TY - GEN
T1 - Modeling and Fitting of Aircraft Fly-by-wire Control System Based on Improved BP Neural Network
AU - Qian, Zhen
AU - Liang, Yan
AU - Ma, Cunbao
AU - Zhao, Zhenyao
AU - Yao, Aihua
N1 - Publisher Copyright:
© 2022 IEEE.
PY - 2022
Y1 - 2022
N2 - In this paper, the modeling and fitting of the longitudinal and lateral fly-by-wire control systems of the flight parameter data are realized through the analysis of the control laws and manipulation response types of the aircraft fly-by-wire control system. Firstly, outliers are eliminated and corrected based on the polynomial fitting method, and the angular velocity, attitude angle, overload and other flight parameter data are digitally filtered by the median filter based on the least square method. Then the appropriate flight parameters are selected as the input and output, which are sent to the improved BP neural network based on genetic algorithm for training. Finally, the modeling and fitting of aircraft longitudinal and lateral fly-by-wire control systems are realized in the three flight stages of climb, cruise, and descent/landing.
AB - In this paper, the modeling and fitting of the longitudinal and lateral fly-by-wire control systems of the flight parameter data are realized through the analysis of the control laws and manipulation response types of the aircraft fly-by-wire control system. Firstly, outliers are eliminated and corrected based on the polynomial fitting method, and the angular velocity, attitude angle, overload and other flight parameter data are digitally filtered by the median filter based on the least square method. Then the appropriate flight parameters are selected as the input and output, which are sent to the improved BP neural network based on genetic algorithm for training. Finally, the modeling and fitting of aircraft longitudinal and lateral fly-by-wire control systems are realized in the three flight stages of climb, cruise, and descent/landing.
KW - BP neural network
KW - data preprocessing
KW - fly-by-wire control system
KW - modeling method
UR - http://www.scopus.com/inward/record.url?scp=85136384359&partnerID=8YFLogxK
U2 - 10.1109/ITAIC54216.2022.9836556
DO - 10.1109/ITAIC54216.2022.9836556
M3 - 会议稿件
AN - SCOPUS:85136384359
T3 - IEEE Joint International Information Technology and Artificial Intelligence Conference (ITAIC)
SP - 2250
EP - 2256
BT - IEEE 10th Joint International Information Technology and Artificial Intelligence Conference, ITAIC 2022
A2 - Xu, Bing
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 10th IEEE Joint International Information Technology and Artificial Intelligence Conference, ITAIC 2022
Y2 - 17 June 2022 through 19 June 2022
ER -