Adaptive Observer-Based Super-Twisting Sliding Mode Control for Low Altitude Quadcopter Grasping
Bibliographic record
Abstract
This article offers an improved robust altitude control solution for an unmanned aerial vehicle (UAV) load grasping system at low altitude under ground-effect and varying loads. We propose a novel technique for adaptive gain selection of the higher-order sliding mode observer (HOSMO). The adaption rate is proportional to the absolute value of the errors computed between the real noisy position measurements and their estimation provided by the observer. In addition, it can adjust to bidirectional disturbance bounds found in UAV grasping applications to avoid overestimation of the gains. The disturbance observer is integrated with a super-twisting sliding mode controller to achieve robust altitude control, effectively attenuating the chattering phenomenon. Moreover, disturbance-based gain conditions are avoided because of the adaptive law for the HOSMO. System stability and finite-time convergence of the adaptive HOSMO are investigated using Lyapunov theory, even in the presence of Lebesgue-measurable noise. Validation is performed through multi-stage simulations using a PX4-powered Clover drone Gazebo simulator and real-time experiments involving low-altitude pick-and-place scenarios with a COEX Clover drone equipped with a rigid gripper mechanism.
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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".