Leader-Follower UAV formation flight control based on feature modelling
To solve the problems of backstepping error and poor dynamic tracking approach rate in traditional PID neural network control in UAV formation flight control, a Leader-Follower UAV formation flight control method based on feature modelling is proposed,and the pose relationship model between virtual...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
Taylor & Francis Group
2023-12-01
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| Series: | Systems Science & Control Engineering |
| Subjects: | |
| Online Access: | https://www.tandfonline.com/doi/10.1080/21642583.2023.2268153 |
| _version_ | 1827629770570989568 |
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| author | Yafei Chen Tao Deng |
| author_facet | Yafei Chen Tao Deng |
| author_sort | Yafei Chen |
| collection | DOAJ |
| description | To solve the problems of backstepping error and poor dynamic tracking approach rate in traditional PID neural network control in UAV formation flight control, a Leader-Follower UAV formation flight control method based on feature modelling is proposed,and the pose relationship model between virtual follower and pilot is established by trajectory tracking and pose dynamic fitting. The pose distribution of thefollower is analyzed in the ground coordinate system, and the parameter information of linear velocity and angular velocity control of UAV is obtained, and the backstepping sliding mode formation controller is formed. The variable structure PID neural network controller is used to design the flight control law of UAV formation, and the fast piecewise power approaching factor is introduced into the PID controller to eliminate the chattering of sliding mode control. The simulation results show that this method can ensure the rapidity of UAV formation flight control also show strong anti-jamming ability. Due to the fast piecewise power approach rate, the UAVs can complete the UAV formation reorganization under disturbance and buffeting in a short time, and the trajectory tracking error approaches zero, and it has good anti-buffeting ability. |
| first_indexed | 2024-03-09T13:57:07Z |
| format | Article |
| id | doaj.art-7a08ef23b7c84307bdbf4a688875dcbf |
| institution | Directory Open Access Journal |
| issn | 2164-2583 |
| language | English |
| last_indexed | 2024-03-09T13:57:07Z |
| publishDate | 2023-12-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | Systems Science & Control Engineering |
| spelling | doaj.art-7a08ef23b7c84307bdbf4a688875dcbf2023-11-30T12:45:32ZengTaylor & Francis GroupSystems Science & Control Engineering2164-25832023-12-0111110.1080/21642583.2023.2268153Leader-Follower UAV formation flight control based on feature modellingYafei Chen0Tao Deng1School of Aeronautics, Chongqing Jiaotong University, Chongqing, People’s Republic of ChinaSchool of Aeronautics, Chongqing Jiaotong University, Chongqing, People’s Republic of ChinaTo solve the problems of backstepping error and poor dynamic tracking approach rate in traditional PID neural network control in UAV formation flight control, a Leader-Follower UAV formation flight control method based on feature modelling is proposed,and the pose relationship model between virtual follower and pilot is established by trajectory tracking and pose dynamic fitting. The pose distribution of thefollower is analyzed in the ground coordinate system, and the parameter information of linear velocity and angular velocity control of UAV is obtained, and the backstepping sliding mode formation controller is formed. The variable structure PID neural network controller is used to design the flight control law of UAV formation, and the fast piecewise power approaching factor is introduced into the PID controller to eliminate the chattering of sliding mode control. The simulation results show that this method can ensure the rapidity of UAV formation flight control also show strong anti-jamming ability. Due to the fast piecewise power approach rate, the UAVs can complete the UAV formation reorganization under disturbance and buffeting in a short time, and the trajectory tracking error approaches zero, and it has good anti-buffeting ability.https://www.tandfonline.com/doi/10.1080/21642583.2023.2268153Feature modellingLeader-Followerdroneformation flight controlposebuffeting |
| spellingShingle | Yafei Chen Tao Deng Leader-Follower UAV formation flight control based on feature modelling Systems Science & Control Engineering Feature modelling Leader-Follower drone formation flight control pose buffeting |
| title | Leader-Follower UAV formation flight control based on feature modelling |
| title_full | Leader-Follower UAV formation flight control based on feature modelling |
| title_fullStr | Leader-Follower UAV formation flight control based on feature modelling |
| title_full_unstemmed | Leader-Follower UAV formation flight control based on feature modelling |
| title_short | Leader-Follower UAV formation flight control based on feature modelling |
| title_sort | leader follower uav formation flight control based on feature modelling |
| topic | Feature modelling Leader-Follower drone formation flight control pose buffeting |
| url | https://www.tandfonline.com/doi/10.1080/21642583.2023.2268153 |
| work_keys_str_mv | AT yafeichen leaderfolloweruavformationflightcontrolbasedonfeaturemodelling AT taodeng leaderfolloweruavformationflightcontrolbasedonfeaturemodelling |