Enhancing UAV Altitude Control: PID Tuning with Reinforcement Learning and Genetic Algorithm
Bibliographic record
Abstract
This paper explores the optimization of Proportional-Integral-Derivative (PID) controller gains to enhance the altitude control of Unmanned Aerial Vehicle (UAV)s, focusing on achieving precise tracking of set points and trajectories. Traditional manual tuning methods often yield suboptimal performance, limiting the controller's ability to balance stability and responsiveness under varying conditions. To address this limitation, we investigate automated optimization techniques, employing Reinforcement Learning (RL) and Genetic algorithm (GA) to refine the PID gains systematically. These methods enable dynamic solution space exploration, identifying parameter combinations that maximize controller performance. Comparative analyses reveal that algorithmically optimized gains consistently outperform those manually tuned by human operators. The results show significant improvements in stability and responsiveness. Using gains tuned by the RL, the system achieves altitude control with minimum overshoot. Integrating advanced optimization approaches into PID tuning enhances controller efficiency and significantly reduces the time and effort required for calibration. Experimental validation demonstrates the practical potential of these techniques, underscoring their effectiveness in improving the reliability and precision of aerial vehicles. This study emphasizes the role of intelligent optimization in advancing UAV control systems, paving the way for broader applications in autonomous aerial navigation and control systems. The video of the experiment conducted at Ericsson's 5G Autonomous Vehicles Lab, located at Carleton University, can be found here: https://youtu.be/oH3rQRhPMGs
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How this classification was reachedexpand
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 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".