Model-Based Motion Control of a Robotic Manipulator With a Flying Multirotor Base
Bibliographic record
Abstract
This article presents new model-based adaptive motion control algorithms for an underactuated aerial robotic manipulator comprised of a conventional multirotor unmanned aerial vehicle (UAV) and a multilink serial robotic arm. Two control strategies are proposed to allow the manipulator to operate in the joint and task spaces. The proposed controllers incorporate the combined dynamics of the UAV base and the serial arm, and properly account for the two degrees of underactuation in the plane of the propellers. The control developments follow the so-called method of virtual decomposition, which by employing a Newtonian formulation of the UAV-manipulator dynamics, sidesteps the complexities associated with the derivation and parametrization of a lumped Lagrangian dynamics model. The algorithms are guaranteed to produce feasible control commands as the constraints associated with the underactuation are explicitly considered in the control calculations. A Lyapunov analysis demonstrates the stability of the overall system and the convergence of the motion tracking errors. Experimental results show the effectiveness of the proposed control strategies.
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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".