基于迭代学习的风洞马赫数控制方法  

Iterative Learning Based Control for Wind Tunnel Mach Number

作　　者：易凡[1] 李欣蕊 杜宁[1] 郁文山[1] XI Fan;LIXin-rui;DU Ning;YU Wen-shan(High Velocity Aerodynamics Institute,China Aerodynamics Research and Development Center,Mianyang 621000,China;College of Information Science and Engineering,Northeastern University,Shenyang 110004,China)

机构地区：[1]中国空气动力研究与发展中心高速所,四川绵阳621000 [2]东北大学信息科学与工程学院,辽宁沈阳110004

出　　处：《控制工程》2020年第1期109-113,共5页Control Engineering of China

摘　　要：大飞机的研制对风洞流场马赫数精度提出了更高的要求,带有姿态角补偿的模型预测控制器有效地提高了马赫数的精度。然而,由于很难准确获取所有吹风工况的姿态角补偿模型,导致部分新工况控制效果不佳,影响了马赫数的精度。因此,提出一种基于迭代学习的获取姿态角补偿模型的方法。在已有的姿态角补偿模型基础上,根据实际的吹风试验数据,对姿态角补偿模型进行修正。经过多次吹风结果逐步提高补偿模型的精度,提升变姿态角过程中流场控制器抵抗扰动的能力,达到提高马赫数精度的目的。The development of large aircraft puts forward higher requirements on the accuracy of Mach number in wind tunnel flow field. The model predictive controller with angle of attack compensation effectively improves the accuracy of Mach number. However, it is difficult to accurately obtain the angle of attack compensation model for all blowing conditions, resulting in poor control of some new working conditions, which affects the accuracy of the Mach number. This paper proposes a method based on iterative learning to obtain the angle of attack compensation model. Based on the existing angle of attack compensation model, the angle of attack compensation model is corrected based on the actual blowing test data. After several times of blowing results, the accuracy of the compensation model is gradually improved, and the ability of the flow field controller to resist disturbance during the variable angle of attack is improved, thereby achieving the purpose of improving the accuracy of the Mach number.

关 键 词：风洞 马赫数 预测控制 姿态角补偿 迭代修正 

分 类 号：TP273[自动化与计算机技术—检测技术与自动化装置]