Optimal Real-Time Trajectory Control of a Pitch-Hover UAV with a Two Link Manipulator
Bibliographic record
Abstract
Rotary-wing-based Unmanned Aerial Manipulators (UAM) are gaining attention for their in-place hovering capability, holonomy in motion, and safe operation due to their redundancy. Task-oriented optimal control strategies can also further improve the flight time by generating energy-optimal coordinated motions. This paper addresses the dynamic modeling, trajectory tracking and control of an UAM that consists of an under-actuated rotary-wing UAV, with a novel pitch-hover-maneuvering capability, and a 2-DOF robotic arm. A decoupled velocity-based Model Predictive Control (MPC) strategy is proposed for tracking a trajectory in the sagittal plane of the UAM while the UAV base pitch hovers in place. Conventional PD controllers were used to generate set-point velocity screws as the inputs to the MPC. A partitioned (but complete) dynamic model of the UAM was developed and used for implementing the proposed control strategy in a simulated environment. The MPC controller takes the force/torque exerted (by the arm) on the UAV base into account and augments the desired control inputs accordingly. The proposed control strategy provides the following advantages: (1) it can be implemented in real time since a linearized dynamic model of the UAV base is used, (2) it provides a generic control structure that can be applied to different classes of UAMs.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".