论文:2014,Vol:32,Issue(3):351-355
引用本文:
周国昌, 李清东, 郭阳明. 一种高精度的嵌入式大气数据传感系统算法[J]. 西北工业大学
Zhou Guochang, Li Qingdong, Guo Yangming. A Highly Precise FADS (Flush Air-Data Sensing System) Algorithm[J]. Northwestern polytechnical university

一种高精度的嵌入式大气数据传感系统算法
周国昌1, 李清东2, 郭阳明3
1. 中国空间技术研究院 西安分院, 陕西 西安 710000;
2. 北京航空航天大学 自动化科学与电气工程学院, 北京 100191;
3. 西北工业大学 计算机学院, 陕西 西安 710072
摘要:
针对现有FADS算法存在的不足,提出了一种融合广义逆和BP神经网络的高精度嵌入式大气数据传感系统算法。该算法的特点是:①应用三点法预估当地迎角和当地侧滑角,并对测压点进行故障诊断;然后用具有容错能力的广义逆矩阵求解总压力和修正动压;②应用BP神经网络具有的强大非线性映射能力,拟合FADS系统的非线性数学模型,减少输入向量的维数和网络训练难度,完成测量校正。结果表明,所提出的FADS算法在精度、可靠性等方面均有较好的性能。
关键词:    嵌入式大气数据传感器系统    广义逆矩阵    BP神经网络   
A Highly Precise FADS (Flush Air-Data Sensing System) Algorithm
Zhou Guochang1, Li Qingdong2, Guo Yangming3
1. Academy of Space Technology, Xi'an 710000, China;
2. School of Automation Science and Electronic Engineering, Beihang University, Beijing 100083, China;
3. Department of Computer Science and Technology, Northwestern Polytechnical University, Xi'an 710072, China
Abstract:
The existing flush air data sensing systems have some deficiencies such as singularity values in calculat-ing air data. Hence we propose a flush air data sensing algorithm based on the pseudo-inverse matrix and back-propagation (BP) neural networks, which we believe can overcome the deficiencies. The core of the algorithm con-sists of:(1) it uses the three-point method to estimate the local angle of attack and sideslip of an aircraft and diag-nose its faults at pressure points;it then uses the pseudo-inverse matrix with fault tolerance to solve the total pres-sure and amend the dynamic pressure;(2) it utilizes the strong nonlinear mapping capability of the BP neural net-works to fit the nonlinear mathematical model of the flush air-data sensing system, thus reducing the number of di-mensions of input vectors and the level of difficulty in training networks and achieving the measurement calibration. The simulation results, given in Tables 1 and 2, and their analysis show preliminarily that our new algorithm has better fault tolerance and can produce highly precise and reliable air data.
Key words:    aircraft    angle of attack    backpropagation algorithms    estimation    fault tolerance    conformal mapping    mathematical models    neural networks    sensors    flush air-data sensing system    pseudo-inverse matrix   
收稿日期: 2013-11-06     修回日期:
DOI:
基金项目: 国家自然科学基金(61371024);航空科学基金(2013ZD5351);航天支撑技术基金
通讯作者:     Email:
作者简介: 周国昌(1978-),中国空间技术研究院高级工程师,主要从事航天器测控故障检测与诊断等研究。
相关功能
PDF(405KB) Free
打印本文
把本文推荐给朋友
作者相关文章
周国昌  在本刊中的所有文章
李清东  在本刊中的所有文章
郭阳明  在本刊中的所有文章

参考文献:
[1] Cobleigh B R, Whitmore S A, Haering E A. Flush Air Data Sensing (FADS) System Calibration Procedures and Results for Blunt Fore-Bodies[R]. California: Dryden Flight Research Center Edwards, 1999
[2] Jost M, Schwegmann F, Khler T. Flush Air Data System—An Advanced Air Data System for the Aerospace Industry [R]. AIAA-2004-5028, 2004
[3] Ian A Johnston, Peter A Jacobs. A Study of Flush Air Data System Calibration Using Numerical Simulation[R]. AIAA-1998-1606, 1998
[4] Whitmore S A, Cobleigh B R, Hearing E A. Design and Calibration of the X-33 Flush Airdata Sensing (FADS) System[R]. NASA /TM-1998-206540, 1998
[5] Moody J, Darken C. Fast Learning in Networks of Locally-Turned Processing Units[J]. Neural Computation, 1989 (1):281-294
[6] Broomhead D S, Lowe D. Multivariable Function Interpolation and Adaptive Networks[J]. Complex System, 1988 (2):321-355
[7] Albert A. Regression and the Moore-Penrose Pseudoinverse[M]. New York: Academic Press, 1972