Position Control of Quadrotor UAV Based on Cascade Fuzzy Neural Network
In this article, a cascade fuzzy neural network (FNN) control approach is proposed for position control of quadrotor unmanned aerial vehicle (UAV) system with high coupling and underactuated. For the attitude loop with limited range, the FNN controller parameters were trained offline using flight da...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-02-01
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| Series: | Energies |
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| Online Access: | https://www.mdpi.com/1996-1073/15/5/1763 |
| _version_ | 1797475204511825920 |
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| author | Jinjun Rao Bo Li Zhen Zhang Dongdong Chen Wojciech Giernacki |
| author_facet | Jinjun Rao Bo Li Zhen Zhang Dongdong Chen Wojciech Giernacki |
| author_sort | Jinjun Rao |
| collection | DOAJ |
| description | In this article, a cascade fuzzy neural network (FNN) control approach is proposed for position control of quadrotor unmanned aerial vehicle (UAV) system with high coupling and underactuated. For the attitude loop with limited range, the FNN controller parameters were trained offline using flight data, whereas for the position loop, the method based on FNN compensation proportional-integral-derivative (PID) was adopted to tune the system online adaptively. This method not only combined the advantages of fuzzy systems and neural networks but also reduced the amount of calculation for cascade neural network control. Simulations of fixed set point flight and spiral and square trajectory tracking flight were then conducted. The comparison of the results showed that our method had advantages in terms of minimizing overshoot and settling time. Finally, flight experiments were carried out on a DJI Tello quadrotor UAV. The experimental results showed that the proposed controller had good performance in position control. |
| first_indexed | 2024-03-09T20:41:52Z |
| format | Article |
| id | doaj.art-6d75053712e04e1e84c73fcd9fa92192 |
| institution | Directory Open Access Journal |
| issn | 1996-1073 |
| language | English |
| last_indexed | 2024-03-09T20:41:52Z |
| publishDate | 2022-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Energies |
| spelling | doaj.art-6d75053712e04e1e84c73fcd9fa921922023-11-23T22:57:08ZengMDPI AGEnergies1996-10732022-02-01155176310.3390/en15051763Position Control of Quadrotor UAV Based on Cascade Fuzzy Neural NetworkJinjun Rao0Bo Li1Zhen Zhang2Dongdong Chen3Wojciech Giernacki4Shanghai Key Laboratory of Intelligent Manufacturing and Robotics, School of Mechatronic Engineering and Automation, Shanghai University, Shanghai 200444, ChinaShanghai Key Laboratory of Intelligent Manufacturing and Robotics, School of Mechatronic Engineering and Automation, Shanghai University, Shanghai 200444, ChinaShanghai Key Laboratory of Intelligent Manufacturing and Robotics, School of Mechatronic Engineering and Automation, Shanghai University, Shanghai 200444, ChinaShanghai Key Laboratory of Intelligent Manufacturing and Robotics, School of Mechatronic Engineering and Automation, Shanghai University, Shanghai 200444, ChinaFaculty of Control, Institute of Robotics and Machine Intelligence, Robotics and Electrical Engineering, Poznan University of Technology, Piotrowo 3A, 60-965 Poznan, PolandIn this article, a cascade fuzzy neural network (FNN) control approach is proposed for position control of quadrotor unmanned aerial vehicle (UAV) system with high coupling and underactuated. For the attitude loop with limited range, the FNN controller parameters were trained offline using flight data, whereas for the position loop, the method based on FNN compensation proportional-integral-derivative (PID) was adopted to tune the system online adaptively. This method not only combined the advantages of fuzzy systems and neural networks but also reduced the amount of calculation for cascade neural network control. Simulations of fixed set point flight and spiral and square trajectory tracking flight were then conducted. The comparison of the results showed that our method had advantages in terms of minimizing overshoot and settling time. Finally, flight experiments were carried out on a DJI Tello quadrotor UAV. The experimental results showed that the proposed controller had good performance in position control.https://www.mdpi.com/1996-1073/15/5/1763position controlfuzzy neural networkcascade controltrajectory trackingUAV |
| spellingShingle | Jinjun Rao Bo Li Zhen Zhang Dongdong Chen Wojciech Giernacki Position Control of Quadrotor UAV Based on Cascade Fuzzy Neural Network Energies position control fuzzy neural network cascade control trajectory tracking UAV |
| title | Position Control of Quadrotor UAV Based on Cascade Fuzzy Neural Network |
| title_full | Position Control of Quadrotor UAV Based on Cascade Fuzzy Neural Network |
| title_fullStr | Position Control of Quadrotor UAV Based on Cascade Fuzzy Neural Network |
| title_full_unstemmed | Position Control of Quadrotor UAV Based on Cascade Fuzzy Neural Network |
| title_short | Position Control of Quadrotor UAV Based on Cascade Fuzzy Neural Network |
| title_sort | position control of quadrotor uav based on cascade fuzzy neural network |
| topic | position control fuzzy neural network cascade control trajectory tracking UAV |
| url | https://www.mdpi.com/1996-1073/15/5/1763 |
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