System Identification Based Methods for the Development of Nonlinear Dynamic Models for Flight Simulators

固定翼和旋转翼训练模拟器是提高民用和军用航空安全性的重要组成部分，CAE是世界上最大的训练模拟器供应商。可以说，训练模拟器最重要的部分是飞行模型，因为它驱动所有模拟器子系统。因此，民航管理机构要求飞行模型的行为在特定的公差范围内与真实飞机的行为相匹配。军用模拟器也有类似的要求。因此，通常通过选择模型的参数来开发飞行模型，以使其行为与来自真实飞机的记录的飞行试验数据相匹配。为测试飞机配备大量的仪器设备，并进行大量的测试动作，是非常耗时和昂贵的。此外，调整飞行模型的参数，使其行为与真实飞机的行为匹配，达到所需的容差，也是一个极其耗时的过程，需要昂贵的主题专家的大量参与。该项目旨在开发和应用新的先进系统识别算法，该算法将自动估计飞行模型中的参数，使其行为在最佳意义上与记录的飞行试验数据相匹配。此外，相同的系统识别算法将用于确定试飞飞机的最低Airdata仪器要求，这将导致飞行模型具有足够的保真度，以符合适当的法规。这两项任务都将极大地降低CAE开发训练模拟器的成本，并潜在地提高模拟器的逼真度。

Fixed wing and rotary wing training simulators are an important part of improving the safety of both civil and military aviation, and CAE is the world's largest supplier of training simulators. Arguably the most important part of a training simulator is the flight model as it drives all the simulator sub-systems. Therefore, the regulating bodies for civil aviation require the flight model behaviour to match that of the real aircraft within a specified set of tolerances. Military simulators have similar requirements. Flight models are therefore often developed by selecting the parameters of the model such that its behaviour matches recorded flight test data from the real aircraft. Outfitting the test aircraft with a vast array of instrumentation and flying a large set of test maneuvers is extremely time consuming and expensive. In addition, tuning the parameters of the flight model such that its behaviour matches the real aircraft, to within the required tolerances, is also extremely time-consuming process that requires significant involvement of expensive subject matter experts. This project aims to develop and apply new advanced system identification algorithms that will automatically estimate the parameters in a flight model such that its behaviour matches the recorded flight test data in an optimal sense. In addition, the same system identification algorithms will be used to determine the minimum airdata instrumentation requirements for the flight test aircraft that will result in a flight model with sufficient fidelity to comply with the appropriate regulations. Both of these tasks will greatly reduce CAE costs for developing training simulators, and potentially increase the fidelity of the simulators.