Adaptive Observer-Based Super-Twisting Sliding Mode Control for Low Altitude Quadcopter Grasping
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
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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