linear PI controllers and next the transfer of the results to the PI-fuzzy controllers in terms of the modal equivalence principle. Set-point filters are included at the lower hierarchical level in the context of the ESO method for overshoot reduction. The design approach is presented in a unified way for all three controllers. The gains of the PD-type learning rules are optimally computed in the iteration domain considering a metaheuristic Slime Mold Algorithm (SMA) in a transparent and simplified version, that settles the optimization problems with objective functions expressed as the sums of squared control errors multiplied by time. The enhanced performance is settled considering ten sets of iterations of SMA.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 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".