A Formation Flight Method with an Improved Deep Neural Network for Multi-UAV System

一种面向多无人机协同编队控制的改进深度神经网络方法

¹ Key Laboratory of Airworthiness Certification Technology for Civil Aviation Aircraft, Tianjin 300300, China
² College of Airworthiness, Civil Aviation University of China, Tianjin 300300, China
³ College of Electronic Information and Automation, Civil Aviation University of China, Tianjin 300300, China
⁴ School of Computer Science, Northwestern Polytechnical University, Xi'an 710072, China

Received: 23 April 2019

Abstract

It is crucial to develop an effective controller for the multi-UAV system to contribute to the frontier fields, such as the electronic warfare. To address the dilemma of the cooperative formation with the high dimensional data, a deep neural network(NN) controller is developed in this paper. Firstly, a deep NN model is used to tune parameters of PID controller online. Secondly, this paper introduces an improved deep NN model integrating the momentum to improve the performance of the classical NN model and satisfy the condition for the real time cooperative formation. Lastly, the cooperative formation task is achieved by extending the proposed cooperative controller with an improved NN to the complex multi-UAV system. The simulation result of multi-UAV formation demonstrates the effectiveness of the proposed method, which achieves a faster formation than competitors.

摘要

研究了多维度飞行数据下的协同编队控制问题，提出了一种基于改进深度神经网络的协同飞行控制方法。首先，使用深度神经网络在线整定PID控制器，设计了一种基于深度神经网络的PID控制器；其次，针对传统深度神经网络收敛速度慢、学习效率低的问题，同时为了满足多无人机编队飞行的实时性，在深度神经网络控制器中引入动量因子以提高网络的学习性能；最后，将所设计的改进深度神经网络控制器扩展到多无人机协同飞行任务场景实现协同编队飞行。对多无人机协同编队飞行进行仿真验证，仿真结果表明，所设计的改进深度神经网络编队控制器可以有效实现多无人机的编队生成与协同飞行。