Deep Reinforcement Learning Based Active Disturbance Rejection Control for ROV Position and Attitude Control
Remotely operated vehicles (ROVs) face challenges in achieving optimal trajectory tracking performance during underwater movement due to external disturbances and parameter uncertainties. To address this issue, this paper proposes a position and attitude control strategy for underwater robots based...
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| Format: | Article |
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MDPI AG
2025-04-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/15/8/4443 |
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| author | Gaosheng Luo Dong Zhang Wei Feng Zhe Jiang Xingchen Liu |
| author_facet | Gaosheng Luo Dong Zhang Wei Feng Zhe Jiang Xingchen Liu |
| author_sort | Gaosheng Luo |
| collection | DOAJ |
| description | Remotely operated vehicles (ROVs) face challenges in achieving optimal trajectory tracking performance during underwater movement due to external disturbances and parameter uncertainties. To address this issue, this paper proposes a position and attitude control strategy for underwater robots based on a reinforcement learning active disturbance rejection controller. The linear active disturbance rejection controller has achieved satisfactory results in the field of underwater robot control. However, fixed-parameter controllers cannot achieve optimal control performance for the controlled object. Therefore, further exploration of the adaptive capability of control parameters based on the linear active disturbance rejection controller was conducted. The deep deterministic policy gradient (DDPG) algorithm was used to optimize the linear extended state observer (LESO). This strategy employs deep neural networks to adjust the LESO parameters online based on measured states, allowing for more accurate estimation of model uncertainties and environmental disturbances, and compensating the total disturbance into the control input online, resulting in better disturbance estimation and control performance. Simulation results show that the proposed control scheme, compared to PID and fixed parameter LADRC, as well as the double closed-loop sliding mode control method based on nonlinear observers (NESO-DSMC), significantly improves the disturbance estimation accuracy of the linear active disturbance rejection controller, leading to higher control precision and stronger robustness, thus demonstrating the effectiveness of the proposed control strategy. |
| format | Article |
| id | doaj-art-d11b2bc7daa04d12a153a54d20fb23f9 |
| institution | DOAJ |
| issn | 2076-3417 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj-art-d11b2bc7daa04d12a153a54d20fb23f92025-08-20T03:14:16ZengMDPI AGApplied Sciences2076-34172025-04-01158444310.3390/app15084443Deep Reinforcement Learning Based Active Disturbance Rejection Control for ROV Position and Attitude ControlGaosheng Luo0Dong Zhang1Wei Feng2Zhe Jiang3Xingchen Liu4Shanghai Engineering Research Center of Hadal Science and Technology, College of Engineering Science and Technology, Shanghai Ocean University, Shanghai 201306, ChinaShanghai Engineering Research Center of Hadal Science and Technology, College of Engineering Science and Technology, Shanghai Ocean University, Shanghai 201306, ChinaShenzhen Institute for Advanced Study, University of Electronic Science and Technology of China, Shenzhen 518110, ChinaShanghai Engineering Research Center of Hadal Science and Technology, College of Engineering Science and Technology, Shanghai Ocean University, Shanghai 201306, ChinaShanghai Engineering Research Center of Hadal Science and Technology, College of Engineering Science and Technology, Shanghai Ocean University, Shanghai 201306, ChinaRemotely operated vehicles (ROVs) face challenges in achieving optimal trajectory tracking performance during underwater movement due to external disturbances and parameter uncertainties. To address this issue, this paper proposes a position and attitude control strategy for underwater robots based on a reinforcement learning active disturbance rejection controller. The linear active disturbance rejection controller has achieved satisfactory results in the field of underwater robot control. However, fixed-parameter controllers cannot achieve optimal control performance for the controlled object. Therefore, further exploration of the adaptive capability of control parameters based on the linear active disturbance rejection controller was conducted. The deep deterministic policy gradient (DDPG) algorithm was used to optimize the linear extended state observer (LESO). This strategy employs deep neural networks to adjust the LESO parameters online based on measured states, allowing for more accurate estimation of model uncertainties and environmental disturbances, and compensating the total disturbance into the control input online, resulting in better disturbance estimation and control performance. Simulation results show that the proposed control scheme, compared to PID and fixed parameter LADRC, as well as the double closed-loop sliding mode control method based on nonlinear observers (NESO-DSMC), significantly improves the disturbance estimation accuracy of the linear active disturbance rejection controller, leading to higher control precision and stronger robustness, thus demonstrating the effectiveness of the proposed control strategy.https://www.mdpi.com/2076-3417/15/8/4443ROVlinear active disturbance rejection controldeep reinforcement learningdeep deterministic policy gradient (DDPG) algorithmresilience to disturbance |
| spellingShingle | Gaosheng Luo Dong Zhang Wei Feng Zhe Jiang Xingchen Liu Deep Reinforcement Learning Based Active Disturbance Rejection Control for ROV Position and Attitude Control Applied Sciences ROV linear active disturbance rejection control deep reinforcement learning deep deterministic policy gradient (DDPG) algorithm resilience to disturbance |
| title | Deep Reinforcement Learning Based Active Disturbance Rejection Control for ROV Position and Attitude Control |
| title_full | Deep Reinforcement Learning Based Active Disturbance Rejection Control for ROV Position and Attitude Control |
| title_fullStr | Deep Reinforcement Learning Based Active Disturbance Rejection Control for ROV Position and Attitude Control |
| title_full_unstemmed | Deep Reinforcement Learning Based Active Disturbance Rejection Control for ROV Position and Attitude Control |
| title_short | Deep Reinforcement Learning Based Active Disturbance Rejection Control for ROV Position and Attitude Control |
| title_sort | deep reinforcement learning based active disturbance rejection control for rov position and attitude control |
| topic | ROV linear active disturbance rejection control deep reinforcement learning deep deterministic policy gradient (DDPG) algorithm resilience to disturbance |
| url | https://www.mdpi.com/2076-3417/15/8/4443 |
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