Scholarlink

In this paper, a data-based BP neural network offline parameter identification method is proposed with a 500kg-class rotorcraft as the research object. Firstly, multiple manned flight tests are conducted to obtain the flight test data for offline training; secondly, the longitudinal and lateral identification models of aerodynamic data are established by offline training using BP neural network algorithm with the pilot's action of the operating stick as the input and the flight state quantities of the actual flights as the output; lastly, the nonlinear mathematical simulation is conducted to validate the effectiveness of the identification algorithm by comparing the flight test and the flight state quantities solved by the identification model, and the CR bounds are used as the analytical method to verify the validity of the identification algorithm. Finally, nonlinear mathematical simulation is carried out to verify the effectiveness of the recognition algorithm by comparing the flight test and the state quantities solved by the recognition model, and the recognition accuracy is evaluated by using the CR community as an analytical tool.

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Research on Model Parameter Identification of Rotorcraft Based on BP Neural Network

  • 会议论文

  • 发表日期：

    2025年

查看全文
  • Pengcheng Dong,
  • Na Yao,
  • Kunfeng Lu,
  • Zhaolei Wang,
  • Zhe Zhang

摘要

In this paper, a data-based BP neural network offline parameter identification method is proposed with a 500kg-class rotorcraft as the research object. Firstly, multiple manned flight tests are conducted to obtain the flight test data for offline training; secondly, the longitudinal and lateral identification models of aerodynamic data are established by offline training using BP neural network algorithm with the pilot's action of the operating stick as the input and the flight state quantities of the actual flights as the output; lastly, the nonlinear mathematical simulation is conducted to validate the effectiveness of the identification algorithm by comparing the flight test and the flight state quantities solved by the identification model, and the CR bounds are used as the analytical method to verify the validity of the identification algorithm. Finally, nonlinear mathematical simulation is carried out to verify the effectiveness of the recognition algorithm by comparing the flight test and the state quantities solved by the recognition model, and the recognition accuracy is evaluated by using the CR community as an analytical tool.