Advanced Hybrid Nonlinear Control for Morphing Quadrotors
Bibliographic record
Abstract
This paper presents a novel nonlinear control strategy tailored for foldable quadrotors capable of altering their morphology in-flight. By changing the quadrotor's shape through servo motors, these adaptive systems can effectively overcome various challenges, including diverse environmental conditions, such as obstacles and climate variations. The proposed control approach utilizes a double-loop control method to enhance the robustness and performance of the folding quadrotor during flight. The outer loop consists of a nonlinear PID controller responsible for regulating motion in the X and Y directions, while the inner loop features a sliding mode controller for altitude control in the Z direction and attitude stabilization. This dual-loop structure ensures the quadrotor's stability even during arm folding. The firefly algorithm is employed to optimize the parameters of both controllers, minimizing position errors using the Root Mean Square Error (RMSE) as a performance metric. Our results demonstrate a significant improvement in the quadrotor's performance and adaptability to various proposed morphologies, with error rates reduced to near-negligible levels for all shapes. Specifically, the X position error was reduced by 100% for all morphologies, the Y position error by 95% for the X shape, 97% for the T shape, 98% for the H shape, and 99% for the O shape, and the Z position error by 99% for all morphologies.
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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 | 0.000 |
| 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) | 0.001 | 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".