Fuzzy-logic controller synthesis for electro-mechanical systems with nonlinear friction
Bibliographic record
Abstract
We report on preliminary results in developing a new method for the synthesis of fussy-logic controllers for amplitude-sensitive nonlinear plants based on sinusoidal-input describing-function methods. This technique exploits the fact that two traditional classes of fuzzy-logic controllers are, in functional terms, of the proportional-plus-derivative (PD) and proportional-plus-integral (PI) types. It involves the direct generation of the membership functions and output levels based on the "frequency response" of the nonlinear plant in the describing-function sense. The resulting fuzzy-logic controller obtained in this paper includes derivative action in an inner-loop feedback path (nonlinear rate feedback) and nonlinear PI compensation in the forward path; the performance of the closed-loop system is, by design, quite insensitive to reference-input amplitude. An illustration of the method and its effectiveness is provided, based on a prototypical position control problem where a servomotor plus mechanical load are characterized by torque saturation and nonlinear friction.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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 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".