Neural Network and Dynamic Inversion Based Adaptive Control for a HALE-UAV against Icing Effects
In the past few decades, in-flight icing has become a common problem for many missions, potentially leading to a reduction in control effectiveness and flight stability, which would threaten flight safety. One of the most popular methods to address this problem is adaptive control. This paper establ...
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2023-04-01
|
| Series: | Drones |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2504-446X/7/4/273 |
| _version_ | 1797605741720240128 |
|---|---|
| author | Yiyang Li Lingquan Cheng Jiayi Yuan Jianliang Ai Yiqun Dong |
| author_facet | Yiyang Li Lingquan Cheng Jiayi Yuan Jianliang Ai Yiqun Dong |
| author_sort | Yiyang Li |
| collection | DOAJ |
| description | In the past few decades, in-flight icing has become a common problem for many missions, potentially leading to a reduction in control effectiveness and flight stability, which would threaten flight safety. One of the most popular methods to address this problem is adaptive control. This paper establishes a dynamic model of an iced high-altitude long-endurance unmanned aerial vehicle (HALE-UAV) with disturbance and measurement noise. Then, by combining multilayer perceptrons (MLP) with a nonlinear dynamic inversion (NDI) controller, we propose an MLP-NDI controller to compensate for online inversion errors and provide a brief proof of control stability. Two experiments were conducted: on one hand, we compared the MLP-NDI controller with other typical controllers; on the other hand, we evaluated its robustness and adaptiveness under different icing conditions. Results indicate that the MLP-NDI controller outperforms other typical controllers with higher tracking accuracy and exhibits strong robustness in the presence of icing errors and measurement noise, which has huge potential to ensure flight safety. |
| first_indexed | 2024-03-11T05:05:22Z |
| format | Article |
| id | doaj.art-bfe82bc4bcd44d9286cf477874b9b1c1 |
| institution | Directory Open Access Journal |
| issn | 2504-446X |
| language | English |
| last_indexed | 2024-03-11T05:05:22Z |
| publishDate | 2023-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Drones |
| spelling | doaj.art-bfe82bc4bcd44d9286cf477874b9b1c12023-11-17T18:58:24ZengMDPI AGDrones2504-446X2023-04-017427310.3390/drones7040273Neural Network and Dynamic Inversion Based Adaptive Control for a HALE-UAV against Icing EffectsYiyang Li0Lingquan Cheng1Jiayi Yuan2Jianliang Ai3Yiqun Dong4Department of Aeronautics and Astronautics, Fudan University, Shanghai 200433, ChinaDepartment of Aeronautics and Astronautics, Fudan University, Shanghai 200433, ChinaDepartment of Aeronautics and Astronautics, Fudan University, Shanghai 200433, ChinaDepartment of Aeronautics and Astronautics, Fudan University, Shanghai 200433, ChinaDepartment of Aeronautics and Astronautics, Fudan University, Shanghai 200433, ChinaIn the past few decades, in-flight icing has become a common problem for many missions, potentially leading to a reduction in control effectiveness and flight stability, which would threaten flight safety. One of the most popular methods to address this problem is adaptive control. This paper establishes a dynamic model of an iced high-altitude long-endurance unmanned aerial vehicle (HALE-UAV) with disturbance and measurement noise. Then, by combining multilayer perceptrons (MLP) with a nonlinear dynamic inversion (NDI) controller, we propose an MLP-NDI controller to compensate for online inversion errors and provide a brief proof of control stability. Two experiments were conducted: on one hand, we compared the MLP-NDI controller with other typical controllers; on the other hand, we evaluated its robustness and adaptiveness under different icing conditions. Results indicate that the MLP-NDI controller outperforms other typical controllers with higher tracking accuracy and exhibits strong robustness in the presence of icing errors and measurement noise, which has huge potential to ensure flight safety.https://www.mdpi.com/2504-446X/7/4/273nonlinear and adaptive flight controllersmodeling of icing UAVsmultilayer perceptronscomparison simulation |
| spellingShingle | Yiyang Li Lingquan Cheng Jiayi Yuan Jianliang Ai Yiqun Dong Neural Network and Dynamic Inversion Based Adaptive Control for a HALE-UAV against Icing Effects Drones nonlinear and adaptive flight controllers modeling of icing UAVs multilayer perceptrons comparison simulation |
| title | Neural Network and Dynamic Inversion Based Adaptive Control for a HALE-UAV against Icing Effects |
| title_full | Neural Network and Dynamic Inversion Based Adaptive Control for a HALE-UAV against Icing Effects |
| title_fullStr | Neural Network and Dynamic Inversion Based Adaptive Control for a HALE-UAV against Icing Effects |
| title_full_unstemmed | Neural Network and Dynamic Inversion Based Adaptive Control for a HALE-UAV against Icing Effects |
| title_short | Neural Network and Dynamic Inversion Based Adaptive Control for a HALE-UAV against Icing Effects |
| title_sort | neural network and dynamic inversion based adaptive control for a hale uav against icing effects |
| topic | nonlinear and adaptive flight controllers modeling of icing UAVs multilayer perceptrons comparison simulation |
| url | https://www.mdpi.com/2504-446X/7/4/273 |
| work_keys_str_mv | AT yiyangli neuralnetworkanddynamicinversionbasedadaptivecontrolforahaleuavagainsticingeffects AT lingquancheng neuralnetworkanddynamicinversionbasedadaptivecontrolforahaleuavagainsticingeffects AT jiayiyuan neuralnetworkanddynamicinversionbasedadaptivecontrolforahaleuavagainsticingeffects AT jianliangai neuralnetworkanddynamicinversionbasedadaptivecontrolforahaleuavagainsticingeffects AT yiqundong neuralnetworkanddynamicinversionbasedadaptivecontrolforahaleuavagainsticingeffects |