Bioinspired backstepping sliding mode control and adaptive sliding innovation filter of quadrotor unmanned aerial vehicles
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Bibliographic record
Abstract
Quadrotor unmanned aerial vehicles have become the most commonly used flying robots with wide applications in recent years. This paper presents a bioinspired control strategy by integrating the backstepping sliding mode control technique and a bioinspired neural dynamics model. The effects of both disturbances and system and measurement noises on the quadrotor unmanned aerial vehicle control performance have been addressed in this paper. The proposed control strategy is robust against disturbances with guaranteed stability proven by the Lyapunov stability theory. In addition, the proposed control strategy is capable of providing smooth control inputs under noises. Considering the modeling uncertainties, the adaptive sliding innovation filter is integrated with the proposed control to provide accurate state estimates to improve tracking effectiveness. Finally, the simulation results demonstrate that the proposed control strategy provides satisfactory tracking performance for a quadrotor unmanned vehicle operating under disturbances and noises.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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