Theoretic and genetic design of a three-rule fuzzy PI controller
Bibliographic record
Abstract
This paper describes the optimal design of a fuzzy PI controller based on theoretical fuzzy analysis and genetic-based optimizations. The most important feature of the proposed controller is its simple structure, consisting of a single input variable, three rules, and four design parameters. The four parameters are a fuzzy integral gain, a scalar factor for crisp output, and two parameters for the allocation of the membership functions of the fuzzy sets. A closed-form solution for the proportional control action is defined in terms of the design parameters. The nonlinear proportional gain is explicitly presented in the error domain. Through genetic algorithms, the optimal design of the system is achieved. This new method has been applied for two problems, a first-order process with/without a time delay, and an overdamped second-order process. A practical limitation on actuator saturation is considered in the simulation. Good simulation results were obtained using the present method, which produced superior control performance in handling nonlinearities due to time delay and saturation.
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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 | 0.000 | 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 | 0.001 |
| 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".