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| On extended state Kalman filter-based identification algorithm for aerodynamic parameters |
| WenyanBai1,RuizheJia2,3,PengYu2,3,WenchaoXue4 |
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| (1 Beijing Aerospace Automatic Control Institute, Beijing 100854, China;2 School of Automation and Electrical Engineering, University of Science and Technology Beijing, Beijing 100083, China
3 Key Laboratory of Knowledge Automation for Industrial Processes, Ministry of Education, Beijing 100083, China;4 Key Laboratory of Systems and Control, Institute of Systems Science, Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing 100190, China) |
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| 摘要: |
| In this paper, the problem of time-varying aerodynamic parameters identification under measurement noises is studied. By
analyzing the key aerodynamic parameters that affect the aircraft control system, a system model with extended states for
identifying equivalent aerodynamic parameters is established, and error parameters are extended to the system state, avoiding
the difficulty caused by the unknown dynamic in the system. Furthermore, an identification algorithm based on extended state
Kalman filter is designed, and it is proved that the algorithm has quasi-consistency, thus, the estimation error can be evaluated
in real time. Finally, the simulation results under typical flight scenarios show that the designed algorithm can accurately
identify aerodynamic parameters, and has desired convergence speed and convergence precision. |
| 关键词: Aerodynamic parameters · Parameter identification · Extended state Kalman filter |
| DOI：https://doi.org/10.1007/s11768-023-00192-5 |
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| 基金项目:This work was supported by the National Natural Science Foundation of China (No. 62122083) and Youth Innovation Promotion Association, CAS. |
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| On extended state Kalman filter-based identification algorithm for aerodynamic parameters |
| Wenyan Bai1,Ruizhe Jia2,3,Peng Yu2,3,Wenchao Xue4 |
| (1 Beijing Aerospace Automatic Control Institute, Beijing 100854, China;2 School of Automation and Electrical Engineering, University of Science and Technology Beijing, Beijing 100083, China
3 Key Laboratory of Knowledge Automation for Industrial Processes, Ministry of Education, Beijing 100083, China;4 Key Laboratory of Systems and Control, Institute of Systems Science, Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing 100190, China) |
| Abstract: |
| In this paper, the problem of time-varying aerodynamic parameters identification under measurement noises is studied. By
analyzing the key aerodynamic parameters that affect the aircraft control system, a system model with extended states for
identifying equivalent aerodynamic parameters is established, and error parameters are extended to the system state, avoiding
the difficulty caused by the unknown dynamic in the system. Furthermore, an identification algorithm based on extended state
Kalman filter is designed, and it is proved that the algorithm has quasi-consistency, thus, the estimation error can be evaluated
in real time. Finally, the simulation results under typical flight scenarios show that the designed algorithm can accurately
identify aerodynamic parameters, and has desired convergence speed and convergence precision. |
| Key words: Aerodynamic parameters · Parameter identification · Extended state Kalman filter |
