Comparative analysis on performances of adjustable-gain single-neuron PID controllers based on general fuzzy logic and normal cloud model
Why this work is in the frame
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Bibliographic record
Abstract
The solutions to parameter setting of PID controllers have always been an essential problem of control system design. The single-neuron PID controller can achieve the parameters' self-adaption to the real operation conditions by adjusting the gain value. Two computational intelligent algorithms deriving their theory sources from the uncertain reasoning are introduced to realize the on-line adjustments of gain, which are general fuzzy logic and a normal cloud model with universality. The designs on these two regulators are given containing the forms of the membership functions under the fuzzy logic & cloud model, control rules for 1-dimensional & 2-dimensional input modes, and the inference models, etc. The numerical simulations are implemented and the comparative analysis on the dynamic performance is presented based on the existent step responses and the adjustable parameters' curves with adaptive changes. By contrast, it concludes that a 2-dimensional normal cloud model leads to the desired overshoot, and its 1-dimensional model with shorter program running time adapts to occasions of high real-time demands, and fuzzy logic regulators can better meet the control requirements of the shorter setting time.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 it