| 尖楔前体飞行器FADS系统的径向基网络建模及验证 |
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| 作者姓名： | 王鹏 |
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| 作者单位： | 中国航天空气动力技术研究院，北京 100074 |
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| 摘 要： | 文章研究了针对一种用于尖楔外形的嵌入式大气数据传感(flush air data sensing,FADS)系统的解算模型及精度.首先基于飞行包络及CFD数据建立了FADS系统的测压孔选取标准;然后基于径向基函数(radial basis function,RBF)的人工神经网络建模技术构建了FADS系统的网络解算模型;最后给出了模型的测试误差,分析了气动延时效应、位置误差等误差源模型对算法精度的影响,并给出了网络模型的预测精度.结果表明,针对尖楔外形测压孔配置特征,基于RBF的人工神经网络算法解算精度较好,攻角、侧滑角、Mach数及静压的网络输出预测值与真实值吻合较好,输出的测试误差(绝对值)分别小于0.25°,0.5°,0.05及250 Pa.结果同时表明神经网络建模技术在尖楔前体飞行器FADS系统中的有效性.
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| 关 键 词： | 嵌入式大气数据传感系统 尖楔前体 径向基函数神经网络 飞行参数 预测精度 |
| 收稿时间： | 2019-03-21 |
RBF Neural Network Modeling and Validation for FADS System Applied to the Vehicle with Sharp Wedged Fore-Bodies |
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| Authors: | WANG Peng |
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| Affiliation: | China Academy of Aerospace Aerodynamics, Beijing 100074, China |
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| Abstract: | In allusion to the difficulty in aerodynamic modeling of the flush air data sensing(FADS) system for sharp wedged fore-bodies, an extended minimum resource allocating network radial basis function(RBF) neural network was developed as the FADS system model for its good generalization capabilities and compact algorithm structure. Single hidden layer artificial neural networks based on RBF were designed to generate regression model for the FADS system. The neural networks were trained and tested with pressure measurements inputs to model the aerodynamic relationship between aircraft surface pressure and air data states. RBF neural network model was then used to calculate and predict the Mach number, angle of attack, angle of sideslip and free stream static pressure displacing, rather than the traditional aerodynamic model. Computational fluid dynamic(CFD) simulations were implemented to identify the ideal pressure port locations, and wind-tunnel tests and CFD results were carried out to train and test the extended minimum resource allocating network RBF neural network. These models were then applied to pressure measurements during flight tests of the aircraft and sensing system to judge the viability of the method. The results show that, the RBF neural network model for the FADS system is accurate enough. Neural network outputs agree well with the real test data, and the testing error distribution (absolute error distribution) for angle of attack, angle of sideslip, Mach number and free stream static pressure is less than 0.25°, 0.5°, 0.05, and 250 Pa, respectively. The results also present that the RBF neural network model for the FADS system will be further developed in the future. |
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| Keywords: | flush air data sensing system(FADS) sharp wedged fore-bodies RBF neural network flight parameters prediction accuracy |
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