基于D3QN的无人机编队控制技术

赵启; 甄子洋; 龚华军; 曹红波; 李荣; 刘继承

doi:10.13700/j.bh.1001-5965.2021.0601

基于D3QN的无人机编队控制技术

doi: 10.13700/j.bh.1001-5965.2021.0601

南京航空航天大学自动化学院，南京 211100

基金项目: 国家自然科学基金(61973158)；南京航空航天大学研究生创新基地(实验室)开放基金项目(kfjj20200310，kfjj20200311)

详细信息

作者简介:
赵启男，硕士研究生。主要研究方向：无人机编队控制、强化学习

甄子洋男，博士，教授，博士生导师。主要研究方向：舰载机/无人机着舰引导与控制、无人机集群编队协同控制与决策

龚华军男，博士，教授，博士生导师。主要研究方向：先进飞行控制技术、飞行综合控制、系统建模与仿真

通讯作者:
E-mail：zhenziyang@nuaa.edu.cn

中图分类号: V249.1
计量
- 文章访问数: 518
- HTML全文浏览量: 47
- PDF下载量: 67
- 被引次数: 0
出版历程
- 收稿日期: 2021-10-10
- 录用日期: 2021-12-09
- 网络出版日期: 2021-12-30
- 整期出版日期: 2023-08-31

UAV formation control based on dueling double DQN

College of Automation Engineering，Nanjing University of Aeronautics and Astronautics，Nanjing 211100，China

Funds: National Natural Science Foundation of China (61973158); Postgraduate Research & Practice Innovation Program of Nanjing University of Aeronautics and Astronautics (kfjj20200310, kfjj20200311)

More Information

Corresponding author: E-mail：zhenziyang@nuaa.edu.cn

摘要

摘要:
针对无人机编队中控制器设计需要基于模型信息，以及无人机智能化程度低等问题，采用深度强化学习解决编队控制问题。针对编队控制问题设计对应强化学习要素，并设计基于深度强化学习对偶双重深度Q网络（D3QN）算法的编队控制器，同时提出一种优先选择策略与多层动作库结合的方法，加快算法收敛速度并使僚机最终能够保持到期望距离。通过仿真将设计的控制器与PID控制器、Backstepping控制器对比，验证D3QN控制器的有效性。仿真结果表明：该控制器可应用于无人机编队，提高僚机智能化程度，自主学习保持到期望距离，且控制器设计无需模型精确信息，为无人机编队智能化控制提供了依据与参考。
- 无人机编队 /
- 编队控制 /
- 深度强化学习 /
- 深度Q网络 /
- 对偶双重深度Q网络
Abstract:
To address issues such as the need for controller design based on model information in UAV formation and the low level of UVA intelligence, deep reinforcement learning is used to solve formation control problems. In this paper, the corresponding reinforcement learning elements are designed for the formation control problem, then a formation controller based on dueling double deep Q-network (D3QN) is designed. Moreover, to speed up the convergence of the algorithm and make the follower accurately keep the desired distance, a priority strategy is proposed and combined with a multi-layer action library. Finally, through simulation, the proposed controller is compared with the PID controller and the Backstepping controller to verify its effectiveness. The results show that the controller based on D3QN can be applied to formation flight of UAV, improve the intelligence degree of the follower, and keep the desired distance without the need for precise model information, which provides a basis and reference for UAV intelligent control.
- UAV formation /
- formation control /
- deep reinforcement learning /
- deep Q-network /
- dueling double deep Q-network

HTML全文

图 1 僚机跟随目标示意图

Figure 1. Learning target of follower

下载: 全尺寸图片幻灯片

图 2 D3QN编队控制结构

Figure 2. Control structure of D3QN formation control

下载: 全尺寸图片幻灯片

图 3 D3QN神经网络结构

Figure 3. Structure of D3QN neural network

下载: 全尺寸图片幻灯片

图 4 动作选择策略对比

Figure 4. Action selection strategy comparison

下载: 全尺寸图片幻灯片

图 5 D3QN与DQN平均奖励对比

Figure 5. D3QN and DQN average rewards comparison

下载: 全尺寸图片幻灯片

图 6 僚机状态变化曲线（长机平飞）

Figure 6. Change of states of follower (lead aircraft horizontal flight)

