论文标题
在尾灯飞机中实施非线性估算的神经网络
Implementation of a neural network for non-linearities estimation in a tail-sitter aircraft
论文作者
论文摘要
尾气飞机的控制是一项具有挑战性的任务，尤其是在过渡动作中，升力和阻力力是高度非线性的。在这项工作中，我们实施了能够估计此类非线性的神经网络（NN）。一旦估算出它们，就可以提出一个控制方案，这些力量可以正确馈入。我们对NN的实现已在PX4 Autopilot上的C ++中编程为无人机的开源自动驾驶仪。为了确保此实现不会严重影响自动驾驶仪的性能，编码的NN必须具有光计算负载。为了测试我们的方法，我们使用PX4 AutoPilot在循环（SITL）中的软件（SITL）中进行了一系列逼真的模拟。这些实验表明，实施的NN可用于估计尾流空气动力学力，并可用于在尾灯飞机的所有飞行阶段中改善控制算法：悬停，巡航飞行和过渡。
The control of a tail-sitter aircraft is a challenging task, especially during transition maneuver where the lift and drag forces are highly nonlinear. In this work, we implement a Neural Network (NN) capable of estimate such nonlinearities. Once they are estimated, one can propose a control scheme where these forces can correctly feed-forwarded. Our implementation of the NN has been programmed in C++ on the PX4 Autopilot an open-source autopilot for drones. To ensure that this implementation does not considerably affect the autopilot's performance, the coded NN must be of a light computational load. With the aim to test our approach, we have carried out a series of realistic simulations in the Software in The Loop (SITL) using the PX4 Autopilot. These experiments demonstrate that the implemented NN can be used to estimate the tail-sitter aerodynamic forces, and can be used to improve the control algorithms during all the flight phases of the tail-sitter aircraft: hover, cruise flight, and transition.
