Design optimization of multi-objective proportional–integral–derivative controllers for enhanced handling quality of a twin-engine, propeller-driven airplane
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Herein, the design optimization of multi-objective controllers for the lateral–directional motion using proportional–integral–derivative controllers for a twin-engine, propeller-driven airplane is presented. The design optimization has been accomplished using the genetic algorithm and the main goal was to enhance the handling quality of the aircraft. The proportional–integral–derivative controllers have been designed such that not only the stability of the lateral–directional motion was satisfied but also the optimum result in longitudinal trim condition was achieved through genetic algorithm. Using genetic algorithm optimization, the handling quality was improved and placed in level 1 from level 2 for the proposed aircraft. A comprehensive sensitivity analysis to different velocities, altitudes and centre of mass positions is presented. Also, the performance of the genetic algorithm has been compared to the case where the particle swarm optimization tool is implemented. In this work, the aerodynamic coefficients as well as the stability and control derivatives were predicted using analytical and semi-empirical methods validated for this type of aircraft.
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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 | 0.002 |
| 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 it