Linear Model Predictive Control for the encirclement of a target using a quadrotor aircraft
Bibliographic record
Abstract
Encirclement is a task accomplished by an Unmanned Aerial Vehicle (UAV) in order to maintain awareness and containment of a given target. The aim of the UAV encircling this target is to maintain close proximity at all times. In this paper, the problem of maintaining a circular path around a target is considered and a Linear Model Predictive Control (LMPC) strategy is implemented on a Qball-X4 quadrotor aircraft in order to follow the circular path. A linear model for the two-dimensional movement of the UAV and its respective MP controller has been designed in MATLAB Simulink, simulated in a X-Plane/MATLAB interface and implemented on the actual vehicle in real-time. The results of the LMPC in simulation are compared to those found while implementing the algorithm on a physical platform. The contributions of this paper lay in the implementation of an autonomous Linear MP controller for the encirclement of a stationary target by a Qball-X4 quadrotor.
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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".