ADHDP-based robust self-learning 3D trajectory tracking control for underactuated UUVs
In this work, we propose a robust self-learning control scheme based on action-dependent heuristic dynamic programming (ADHDP) to tackle the 3D trajectory tracking control problem of underactuated uncrewed underwater vehicles (UUVs) with uncertain dynamics and time-varying ocean disturbances. Initia...
Saved in:
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
PeerJ Inc.
2024-12-01
|
| Series: | PeerJ Computer Science |
| Subjects: | |
| Online Access: | https://peerj.com/articles/cs-2605.pdf |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1846126674566971392 |
|---|---|
| author | Chunbo Zhao Huaran Yan Deyi Gao |
| author_facet | Chunbo Zhao Huaran Yan Deyi Gao |
| author_sort | Chunbo Zhao |
| collection | DOAJ |
| description | In this work, we propose a robust self-learning control scheme based on action-dependent heuristic dynamic programming (ADHDP) to tackle the 3D trajectory tracking control problem of underactuated uncrewed underwater vehicles (UUVs) with uncertain dynamics and time-varying ocean disturbances. Initially, the radial basis function neural network is introduced to convert the compound uncertain element, comprising uncertain dynamics and time-varying ocean disturbances, into a linear parametric form with just one unknown parameter. Then, to improve the tracking performance of the UUVs trajectory tracking closed-loop control system, an actor-critic neural network structure based on ADHDP technology is introduced to adaptively adjust the weights of the action-critic network, optimizing the performance index function. Finally, an ADHDP-based robust self-learning control scheme is constructed, which makes the UUVs closed-loop system have good robustness and control performance. The theoretical analysis demonstrates that all signals in the UUVs trajectory tracking closed-loop control system are bounded. The simulation results for the UUVs validate the effectiveness of the proposed control scheme. |
| format | Article |
| id | doaj-art-c608b069dc1c4f0aaf497cf9e9e3b1a8 |
| institution | Kabale University |
| issn | 2376-5992 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | PeerJ Inc. |
| record_format | Article |
| series | PeerJ Computer Science |
| spelling | doaj-art-c608b069dc1c4f0aaf497cf9e9e3b1a82024-12-12T15:05:20ZengPeerJ Inc.PeerJ Computer Science2376-59922024-12-0110e260510.7717/peerj-cs.2605ADHDP-based robust self-learning 3D trajectory tracking control for underactuated UUVsChunbo ZhaoHuaran YanDeyi GaoIn this work, we propose a robust self-learning control scheme based on action-dependent heuristic dynamic programming (ADHDP) to tackle the 3D trajectory tracking control problem of underactuated uncrewed underwater vehicles (UUVs) with uncertain dynamics and time-varying ocean disturbances. Initially, the radial basis function neural network is introduced to convert the compound uncertain element, comprising uncertain dynamics and time-varying ocean disturbances, into a linear parametric form with just one unknown parameter. Then, to improve the tracking performance of the UUVs trajectory tracking closed-loop control system, an actor-critic neural network structure based on ADHDP technology is introduced to adaptively adjust the weights of the action-critic network, optimizing the performance index function. Finally, an ADHDP-based robust self-learning control scheme is constructed, which makes the UUVs closed-loop system have good robustness and control performance. The theoretical analysis demonstrates that all signals in the UUVs trajectory tracking closed-loop control system are bounded. The simulation results for the UUVs validate the effectiveness of the proposed control scheme.https://peerj.com/articles/cs-2605.pdfUnmanned underactuated vehicles (UUVs)Robust adaptive controlTrajectory trackingAction-dependent heuristic dynamic programming (ADHDP) |
| spellingShingle | Chunbo Zhao Huaran Yan Deyi Gao ADHDP-based robust self-learning 3D trajectory tracking control for underactuated UUVs PeerJ Computer Science Unmanned underactuated vehicles (UUVs) Robust adaptive control Trajectory tracking Action-dependent heuristic dynamic programming (ADHDP) |
| title | ADHDP-based robust self-learning 3D trajectory tracking control for underactuated UUVs |
| title_full | ADHDP-based robust self-learning 3D trajectory tracking control for underactuated UUVs |
| title_fullStr | ADHDP-based robust self-learning 3D trajectory tracking control for underactuated UUVs |
| title_full_unstemmed | ADHDP-based robust self-learning 3D trajectory tracking control for underactuated UUVs |
| title_short | ADHDP-based robust self-learning 3D trajectory tracking control for underactuated UUVs |
| title_sort | adhdp based robust self learning 3d trajectory tracking control for underactuated uuvs |
| topic | Unmanned underactuated vehicles (UUVs) Robust adaptive control Trajectory tracking Action-dependent heuristic dynamic programming (ADHDP) |
| url | https://peerj.com/articles/cs-2605.pdf |
| work_keys_str_mv | AT chunbozhao adhdpbasedrobustselflearning3dtrajectorytrackingcontrolforunderactuateduuvs AT huaranyan adhdpbasedrobustselflearning3dtrajectorytrackingcontrolforunderactuateduuvs AT deyigao adhdpbasedrobustselflearning3dtrajectorytrackingcontrolforunderactuateduuvs |