a physics-based digital twin for model predictive control of autonomous unmanned aerial vehicle landing

This paper proposes a two-level, data-driven, digital twin concept for the autonomous landing of aircraft, under some assumptions. It features a digital twin instance for model predictive control; and an innovative, real-time, digital twin prototype for fluid-structure interaction and flight dynamics to inform it. The latter digital twin is based on the linearization about a pre-designed glideslope trajectory of a high-fidelity, viscous, nonlinear computational model for flight dynamics; and its projection onto a low-dimensional approximation subspace to achieve real-time performance, while maintaining accuracy. Its main purpose is to predict in real-time, during flight, the state of an aircraft and the aerodynamic forces and moments acting on it. Unlike static lookup tables or regression-based surrogate models based on steady-state wind tunnel data, the aforementioned real-time digital twin prototype allows the digital twin instance for model predictive control to be informed by a trul...本文在若干假设前提下，提出了一种面向飞行器自主着陆的两级数据驱动数字孪生 (digital twin) 概念。该方案包含用于模型预测控制 (model predictive control) 的数字孪生实例，以及面向流固耦合与飞行动力学的创新型实时数字孪生原型，为前者提供信息支撑。后者基于针对飞行动力学的高保真黏性非线性计算模型，围绕预先设计的下滑航迹进行线性化处理，并将其投影至低维近似子空间，以在保证精度的同时实现实时运行性能。其核心目标为在飞行过程中实时预测飞行器的状态以及作用于其上的气动力与气动力矩。与基于稳态风洞数据的静态查找表或基于回归的代理模型不同，上述实时数字孪生原型可使用于模型预测控制的数字孪生实例获得真正意义上的……