Siwek Michał, Baranowski Leszek, Ładyżyńska-Kozdraś Edyta
Faculty of Mechatronics, Armament and Aerospace, Military University of Technology, Kaliskiego 2 Street, 00-908 Warsaw, Poland.
Faculty of Mechatronics, Warsaw University of Technology, ul. św. Boboli 8, 02-525 Warsaw, Poland.
Sensors (Basel). 2024 Dec 18;24(24):8072. doi: 10.3390/s24248072.
This paper considers the problem of flying a UAV along a given trajectory at speeds close to the speed of sound and above. A novel pitch channel control system is presented using the example of a trajectory with rapid and large changes in flight height. The control system uses a proportional-integral-differential (PID) controller, whose gains were first determined using the Ziegler-Nichols II method. The determined gains were then optimised to minimise height error using a recurrent back-propagation neural network (PIDNN), with which new controller gains were determined, which is also a novelty of this study. Simulations were carried out for flights at subsonic speeds close to the speed of sound and supersonic speeds, at low and high altitudes. The simulations showed that determining controller gains using a recurrent neural network significantly minimises height errors and increases the flexibility of the PID controller.
本文考虑了无人机以接近音速及高于音速的速度沿给定轨迹飞行的问题。以飞行高度快速且大幅变化的轨迹为例，提出了一种新颖的俯仰通道控制系统。该控制系统使用比例-积分-微分（PID）控制器，其增益首先采用齐格勒-尼科尔斯II方法确定。然后，使用递归反向传播神经网络（PIDNN）对确定的增益进行优化，以最小化高度误差，由此确定了新的控制器增益，这也是本研究的一个新颖之处。针对在接近音速的亚音速速度以及超音速速度下、在低空和高空的飞行进行了仿真。仿真结果表明，使用递归神经网络确定控制器增益可显著减小高度误差并提高PID控制器的灵活性。
