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论文:2014,Vol:32,Issue(3):351-355 |
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引用本文: |
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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 |
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Zhou Guochang, Li Qingdong, Guo Yangming. A Highly Precise FADS (Flush Air-Data Sensing System) Algorithm[J]. Northwestern polytechnical university |
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一种高精度的嵌入式大气数据传感系统算法 |
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周国昌1, 李清东2, 郭阳明3 |
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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
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A Highly Precise FADS (Flush Air-Data Sensing System) Algorithm |
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Zhou Guochang1, Li Qingdong2, Guo Yangming3 |
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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
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收稿日期: 2013-11-06
修回日期:
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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:
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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。
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参考文献: |
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[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, Khler 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 |
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