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Identification of a Cessna Citation II Model Based on Flight Test Data

机译：基于飞行测试数据的塞斯纳Citation II模型识别

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As a result of new aviation legislation, from 2019 on all air-carrier pilots are obliged to go through flight simulator-based stall recovery training. For this reason the Control and Simulation division at Delft University of Technology has set up a task force to develop a new methodology for high-fidelity aircraft stall behavior modeling and simulation. As part of this research project, the development of a new high-fidelity Cessna II simulation model, valid throughout the normal, pre-stall flight envelope, is presented in this paper. From an extensive collection of flight test data, aerodynamic model identification was performed using the Two-Step Method. New in this approach is the use of the Unscented Kalman Filter for an improved accuracy and robustness of the state estimation step. Also, for the first time an explicit data-driven model structure selection is presented for the Citation II by making use of an orthogonal regression scheme. This procedure has indicated that most of the six non-dimensional forces and moments can be parametrized sufficiently by a linear model structure. It was shown that only the translational and lateral aerodynamic force models would benefit from the addition of higher order terms, more specifically the squared angle of attack and angle of sideslip. The newly identified aerodynamic model was implemented into an upgraded version of the existing simulation framework and will serve as a basis for the integration of a stall and post-stall model.
机译：根据新的航空法规，从2019年开始，所有航空承运人飞行员都必须接受基于飞行模拟器的失速恢复培训。因此，代尔夫特理工大学的控制与仿真部门成立了一个工作队，以开发一种用于高保真飞机失速行为建模和仿真的新方法。作为该研究项目的一部分，本文介绍了一种新的高保真塞斯纳II仿真模型的开发，该模型在正常的失速前飞行包线中有效。从广泛的飞行测试数据收集中，使用两步法进行了空气动力学模型识别。此方法的新功能是使用无味卡尔曼滤波器，以提高状态估计步骤的准确性和鲁棒性。同样，这是首次通过使用正交回归方案为Citation II提出了明确的数据驱动模型结构选择。该过程表明，线性模型结构可以充分参数化六个无量纲力和力矩。结果表明，只有平移和侧向气动模型会受益于更高阶项的增加，更确切地说是迎角平方和侧滑角。新确定的空气动力学模型已实施到现有模拟框架的升级版本中，并将作为失速和失速后模型集成的基础。

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