Deep Reinforcement Learning of Position and Velocity PID Control for Rotational Wing Unmanned Aerial Vehicles
Bibliographic record
Abstract
A Deep Reinforcement Learning (DRL) network in a mission controller was engaged with position and velocity Proportional, Integral, and Derivative (PID) flight controllers in a Rotational Wing Unmanned Aerial Vehicle (RWUAV) as a Software In The Loop (SITL). The DRL network was trained with 50-point flight telemetry data including gyroscope at 10Hz rate, and it dynamically updated PID controllers with their coefficients to hold a target position as initial climb and final approach flight phases, where a RWUAV is susceptible to most accidents. The DRL network was implemented with 5 inner layers and REINFORCE TF-agent. As a baseline, a static PID RWUAV's position holding response under random wind direction and velocity was examined. With mean velocity of 5m/s random wind, the DRL-engaged RWUAV's training and evaluation processes were collected and compared with static PID controller RWUAV performance. The DRL network with dynamic position and velocity PID controller coefficient updates improved position hold consistency by 65% at 0.17m on average from a static PID controller's 0.50m under 5m/s average velocity random wind.
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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 | 0.001 | 0.002 |
| 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 | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".