下载: 全尺寸图片幻灯片

图 7 长机飞行指令

Figure 7. Flight command of leader

下载: 全尺寸图片幻灯片

图 8 僚机状态变化曲线（长机变换）

Figure 8. Change of states of follower (lead aircraft change flight)

下载: 全尺寸图片幻灯片

表 1 参数设置

Table 1. Parameter setting

参数	数值
${\rm{ ep} }{{\rm{i}}_{\max } }$	10000
${\rm{Maxstep}}$	200
${\psi _{{\rm{max}}} }$	180
${\rm{batchsize}}$	128
$\varepsilon $	1→0.1
$\alpha $	0.0001
$n$	1
${ {\mu _1},{\mu _2} }$	50
${\tau _{\psi a}}$	0.919
${T_{\rm{s}}}$	0.5
${T_{\rm{d}}}$	200
$M$	1×10⁶
$\gamma $	0.95
$m$	8
$\tau $	0.9
${ {\sigma _1}，{\sigma _2}，{\sigma _3} }$	1
${ {\mu _3} }$	20
${\tau _{\psi b}}$	0.919

下载: 导出CSV

参考文献(24)

[1]	宗群, 王丹丹, 邵士凯, 等. 多无人机协同编队飞行控制研究现状及发展[J]. 哈尔滨工业大学学报, 2017, 49(3): 1-14. doi: 10.11918/j.issn.0367-6234.2017.03.001 ZONG Q, WANG D D, SHAO S K, et al. Research status and development of multi UAV coordinated formation flight control[J]. Journal of Harbin Institute of Technology, 2017, 49(3): 1-14(in Chinese). doi: 10.11918/j.issn.0367-6234.2017.03.001
[2]	DESAI J P, OSTROWSKI J, KUMAR V. Controlling formations of multiple mobile robots[C]//1998 IEEE International Conference on Robotics and Automation. Piscataway: IEEE Press, 2002: 2864-2869.
[3]	LEWIS M A, TAN K H. High precision formation control of mobile robots using virtual structures[J]. Autonomous Robots, 1997, 4(4): 387-403. doi: 10.1023/A:1008814708459
[4]	QIU H, DUAN H, FAN Y. Multiple unmanned aerial vehicle autonomous formation based on the behavior mechanism in pigeon flocks[J]. Control Theory & Applications, 2015, 32(10): 1298-1304.
[5]	LAMAN G. On graphs and rigidity of plane skeletal structures[J]. Journal of Engineering Mathematics, 1970, 4(4): 331-340. doi: 10.1007/BF01534980
[6]	OLFATI-SABER R, MURRAY R M. Consensus problems in networks of agents with switching topology and time-delays[J]. IEEE Transactions on Automatic Control, 2004, 49(9): 1520-1533. doi: 10.1109/TAC.2004.834113
[7]	邓婉, 王新民, 王晓燕, 等. 无人机编队队形保持变换控制器设计[J]. 计算机仿真, 2011, 28(10): 73-77. doi: 10.3969/j.issn.1006-9348.2011.10.018 DENG W, WANG X M, WANG X Y, et al. Controller design of UAVs formation keep and change[J]. Computer Simulation, 2011, 28(10): 73-77(in Chinese). doi: 10.3969/j.issn.1006-9348.2011.10.018
[8]	于美妍, 杨洪勇, 孙玉娇. 基于Backstepping的三轮机器人编队控制[J]. 复杂系统与复杂性科学, 2021, 18(3): 28-34. doi: 10.13306/j.1672-3813.2021.03.005 YU M Y, YANG H Y, SUN Y J. Formation control of three wheeled robots based on Backstepping[J]. Complex Systems and Complexity Science, 2021, 18(3): 28-34(in Chinese). doi: 10.13306/j.1672-3813.2021.03.005
[9]	周彬, 郭艳, 李宁, 等. 基于导向强化Q学习的无人机路径规划[J]. 航空学报, 2021, 42(9): 325109. doi: 10.7527/S1000-6893.2021.25109 ZHOU B, GUO Y, LI N, et al. Path planning of UAV using guided enhancement Q-learning algorithm[J]. Acta Aeronautica et Astronautica Sinica, 2021, 42(9): 325109(in Chinese). doi: 10.7527/S1000-6893.2021.25109
