Design of Ice Tolerance Flight Envelope Protection Control System for UAV Based on LSTM Neural Network for Detecting Icing Severity
Icing on an unmanned aerial vehicle (UAV) can degrade aerodynamic performance, reduce flight capabilities, impair maneuverability and stability, and significantly impact flight safety. At present, most flight control methods for icing-affected aircraft adopt a conservative control strategy, in which...
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MDPI AG
2025-01-01
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| Series: | Drones |
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| Online Access: | https://www.mdpi.com/2504-446X/9/1/63 |
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| author | Ting Yue Xianlong Wang Bo Wang Shang Tai Hailiang Liu Lixin Wang Feihong Jiang |
| author_facet | Ting Yue Xianlong Wang Bo Wang Shang Tai Hailiang Liu Lixin Wang Feihong Jiang |
| author_sort | Ting Yue |
| collection | DOAJ |
| description | Icing on an unmanned aerial vehicle (UAV) can degrade aerodynamic performance, reduce flight capabilities, impair maneuverability and stability, and significantly impact flight safety. At present, most flight control methods for icing-affected aircraft adopt a conservative control strategy, in which small control inputs are used to keep the aircraft’s angle of attack and other state variables within a limited range. However, this approach restricts the flight performance of icing aircraft. To address this issue, this paper innovatively proposes a design method of an ice tolerance flight envelope protection control system for a UAV on the base of icing severity detection using a long short-term memory (LSTM) neural network. First, the icing severity is detected using an LSTM neural network without requiring control surface excitation. It relies solely on the aircraft’s historical flight data to detect the icing severity. Second, by modifying the fuzzy risk level boundaries of the icing aircraft flight parameters, a nonlinear mapping relationship is established between the tracking command risk level, the UAV flight control command magnitude, and the icing severity. This provides a safe range of tracking commands for guiding the aircraft out of the icing region. Finally, the ice tolerance flight envelope protection control law is developed, using a nonlinear dynamic inverse controller (NDIC) as the inner loop and a nonlinear model predictive controller (NMPC) as the outer loop. This approach ensures boundary protection for state variables such as the angle of attack and roll angle while simultaneously enhancing the robustness of the flight control system. The effectiveness and superiority of the method proposed in this paper are verified for the example aircraft through mathematical simulation. |
| format | Article |
| id | doaj-art-06b5fa422da646f48168bf149524fb33 |
| institution | Kabale University |
| issn | 2504-446X |
| language | English |
| publishDate | 2025-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Drones |
| spelling | doaj-art-06b5fa422da646f48168bf149524fb332025-01-24T13:29:50ZengMDPI AGDrones2504-446X2025-01-01916310.3390/drones9010063Design of Ice Tolerance Flight Envelope Protection Control System for UAV Based on LSTM Neural Network for Detecting Icing SeverityTing Yue0Xianlong Wang1Bo Wang2Shang Tai3Hailiang Liu4Lixin Wang5Feihong Jiang6School of Aeronautic Science and Engineering, Beihang University, Beijing 100191, ChinaSchool of Aeronautic Science and Engineering, Beihang University, Beijing 100191, ChinaFlight Control Division, AVIC The First Aircraft Institute, Xi’an 710089, ChinaSchool of Aeronautic Science and Engineering, Beihang University, Beijing 100191, ChinaSchool of Aeronautic Science and Engineering, Beihang University, Beijing 100191, ChinaSchool of Aeronautic Science and Engineering, Beihang University, Beijing 100191, ChinaFlight Control Division, AVIC The First Aircraft Institute, Xi’an 710089, ChinaIcing on an unmanned aerial vehicle (UAV) can degrade aerodynamic performance, reduce flight capabilities, impair maneuverability and stability, and significantly impact flight safety. At present, most flight control methods for icing-affected aircraft adopt a conservative control strategy, in which small control inputs are used to keep the aircraft’s angle of attack and other state variables within a limited range. However, this approach restricts the flight performance of icing aircraft. To address this issue, this paper innovatively proposes a design method of an ice tolerance flight envelope protection control system for a UAV on the base of icing severity detection using a long short-term memory (LSTM) neural network. First, the icing severity is detected using an LSTM neural network without requiring control surface excitation. It relies solely on the aircraft’s historical flight data to detect the icing severity. Second, by modifying the fuzzy risk level boundaries of the icing aircraft flight parameters, a nonlinear mapping relationship is established between the tracking command risk level, the UAV flight control command magnitude, and the icing severity. This provides a safe range of tracking commands for guiding the aircraft out of the icing region. Finally, the ice tolerance flight envelope protection control law is developed, using a nonlinear dynamic inverse controller (NDIC) as the inner loop and a nonlinear model predictive controller (NMPC) as the outer loop. This approach ensures boundary protection for state variables such as the angle of attack and roll angle while simultaneously enhancing the robustness of the flight control system. The effectiveness and superiority of the method proposed in this paper are verified for the example aircraft through mathematical simulation.https://www.mdpi.com/2504-446X/9/1/63aircraft icingicing severity detectionflight risk level assessmentLSTMflight envelope protection controlNMPC |
| spellingShingle | Ting Yue Xianlong Wang Bo Wang Shang Tai Hailiang Liu Lixin Wang Feihong Jiang Design of Ice Tolerance Flight Envelope Protection Control System for UAV Based on LSTM Neural Network for Detecting Icing Severity Drones aircraft icing icing severity detection flight risk level assessment LSTM flight envelope protection control NMPC |
| title | Design of Ice Tolerance Flight Envelope Protection Control System for UAV Based on LSTM Neural Network for Detecting Icing Severity |
| title_full | Design of Ice Tolerance Flight Envelope Protection Control System for UAV Based on LSTM Neural Network for Detecting Icing Severity |
| title_fullStr | Design of Ice Tolerance Flight Envelope Protection Control System for UAV Based on LSTM Neural Network for Detecting Icing Severity |
| title_full_unstemmed | Design of Ice Tolerance Flight Envelope Protection Control System for UAV Based on LSTM Neural Network for Detecting Icing Severity |
| title_short | Design of Ice Tolerance Flight Envelope Protection Control System for UAV Based on LSTM Neural Network for Detecting Icing Severity |
| title_sort | design of ice tolerance flight envelope protection control system for uav based on lstm neural network for detecting icing severity |
| topic | aircraft icing icing severity detection flight risk level assessment LSTM flight envelope protection control NMPC |
| url | https://www.mdpi.com/2504-446X/9/1/63 |
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