Reinforcement Learning Based Prediction of PID Controller Gains for Quadrotor UAVs
Fuente:
arXiv
Enregistré dans:
| Auteurs principaux: | , , , , , , |
|---|---|
| Format: | Preprint |
| Publié: |
2025
|
| Sujets: | |
| Accès en ligne: | |
| Tags: |
Ajouter un tag
Pas de tags, Soyez le premier à ajouter un tag!
|
| _version_ | 1866929702150602752 |
|---|---|
| author | Sönmez, Serhat Montecchio, Luca Martini, Simone Rutherford, Matthew J. Rizzo, Alessandro Stefanovic, Margareta Valavanis, Kimon P. |
| author_facet | Sönmez, Serhat Montecchio, Luca Martini, Simone Rutherford, Matthew J. Rizzo, Alessandro Stefanovic, Margareta Valavanis, Kimon P. |
| contents | A reinforcement learning (RL) based methodology is proposed and implemented for online fine-tuning of PID controller gains, thus, improving quadrotor effective and accurate trajectory tracking. The RL agent is first trained offline on a quadrotor PID attitude controller and then validated through simulations and experimental flights. RL exploits a Deep Deterministic Policy Gradient (DDPG) algorithm, which is an off-policy actor-critic method. Training and simulation studies are performed using Matlab/Simulink and the UAV Toolbox Support Package for PX4 Autopilots. Performance evaluation and comparison studies are performed between the hand-tuned and RL-based tuned approaches. The results show that the controller parameters based on RL are adjusted during flights, achieving the smallest attitude errors, thus significantly improving attitude tracking performance compared to the hand-tuned approach. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2502_04552 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Reinforcement Learning Based Prediction of PID Controller Gains for Quadrotor UAVs Sönmez, Serhat Montecchio, Luca Martini, Simone Rutherford, Matthew J. Rizzo, Alessandro Stefanovic, Margareta Valavanis, Kimon P. Systems and Control Robotics A reinforcement learning (RL) based methodology is proposed and implemented for online fine-tuning of PID controller gains, thus, improving quadrotor effective and accurate trajectory tracking. The RL agent is first trained offline on a quadrotor PID attitude controller and then validated through simulations and experimental flights. RL exploits a Deep Deterministic Policy Gradient (DDPG) algorithm, which is an off-policy actor-critic method. Training and simulation studies are performed using Matlab/Simulink and the UAV Toolbox Support Package for PX4 Autopilots. Performance evaluation and comparison studies are performed between the hand-tuned and RL-based tuned approaches. The results show that the controller parameters based on RL are adjusted during flights, achieving the smallest attitude errors, thus significantly improving attitude tracking performance compared to the hand-tuned approach. |
| title | Reinforcement Learning Based Prediction of PID Controller Gains for Quadrotor UAVs |
| topic | Systems and Control Robotics |
| url | https://arxiv.org/abs/2502.04552 |