Investigation of using neuro-fuzzy and self-tuning fuzzy controller to improve pitch angle response of twin rotor MIMO system
Bibliographic record
Abstract
The neuro-fuzzy based fuzzy subtractive clustering method (FSCM) and self-tuning fuzzy proportional-derivative (PD) like controller (STFPDC) were used to solve nonlinearity and trajectory problems of pitch angle control of the twin-rotor multi-input–multi-output (MIMO) system (TRMS). The control objective is to make the vertical beam of the TRMS reach a desired position quickly and accurately. The proposed adaptive network-based fuzzy inference system (ANFIS) – STFPDC is designed to improve the fuzzy logic controller (FLC) response and overcome the high demands for computation resources. To simplify the complexity of STFPDC, ANFIS-based FSCM was used to simplify the controller and improve the response. The proposed controller achieved satisfactory objectives under different input signals. Simulation results using MATLAB Simulink demonstrated an improvement in response and the superiority of simplified STFPDC compared with the FC.
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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 | 0.001 | 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 | 0.000 | 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".