Fictitious Reference Iterative Tuning of Active Disturbance Rejection Control Combined with Fuzzy Logic for Crane Systems
Bibliographic record
Abstract
The current paper proposes the introduction of fuzzy logic to replace the linear Proportional-Derivative (PD) component of second Active Disturbance Rejection Control, resulting in the so-called Fuzzy Active Disturbance Rejection Control (FADRC), whose parameters are tuned using Fictitious Reference Iterative Tuning (FRIT). The novel FADRC-FRIT algorithm is validated using experiments on the 3D crane laboratory equipment by controlling the x-, y-, and z-axes. The main purpose of the current mix lies in its ability to automatically and optimally tune the parameters of ADRC, which is improved with PD Takagi-Sugeno fuzzy control and tuned in a model-free FRIT manner. The secondary purpose is the time efficiency achieved in identifying the optimal control parameters.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".