Dynamic Cascade Spiking Neural Network Supervisory Controller for a Nonplanar Twelve-Rotor UAV
Unknown variables in the environment, such as wind disturbance during a flight, affect the accurate trajectory of multi-rotor UAVs. This study focuses on the intelligent supervisory neurocontrol of trajectory tracking for a nonplanar twelve-rotor UAV to address this issue. Firstly, a twelve-rotor UA...
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MDPI AG
2025-02-01
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| Online Access: | https://www.mdpi.com/1424-8220/25/4/1177 |
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| author | Cheng Peng Guanyu Qiao Bing Ge |
| author_facet | Cheng Peng Guanyu Qiao Bing Ge |
| author_sort | Cheng Peng |
| collection | DOAJ |
| description | Unknown variables in the environment, such as wind disturbance during a flight, affect the accurate trajectory of multi-rotor UAVs. This study focuses on the intelligent supervisory neurocontrol of trajectory tracking for a nonplanar twelve-rotor UAV to address this issue. Firstly, a twelve-rotor UAV is developed with a nonplanar structure, which makes up for the defects of conventional multi-rotors with weak yaw movement. A characteristic model of the twelve-rotor UAV is devised so as to facilitate intelligent controller design without losing model information. For the purpose of achieving accurate and fast trajectory tracking and strong self-learning ability, an intelligent composite controller combining adaptive sliding-mode feedback control and dynamic cascade spiking neural network (DCSNN) supervisory feedforward control is proposed. The novel dynamic cascade network structure is constructed to better adapt to changing data and unstable environments. The weight learning algorithm and dynamic cascade structure learning algorithm work together to ensure network stability and robustness. Finally, comparative numerical simulations and twelve-rotor UAV prototype experiments verify the superior tracking control performance, even outdoors with wind disturbances. |
| format | Article |
| id | doaj-art-eee8120131fd4d1dbb70e54fcbdfcef8 |
| institution | OA Journals |
| issn | 1424-8220 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | MDPI AG |
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| series | Sensors |
| spelling | doaj-art-eee8120131fd4d1dbb70e54fcbdfcef82025-08-20T02:03:27ZengMDPI AGSensors1424-82202025-02-01254117710.3390/s25041177Dynamic Cascade Spiking Neural Network Supervisory Controller for a Nonplanar Twelve-Rotor UAVCheng Peng0Guanyu Qiao1Bing Ge2Changchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences, Changchun 130033, ChinaChangchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences, Changchun 130033, ChinaChangchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences, Changchun 130033, ChinaUnknown variables in the environment, such as wind disturbance during a flight, affect the accurate trajectory of multi-rotor UAVs. This study focuses on the intelligent supervisory neurocontrol of trajectory tracking for a nonplanar twelve-rotor UAV to address this issue. Firstly, a twelve-rotor UAV is developed with a nonplanar structure, which makes up for the defects of conventional multi-rotors with weak yaw movement. A characteristic model of the twelve-rotor UAV is devised so as to facilitate intelligent controller design without losing model information. For the purpose of achieving accurate and fast trajectory tracking and strong self-learning ability, an intelligent composite controller combining adaptive sliding-mode feedback control and dynamic cascade spiking neural network (DCSNN) supervisory feedforward control is proposed. The novel dynamic cascade network structure is constructed to better adapt to changing data and unstable environments. The weight learning algorithm and dynamic cascade structure learning algorithm work together to ensure network stability and robustness. Finally, comparative numerical simulations and twelve-rotor UAV prototype experiments verify the superior tracking control performance, even outdoors with wind disturbances.https://www.mdpi.com/1424-8220/25/4/1177nonplanar twelve-rotor UAVintelligent composite controllerdynamic cascade spiking neural networksupervisory feedforward control |
| spellingShingle | Cheng Peng Guanyu Qiao Bing Ge Dynamic Cascade Spiking Neural Network Supervisory Controller for a Nonplanar Twelve-Rotor UAV Sensors nonplanar twelve-rotor UAV intelligent composite controller dynamic cascade spiking neural network supervisory feedforward control |
| title | Dynamic Cascade Spiking Neural Network Supervisory Controller for a Nonplanar Twelve-Rotor UAV |
| title_full | Dynamic Cascade Spiking Neural Network Supervisory Controller for a Nonplanar Twelve-Rotor UAV |
| title_fullStr | Dynamic Cascade Spiking Neural Network Supervisory Controller for a Nonplanar Twelve-Rotor UAV |
| title_full_unstemmed | Dynamic Cascade Spiking Neural Network Supervisory Controller for a Nonplanar Twelve-Rotor UAV |
| title_short | Dynamic Cascade Spiking Neural Network Supervisory Controller for a Nonplanar Twelve-Rotor UAV |
| title_sort | dynamic cascade spiking neural network supervisory controller for a nonplanar twelve rotor uav |
| topic | nonplanar twelve-rotor UAV intelligent composite controller dynamic cascade spiking neural network supervisory feedforward control |
| url | https://www.mdpi.com/1424-8220/25/4/1177 |
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