A tail-sitter aircraft’s control is a challenging task, especially during transition maneuvers where the lift and drag forces are highly non-linear. In this work, we implement a neural network (NN) capable to estimate such nonlinearities. Once they are estimated, one can propose a control scheme where these forces can correctly feed-forwarded. Our NN implementation has been programmed in C++ within 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 imply light computational load. To test our approach, we have carried out a series of realistic simulations in the Software in The Loop (SITL) using the PX4 Autopilot firmware. These experiments demonstrate that the implemented NN can be used to estimate the tail-sitter aerodynamic forces to improve the control algorithms during all the flight phases of the tail-sitter aircraft: hover, cruise flight, and transition.

尾翼飞机的控制是一项具有挑战性的任务，尤其是在升力和阻力高度非线性的过渡机动中。在这项工作中，我们实现了一个能够估计这种非线性的神经网络 (NN)。一旦估计出来，就可以提出一种控制方案，这些力可以正确地前馈。我们的神经网络实现是在 PX4 Autopilot 中用 C++ 编程的，PX4 Autopilot 是一种用于无人机的开源自动驾驶仪。为了确保这种实现不会显着影响自动驾驶仪的性能，编码的神经网络必须意味着轻的计算负载。为了测试我们的方法，我们使用 PX4 Autopilot 固件在环路软件 (SITL) 中进行了一系列真实模拟。