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论文:2020,Vol:38,Issue(2):295-302 |
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引用本文： |
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谢文光, 吴康, 阎芳, 史豪斌, 张啸晨. 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[J]. 西北工业大学学报 |
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XIE Wenguang, WU Kang, YAN Fang, SHI Haobin, ZHANG Xiaocheng. A Formation Flight Method with an Improved Deep Neural Network for Multi-UAV System[J]. Northwestern polytechnical university |
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| 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 |
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| 谢文光1,2, 吴康1,3, 阎芳1,2, 史豪斌4, 张啸晨1,2 |
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1. 民航航空器适航审定技术重点实验室, 天津 300300;
2. 中国民航大学 适航学院, 天津 300300;
3. 中国民航大学 电子信息与自动化学院, 天津 300300;
4. 西北工业大学 计算机学院, 陕西 西安 710072 |
| 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。 |
| 关键词:
协同编队
改进深度神经网络
PID控制器
动量因子
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| A Formation Flight Method with an Improved Deep Neural Network for Multi-UAV System |
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| XIE Wenguang1,2, WU Kang1,3, YAN Fang1,2, SHI Haobin4, ZHANG Xiaocheng1,2 |
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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 |
| 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. |
| Key words:
multi-UAV formation
improved deep neural network
PID controller
momentum
simulation
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| 收稿日期: 2019-04-23
修回日期:
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| DOI: 10.1051/jnwpu/20203820295 |
| 基金项目: 航空科学基金（20182667009）资助 |
| 通讯作者:
Email： |
| 作者简介: 谢文光(1984-),中国民航大学助理研究员、硕士,主要从事嵌入式计算机软件、计算机控制、机载软件适航研究。
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| 作者相关文章 |
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| 谢文光 在本刊中的所有文章 |
| 吴康 在本刊中的所有文章 |
| 阎芳 在本刊中的所有文章 |
| 史豪斌 在本刊中的所有文章 |
| 张啸晨 在本刊中的所有文章 |
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