Modeling and Fitting of Aircraft Fly-by-wire Control System Based on Improved BP Neural Network

Zhen Qian; Yan Liang; Cunbao Ma; Zhenyao Zhao; Aihua Yao

doi:10.1109/ITAIC54216.2022.9836556

Modeling and Fitting of Aircraft Fly-by-wire Control System Based on Improved BP Neural Network

Zhen Qian, Yan Liang, Cunbao Ma, Zhenyao Zhao, Aihua Yao

School of Automation

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Abstract

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.

Original language	English
Title of host publication	IEEE 10th Joint International Information Technology and Artificial Intelligence Conference, ITAIC 2022
Editors	Bing Xu
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	2250-2256
Number of pages	7
ISBN (Electronic)	9781665422079
DOIs	https://doi.org/10.1109/ITAIC54216.2022.9836556
State	Published - 2022
Event	10th IEEE Joint International Information Technology and Artificial Intelligence Conference, ITAIC 2022 - Chongqing, China Duration: 17 Jun 2022 → 19 Jun 2022

Publication series

Name	IEEE Joint International Information Technology and Artificial Intelligence Conference (ITAIC)
Volume	2022-June
ISSN (Print)	2693-2865

Conference

Conference	10th IEEE Joint International Information Technology and Artificial Intelligence Conference, ITAIC 2022
Country/Territory	China
City	Chongqing
Period	17/06/22 → 19/06/22

Keywords

BP neural network
data preprocessing
fly-by-wire control system
modeling method

Access to Document

10.1109/ITAIC54216.2022.9836556

Cite this

Qian, Z., Liang, Y., Ma, C., Zhao, Z., & Yao, A. (2022). Modeling and Fitting of Aircraft Fly-by-wire Control System Based on Improved BP Neural Network. In B. Xu (Ed.), IEEE 10th Joint International Information Technology and Artificial Intelligence Conference, ITAIC 2022 (pp. 2250-2256). (IEEE Joint International Information Technology and Artificial Intelligence Conference (ITAIC); Vol. 2022-June). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ITAIC54216.2022.9836556

Qian, Zhen ; Liang, Yan ; Ma, Cunbao et al. / Modeling and Fitting of Aircraft Fly-by-wire Control System Based on Improved BP Neural Network. IEEE 10th Joint International Information Technology and Artificial Intelligence Conference, ITAIC 2022. editor / Bing Xu. Institute of Electrical and Electronics Engineers Inc., 2022. pp. 2250-2256 (IEEE Joint International Information Technology and Artificial Intelligence Conference (ITAIC)).

@inproceedings{b424132619d04e02b7087be80dabc08c,

title = "Modeling and Fitting of Aircraft Fly-by-wire Control System Based on Improved BP Neural Network",

abstract = "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.",

keywords = "BP neural network, data preprocessing, fly-by-wire control system, modeling method",

author = "Zhen Qian and Yan Liang and Cunbao Ma and Zhenyao Zhao and Aihua Yao",

note = "Publisher Copyright: {\textcopyright} 2022 IEEE.; 10th IEEE Joint International Information Technology and Artificial Intelligence Conference, ITAIC 2022 ; Conference date: 17-06-2022 Through 19-06-2022",

year = "2022",

doi = "10.1109/ITAIC54216.2022.9836556",

language = "英语",

series = "IEEE Joint International Information Technology and Artificial Intelligence Conference (ITAIC)",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

pages = "2250--2256",

editor = "Bing Xu",

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}

Qian, Z, Liang, Y, Ma, C, Zhao, Z & Yao, A 2022, Modeling and Fitting of Aircraft Fly-by-wire Control System Based on Improved BP Neural Network. in B Xu (ed.), IEEE 10th Joint International Information Technology and Artificial Intelligence Conference, ITAIC 2022. IEEE Joint International Information Technology and Artificial Intelligence Conference (ITAIC), vol. 2022-June, Institute of Electrical and Electronics Engineers Inc., pp. 2250-2256, 10th IEEE Joint International Information Technology and Artificial Intelligence Conference, ITAIC 2022, Chongqing, China, 17/06/22. https://doi.org/10.1109/ITAIC54216.2022.9836556

Modeling and Fitting of Aircraft Fly-by-wire Control System Based on Improved BP Neural Network. / Qian, Zhen; Liang, Yan; Ma, Cunbao et al.
IEEE 10th Joint International Information Technology and Artificial Intelligence Conference, ITAIC 2022. ed. / Bing Xu. Institute of Electrical and Electronics Engineers Inc., 2022. p. 2250-2256 (IEEE Joint International Information Technology and Artificial Intelligence Conference (ITAIC); Vol. 2022-June).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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

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

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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 -

Qian Z, Liang Y, Ma C, Zhao Z, Yao A. Modeling and Fitting of Aircraft Fly-by-wire Control System Based on Improved BP Neural Network. In Xu B, editor, IEEE 10th Joint International Information Technology and Artificial Intelligence Conference, ITAIC 2022. Institute of Electrical and Electronics Engineers Inc. 2022. p. 2250-2256. (IEEE Joint International Information Technology and Artificial Intelligence Conference (ITAIC)). doi: 10.1109/ITAIC54216.2022.9836556

Modeling and Fitting of Aircraft Fly-by-wire Control System Based on Improved BP Neural Network

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