嵌入式大气数据传感系统的模糊逻辑建模方法

王坤; 黄达

doi:10.7638/kqdlxxb-2016.0140

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嵌入式大气数据传感系统的模糊逻辑建模方法

研究论文 | 更新时间：2026-05-28

- 嵌入式大气数据传感系统的模糊逻辑建模方法
- Fuzzy logic algorithm for flush air data sensing system
- 空气动力学学报 2019年37卷第3期页码：412-418
- 作者机构：
  
  南京航空航天大学航空学院,南京,210016
- 作者简介：
  
  王坤(1993-), 男, 江苏淮安人, 硕士, 研究方向:实验流体力学.E-mail:mailwk@163.com
  黄达(1966-), 男, 江苏海门人, 研究员, 研究方向:流体力学, 飞行器设计.E-mail:njdhcn@nuaa.edu.cn
- 基金信息：
- DOI：10.7638/kqdlxxb-2016.0140
  中图分类号： V212
- 纸质出版：2019
- 稿件说明：
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王坤, 黄达. 嵌入式大气数据传感系统的模糊逻辑建模方法[J]. 空气动力学学报, 2019, 37(3): 412-418.

WANG Kun, HUANG Da. Fuzzy logic algorithm for flush air data sensing system[J]. 2019, 37(3): 412-418.
王坤, 黄达. 嵌入式大气数据传感系统的模糊逻辑建模方法[J]. 空气动力学学报, 2019, 37(3): 412-418. DOI： 10.7638/kqdlxxb-2016.0140.

WANG Kun, HUANG Da. Fuzzy logic algorithm for flush air data sensing system[J]. 2019, 37(3): 412-418. DOI： 10.7638/kqdlxxb-2016.0140.

摘要

为克服传统的大气数据传感系统的不足，对嵌入式大气数据系统展开了研究。以某飞翼布局飞行器为研究对象，通过风洞试验和CFD数据，研究了针对嵌入式大气数据系统的模糊逻辑建模方法。以模型表面若干测压点的压力或压力系数作为模糊逻辑系统的输入，以迎角、侧滑角、来流速度和海拔高度作为输出，分别采用自适应和固定形状参数的隶属函数作为模型组成部分，混合使用梯度下降法和最小二乘法来识别模糊逻辑系统的参数，从而建立针对该嵌入式大气数据系统的模糊逻辑模型。建模结果表明，相比以往仅使用梯度下降法和固定形状参数的隶属函数的模糊逻辑模型，自适应隶属函数的引入使得模型精度与求解速度得到提高。

Abstract

To overcome the disadvantages of traditional air data sensing system

the embedded flush air data sensing (FADS) system is investigated. Based on wind tunnel experiments and computational fluid dynamics(CFD) simulation results of an all-wing aircraft

the fuzzy logic modeling algorithm for the FADS system is studied. By using pressure or pressure coefficients on the model surface as input data while angle of attack

angle of sideslip

inlet velocity and altitude as output data

a fuzzy logic structure is built for the FADS system with adaptive and fixed shape parameters membership functions as the fuzzy model's component and parameters of the system solved by a hybrid learning procedure (gradient descent and least squares estimate method). The modeling results show that

compared with the methods with fixed shape parameters membership functions and inner functions parameters solved by gradient descent

the method with adaptive membership functions has higher modeling accuracy and efficiency.

关键词

Keywords

references

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