[10]	李波, 越凯强, 甘志刚, 等. 基于MADDPG的多无人机协同任务决策[J]. 宇航学报, 2021, 42(6): 757-765. doi: 10.3873/j.issn.1000-1328.2021.06.009 LI B, YUE K Q, GAN Z G, et al. Multi-UAV cooperative autonomous navigation based on multi-agent deep deterministic policy gradient[J]. Journal of Astronautics, 2021, 42(6): 757-765(in Chinese). doi: 10.3873/j.issn.1000-1328.2021.06.009
[11]	HUANG X, LUO W Y, LIU J R. Attitude control of fixed-wing UAV based on DDQN[C]//2019 Chinese Automation Congress (CAC). Piscataway: IEEE Press, 2020: 4722-4726.
[12]	HUNG S M, GIVIGI S N, NOURELDIN A. A Dyna-Q(Lambda) approach to flocking with fixed-wing UAVs in a stochastic environment[C]//2015 IEEE International Conference on Systems, Man, and Cybernetics. Piscataway: IEEE Press, 2016: 1918-1923.
[13]	WANG C, WANG J, SHEN Y, et al. Autonomous navigation of UAVs in large-scale complex environments: A deep reinforcement learning approach[J]. IEEE Transactions on Vehicular Technology, 2019, 68(3): 2124-2136. doi: 10.1109/TVT.2018.2890773
[14]	WANG C, YAN C, XIANG X, et al. A continuous actor-critic reinforcement learning approach to flocking with fixed-wing UAVs[C]//The Eleventh Asian Conference on Machine Learning. New York: PMLR, 2019, 101: 1-16.
[15]	HUNG S M, GIVIGI S N. A Q-learning approach to flocking with UAVs in a stochastic environment[J]. IEEE Transactions on Cybernetics, 2016, 47(1): 186-197.
[16]	IIMA H, KUROE Y. Swarm reinforcement learning methods improving certainty of learning for a multi-robot-formation problem[C]//2015 IEEE Congress on Evolutionary Computation (CEC). Piscataway: IEEE Press, 2015: 3026-3033.
[17]	相晓嘉, 闫超, 王菖, 等. 基于深度强化学习的固定翼无人机编队协调控制方法[J]. 航空学报, 2021, 42(4): 524009. XIANG X J, YAN C, WANG C, et al. Coordination control method for fixed-wing UAV formation through deep reinforcement learning[J]. Acta Aeronautica et Astronautica Sinica, 2021, 42(4): 524009(in Chinese).
[18]	PACHTER M, AZZO J J D, DARGAN J L. Automatic formation flight control[J]. Journal of Guidance, Control, and Dynamics, 1994, 17(6): 1380-1383.
[19]	王晓燕, 王新民, 肖亚辉, 等. 无人机三维编队飞行的鲁棒H_∞控制器设计[J]. 控制与决策, 2012, 27(12): 1907-1911. WANG X Y, WANG X M, XIAO Y H, et al. Design of robust H_∞ controller for UAVs three-dimensional formation flight[J]. Control and Decision, 2012, 27(12): 1907-1911(in Chinese).
[20]	MNIH V, KAVUKCUOGLU K, SILVER D, et al. Human-level control through deep reinforcement learning[J]. Nature (London), 2015, 518(7540): 529-533. doi: 10.1038/nature14236
[21]	VAN HASSELT H. Double Q-learning[C]//Advances in Neural Information Processing Systems. Cambridge: MIT Press, 2010: 2613-2621.
[22]	VAN HASSELT H, GUEZ A, SILVER D. Deep reinforcement learning with double Q-learning[C]//AAAI Conference on Artificial Intelligence. Palo Alto: AAAI, 2016: 2094-2100.
[23]	WANG Z Y, SCHAUL T, HESSEL M, et al. Dueling network architectures for deep reinforcement learning[C]//Proceedings of the 33rd International Conference on Machine Learning. New York: ACM, 2016: 1995-2003.
[24]	HU H A, WANG Q L. Proximal policy optimization with an integral compensator for quadrotor control[J]. Frontiers of Information Technology & Electronic Engineering, 2020, 21(5): 777-795.