Multi-AUV Formation Predictive Control Based on CNN-LSTM under Communication Constraints
For the problem of hydroacoustic communication constraints in multi-AUV leader follower formation, this paper designs a formation control method combining CNN-LSTM prediction and backstepping sliding mode control. First, a feedback linearization method is used to transform the AUV nonlinear model in...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2023-04-01
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| Series: | Journal of Marine Science and Engineering |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2077-1312/11/4/873 |
| _version_ | 1797604792121425920 |
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| author | Juan Li Zhenyang Tian Gengshi Zhang Wenbo Li |
| author_facet | Juan Li Zhenyang Tian Gengshi Zhang Wenbo Li |
| author_sort | Juan Li |
| collection | DOAJ |
| description | For the problem of hydroacoustic communication constraints in multi-AUV leader follower formation, this paper designs a formation control method combining CNN-LSTM prediction and backstepping sliding mode control. First, a feedback linearization method is used to transform the AUV nonlinear model into a second-order integral model; then, the influence of hydroacoustic communication constraints on the multi-AUV formation control problem is analyzed, and a sliding window-based formation prediction control strategy is designed; for the characteristics of AUV motion trajectory with certain temporal order, the CNN-LSTM prediction model is selected to predict the trajectory state of the leader follower and compensate the effect of communication delay on formation control, and combine the backstepping method and sliding mode control to design the formation controller. Finally, the simulation experimental results show that the proposed CNN-LSTM prediction and backstepping sliding mode control can improve the effect of hydroacoustic communication constraints on formation control. |
| first_indexed | 2024-03-11T04:51:46Z |
| format | Article |
| id | doaj.art-2d9e0d962e9a4d58a7aad576032fa288 |
| institution | Directory Open Access Journal |
| issn | 2077-1312 |
| language | English |
| last_indexed | 2024-03-11T04:51:46Z |
| publishDate | 2023-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Journal of Marine Science and Engineering |
| spelling | doaj.art-2d9e0d962e9a4d58a7aad576032fa2882023-11-17T19:57:15ZengMDPI AGJournal of Marine Science and Engineering2077-13122023-04-0111487310.3390/jmse11040873Multi-AUV Formation Predictive Control Based on CNN-LSTM under Communication ConstraintsJuan Li0Zhenyang Tian1Gengshi Zhang2Wenbo Li3Key Laboratory of Underwater Robot Technology, Harbin Engineering University, Harbin 150001, ChinaCollege of Intelligent Systems Science and Engineering, Harbin Engineering University, Harbin 150001, ChinaCollege of Intelligent Systems Science and Engineering, Harbin Engineering University, Harbin 150001, ChinaCollege of Intelligent Systems Science and Engineering, Harbin Engineering University, Harbin 150001, ChinaFor the problem of hydroacoustic communication constraints in multi-AUV leader follower formation, this paper designs a formation control method combining CNN-LSTM prediction and backstepping sliding mode control. First, a feedback linearization method is used to transform the AUV nonlinear model into a second-order integral model; then, the influence of hydroacoustic communication constraints on the multi-AUV formation control problem is analyzed, and a sliding window-based formation prediction control strategy is designed; for the characteristics of AUV motion trajectory with certain temporal order, the CNN-LSTM prediction model is selected to predict the trajectory state of the leader follower and compensate the effect of communication delay on formation control, and combine the backstepping method and sliding mode control to design the formation controller. Finally, the simulation experimental results show that the proposed CNN-LSTM prediction and backstepping sliding mode control can improve the effect of hydroacoustic communication constraints on formation control.https://www.mdpi.com/2077-1312/11/4/873formation controlcommunication constraintsfeedback linearizationCNN-LSTM predictionbackstepping slide control |
| spellingShingle | Juan Li Zhenyang Tian Gengshi Zhang Wenbo Li Multi-AUV Formation Predictive Control Based on CNN-LSTM under Communication Constraints Journal of Marine Science and Engineering formation control communication constraints feedback linearization CNN-LSTM prediction backstepping slide control |
| title | Multi-AUV Formation Predictive Control Based on CNN-LSTM under Communication Constraints |
| title_full | Multi-AUV Formation Predictive Control Based on CNN-LSTM under Communication Constraints |
| title_fullStr | Multi-AUV Formation Predictive Control Based on CNN-LSTM under Communication Constraints |
| title_full_unstemmed | Multi-AUV Formation Predictive Control Based on CNN-LSTM under Communication Constraints |
| title_short | Multi-AUV Formation Predictive Control Based on CNN-LSTM under Communication Constraints |
| title_sort | multi auv formation predictive control based on cnn lstm under communication constraints |
| topic | formation control communication constraints feedback linearization CNN-LSTM prediction backstepping slide control |
| url | https://www.mdpi.com/2077-1312/11/4/873 |
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