Robust Reinforcement Learning Control Framework for a Quadrotor Unmanned Aerial Vehicle Using Critic Neural Network
This article introduces a novel robust reinforcement learning (RL) control scheme for a quadrotor unmanned aerial vehicle (QUAV) under external disturbances and model uncertainties. First, the translational and rotational motions of the QUAV are decoupled and trained separately to mitigate the compu...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2025-03-01
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| Series: | Advanced Intelligent Systems |
| Subjects: | |
| Online Access: | https://doi.org/10.1002/aisy.202400427 |
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| _version_ | 1849774923341365248 |
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| author | Yu Cai Yefeng Yang Tao Huang Boyang Li |
| author_facet | Yu Cai Yefeng Yang Tao Huang Boyang Li |
| author_sort | Yu Cai |
| collection | DOAJ |
| description | This article introduces a novel robust reinforcement learning (RL) control scheme for a quadrotor unmanned aerial vehicle (QUAV) under external disturbances and model uncertainties. First, the translational and rotational motions of the QUAV are decoupled and trained separately to mitigate the computational complexity of the controller design and training process. Then, the proximal policy optimization algorithm with a dual‐critic structure is proposed to address the overestimation issue and accelerate the convergence speed of RL controllers. Furthermore, a novel reward function and a robust compensator employing a switch value function are proposed to address model uncertainties and external disturbances. At last, simulation results and comparisons demonstrate the effectiveness and robustness of the proposed RL control framework. |
| format | Article |
| id | doaj-art-3a8672f7a53b43e2a913c7967e2300e7 |
| institution | DOAJ |
| issn | 2640-4567 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | Wiley |
| record_format | Article |
| series | Advanced Intelligent Systems |
| spelling | doaj-art-3a8672f7a53b43e2a913c7967e2300e72025-08-20T03:01:35ZengWileyAdvanced Intelligent Systems2640-45672025-03-0173n/an/a10.1002/aisy.202400427Robust Reinforcement Learning Control Framework for a Quadrotor Unmanned Aerial Vehicle Using Critic Neural NetworkYu Cai0Yefeng Yang1Tao Huang2Boyang Li3Center for Control Theory and Guidance Technology Harbin Institute of Technology Harbin 150001 ChinaCenter for Control Theory and Guidance Technology Harbin Institute of Technology Harbin 150001 ChinaCenter for Control Theory and Guidance Technology Harbin Institute of Technology Harbin 150001 ChinaSchool of Engineering The University of Newcastle Callaghan NSW 2308 AustraliaThis article introduces a novel robust reinforcement learning (RL) control scheme for a quadrotor unmanned aerial vehicle (QUAV) under external disturbances and model uncertainties. First, the translational and rotational motions of the QUAV are decoupled and trained separately to mitigate the computational complexity of the controller design and training process. Then, the proximal policy optimization algorithm with a dual‐critic structure is proposed to address the overestimation issue and accelerate the convergence speed of RL controllers. Furthermore, a novel reward function and a robust compensator employing a switch value function are proposed to address model uncertainties and external disturbances. At last, simulation results and comparisons demonstrate the effectiveness and robustness of the proposed RL control framework.https://doi.org/10.1002/aisy.202400427proximal policy optimizationquadrotor unmanned aerial vehiclesreinforcement learning controlrobust compensations |
| spellingShingle | Yu Cai Yefeng Yang Tao Huang Boyang Li Robust Reinforcement Learning Control Framework for a Quadrotor Unmanned Aerial Vehicle Using Critic Neural Network Advanced Intelligent Systems proximal policy optimization quadrotor unmanned aerial vehicles reinforcement learning control robust compensations |
| title | Robust Reinforcement Learning Control Framework for a Quadrotor Unmanned Aerial Vehicle Using Critic Neural Network |
| title_full | Robust Reinforcement Learning Control Framework for a Quadrotor Unmanned Aerial Vehicle Using Critic Neural Network |
| title_fullStr | Robust Reinforcement Learning Control Framework for a Quadrotor Unmanned Aerial Vehicle Using Critic Neural Network |
| title_full_unstemmed | Robust Reinforcement Learning Control Framework for a Quadrotor Unmanned Aerial Vehicle Using Critic Neural Network |
| title_short | Robust Reinforcement Learning Control Framework for a Quadrotor Unmanned Aerial Vehicle Using Critic Neural Network |
| title_sort | robust reinforcement learning control framework for a quadrotor unmanned aerial vehicle using critic neural network |
| topic | proximal policy optimization quadrotor unmanned aerial vehicles reinforcement learning control robust compensations |
| url | https://doi.org/10.1002/aisy.202400427 |
| work_keys_str_mv | AT yucai robustreinforcementlearningcontrolframeworkforaquadrotorunmannedaerialvehicleusingcriticneuralnetwork AT yefengyang robustreinforcementlearningcontrolframeworkforaquadrotorunmannedaerialvehicleusingcriticneuralnetwork AT taohuang robustreinforcementlearningcontrolframeworkforaquadrotorunmannedaerialvehicleusingcriticneuralnetwork AT boyangli robustreinforcementlearningcontrolframeworkforaquadrotorunmannedaerialvehicleusingcriticneuralnetwork |