Modeling and Simulation Analysis of Bionic Flapping-Wing Flight Attitude Control Based on L1 Adaptive
Flapping-wing flight control is a multi-input and multi-output nonlinear system with uncertainties, which is affected by modeling errors, parameter variations, external disturbances, and unmodeled dynamics. Parameter uncertainty has a great impact on the stability control of flapping-wing flight, an...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2023-01-01
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| Series: | Complexity |
| Online Access: | http://dx.doi.org/10.1155/2023/9066127 |
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| _version_ | 1849396297301229568 |
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| author | Lun Li Fan Bai Wencheng Wang Xiaojin Wu Yihua Dong |
| author_facet | Lun Li Fan Bai Wencheng Wang Xiaojin Wu Yihua Dong |
| author_sort | Lun Li |
| collection | DOAJ |
| description | Flapping-wing flight control is a multi-input and multi-output nonlinear system with uncertainties, which is affected by modeling errors, parameter variations, external disturbances, and unmodeled dynamics. Parameter uncertainty has a great impact on the stability control of flapping-wing flight, and gain adjustment is a common means to deal with parameter uncertainty, but it is complex and time-consuming. Based on the mechanism of flapping-wing flight, a nonlinear dynamic model for flapping-wing dynamic flight is established by analyzing the forces, moments, and attitude changes of the fuselage and wing in detail. Based on the constructed dynamic model, a fast robust adaptive flapping-wing flight control method is proposed. The state predictor is designed to estimate and monitor the uncertain parameters in the flapping-wing attitude control model, and the adaptive law adjusts the parameter estimation to ensure that the output error between the state predictor and the controlled object is stable in the Lyapunov sense, and finally the adaptive control law is obtained. At the same time, the Monte Carlo-support vector machine method is used to optimize the boundary of the control parameters in the flight control to obtain the control parameters that can meet the control expectations, and the obtained parameters are classified and judged according to the stable level flight conditions. Based on the adjusted parameters and the predetermined control signal, the control amount is adjusted according to the control law. When the adaptive gain is large enough, the simulation results show that the system has good transient response characteristics. |
| format | Article |
| id | doaj-art-b4ef6d8c59d74685bf15d080a07411d5 |
| institution | Kabale University |
| issn | 1099-0526 |
| language | English |
| publishDate | 2023-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Complexity |
| spelling | doaj-art-b4ef6d8c59d74685bf15d080a07411d52025-08-20T03:39:22ZengWileyComplexity1099-05262023-01-01202310.1155/2023/9066127Modeling and Simulation Analysis of Bionic Flapping-Wing Flight Attitude Control Based on L1 AdaptiveLun Li0Fan Bai1Wencheng Wang2Xiaojin Wu3Yihua Dong4Institute of Machinery and AutomationSchool of Equipment EngineeringInstitute of Machinery and AutomationInstitute of Machinery and AutomationInstitute of Machinery and AutomationFlapping-wing flight control is a multi-input and multi-output nonlinear system with uncertainties, which is affected by modeling errors, parameter variations, external disturbances, and unmodeled dynamics. Parameter uncertainty has a great impact on the stability control of flapping-wing flight, and gain adjustment is a common means to deal with parameter uncertainty, but it is complex and time-consuming. Based on the mechanism of flapping-wing flight, a nonlinear dynamic model for flapping-wing dynamic flight is established by analyzing the forces, moments, and attitude changes of the fuselage and wing in detail. Based on the constructed dynamic model, a fast robust adaptive flapping-wing flight control method is proposed. The state predictor is designed to estimate and monitor the uncertain parameters in the flapping-wing attitude control model, and the adaptive law adjusts the parameter estimation to ensure that the output error between the state predictor and the controlled object is stable in the Lyapunov sense, and finally the adaptive control law is obtained. At the same time, the Monte Carlo-support vector machine method is used to optimize the boundary of the control parameters in the flight control to obtain the control parameters that can meet the control expectations, and the obtained parameters are classified and judged according to the stable level flight conditions. Based on the adjusted parameters and the predetermined control signal, the control amount is adjusted according to the control law. When the adaptive gain is large enough, the simulation results show that the system has good transient response characteristics.http://dx.doi.org/10.1155/2023/9066127 |
| spellingShingle | Lun Li Fan Bai Wencheng Wang Xiaojin Wu Yihua Dong Modeling and Simulation Analysis of Bionic Flapping-Wing Flight Attitude Control Based on L1 Adaptive Complexity |
| title | Modeling and Simulation Analysis of Bionic Flapping-Wing Flight Attitude Control Based on L1 Adaptive |
| title_full | Modeling and Simulation Analysis of Bionic Flapping-Wing Flight Attitude Control Based on L1 Adaptive |
| title_fullStr | Modeling and Simulation Analysis of Bionic Flapping-Wing Flight Attitude Control Based on L1 Adaptive |
| title_full_unstemmed | Modeling and Simulation Analysis of Bionic Flapping-Wing Flight Attitude Control Based on L1 Adaptive |
| title_short | Modeling and Simulation Analysis of Bionic Flapping-Wing Flight Attitude Control Based on L1 Adaptive |
| title_sort | modeling and simulation analysis of bionic flapping wing flight attitude control based on l1 adaptive |
| url | http://dx.doi.org/10.1155/2023/9066127 |
| work_keys_str_mv | AT lunli modelingandsimulationanalysisofbionicflappingwingflightattitudecontrolbasedonl1adaptive AT fanbai modelingandsimulationanalysisofbionicflappingwingflightattitudecontrolbasedonl1adaptive AT wenchengwang modelingandsimulationanalysisofbionicflappingwingflightattitudecontrolbasedonl1adaptive AT xiaojinwu modelingandsimulationanalysisofbionicflappingwingflightattitudecontrolbasedonl1adaptive AT yihuadong modelingandsimulationanalysisofbionicflappingwingflightattitudecontrolbasedonl1adaptive |