A learning-based fuzzy LQR control scheme for height control of an unmanned quadrotor helicopter
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Bibliographic record
Abstract
In this paper, a novel learning-based fuzzy Linear Quadratic Regulator (LQR) control method using Extended Kalman Filter (EKF) to optimize a Mamdani fuzzy LQR controller is presented. The EKF is used to adjust the shape of membership functions and rules of the fuzzy controller to adapt with the working conditions automatically during the operation process to minimize the control error. Then, the LQR controller is tuned according to the modified fuzzy membership functions and rules. The proposed approach in this paper is verified by testing and comparing performance of the height control problem of an unmanned quadrotor helicopter between the conventional LQR and learning-based fuzzy LQR controllers in the Matlab/Simulink. Simulation results show that developed method is effective for online optimization of fuzzy LQR controllers, improving control performance significantly.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 it