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Development of Aeroservoelastic Analytical Models and Gust Load Alleviation Control Laws of a SensorCraft Wind-Tunnel Model Using Measured Data

机译:利用测得的数据开发SensorCraft风洞模型的航空弹性分析模型和减轻风团负荷的控制规律

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摘要

Aeroservoelastic (ASE) analytical models of a SensorCraft wind-tunnel model are generated using measured data. The data was acquired during the ASE wind-tunnel test of the HiLDA (High Lift-to-Drag Active) Wing model, tested in the NASA Langley Transonic Dynamics Tunnel (TDT) in late 2004. Two time-domain system identification techniques are applied to the development of the ASE analytical models: impulse response (IR) method and the Generalized Predictive Control (GPC) method. Using measured control surface inputs (frequency sweeps) and associated sensor responses, the IR method is used to extract corresponding input/output impulse response pairs. These impulse responses are then transformed into state-space models for use in ASE analyses. Similarly, the GPC method transforms measured random control surface inputs and associated sensor responses into an AutoRegressive with eXogenous input (ARX) model. The ARX model is then used to develop the gust load alleviation (GLA) control law. For the IR method, comparison of measured with simulated responses are presented to investigate the accuracy of the ASE analytical models developed. For the GPC method, comparison of simulated open-loop and closed-loop (GLA) time histories are presented.
机译:使用测得的数据生成SensorCraft风洞模型的航空弹性(ASE)分析模型。该数据是在2004年底在NASA兰利跨音速动力隧道(TDT)中测试的HiLDA(高举升主动式)机翼模型的ASE风洞测试期间获取的。应用了两种时域系统识别技术ASE分析模型的开发:脉冲响应(IR)方法和广义预测控制(GPC)方法。使用测得的控制表面输入(频率扫描)和相关的传感器响应,IR方法用于提取相应的输入/输出脉冲响应对。然后将这些脉冲响应转换为状态空间模型,以用于ASE分析。类似地,GPC方法将测得的随机控制面输入和关联的传感器响应转换为带有外源输入(ARX)的自回归模型。然后使用ARX模型来开发阵风减轻负荷(GLA)控制律。对于IR方法,提出了测量响应与模拟响应的比较,以研究所开发的ASE分析模型的准确性。对于GPC方法,提出了模拟开环和闭环(GLA)时间历史的比较。

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