基于量子行为鸽群优化的无人机紧密编队控制

doi:10.7527/S1000-6893.2020.23722

电子电气工程与控制

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基于量子行为鸽群优化的无人机紧密编队控制

徐博, 张大龙

哈尔滨工程大学自动化学院, 哈尔滨 150001

收稿日期:2019-12-12 修回日期:2020-01-06 出版日期:2020-08-15 发布日期:2020-02-06
通讯作者: 张大龙 E-mail:497986810@qq.com
基金资助:
国家自然科学基金（61203225，61633008）；中国博士后科学基金（2012M510083）；黑龙江省自然科学基金（QC2014C069）；装备发展部领域基金（61403110306）

Tight formation flight control of UAVs based on pigeon inspired algorithm optimization by quantum behavior

XU Bo, ZHANG Dalong

College of automation, Harbin Engineering University, Harbin 150001, China

Received:2019-12-12 Revised:2020-01-06 Online:2020-08-15 Published:2020-02-06
Supported by:
National Natural Science Foundation of China (61203225,61633008);China Postdoctoral Science Foundation (2012M510083);Natural Science Foundation of Heilongjiang Province (QC2014C069);Field Fund of Equipment Development Department (61403110306)

摘要/Abstract

摘要： 随着军事、民用需求的提高和相关领域的技术推动，无人机编队协同作业已成为当今人们逐渐关注的焦点。无人机紧密编队是指无人机间侧向距离在1~2倍翼展内的编队，因其可有效改善编队中无人机的气动性能而备受瞩目。基于无人机紧密编队条件下的气动耦合效应，建立三维空间下的状态空间方程描述双机相对运动，并推导了紧密编队条件下的双机最优编队构型，在此构型下将人工势场法和编队控制相结合作为控制系统中的间接控制环，并针对基本鸽群优化算法的寻优缺陷利用量子行为规则对其进行改进，将改进后的鸽群优化算法和无人机控制量结合作为控制系统中的直接控制环，最后通过仿真对比验证了此控制系统对于紧密编队控制的有效性。

关键词: 无人机, 紧密编队, 气动耦合, 人工势场, 改进鸽群优化

Abstract: UAV tight formation refers to the formation whose lateral distance between the UAVs is within one to two times of the wingspan. It has attracted considerable attention because of its effective improvement in the aerodynamic performance of UAVs in formation. In this paper, the aerodynamic coupling effect of the UAVs in tight formation is studied, and the state-space equation in three-dimensional space is established to describe the relative motion of two UAVs. The optimal formation configuration of two UAVs in close formation is deduced, where the combination of the artificial potential field method and formation control is used as the indirect control loop in the control system, and the basic pigeon inspired opbimization algorithm is calculated. The optimization defect of the algorithm is improved by quantum behavior rules. The improved opbimization pigeon inspired algorithm and the UAV control variables are combined as the direct control loop in the control system. Finally, the effectiveness of the control system is verified by simulation comparisons.

Key words: UAV, tight formation, aerodynamic coupling, artificial potential field, improved pigeon inspired opbimization

中图分类号:

V249.12

徐博, 张大龙. 基于量子行为鸽群优化的无人机紧密编队控制[J]. 航空学报, 2020, 41(8): 323722-323722.

XU Bo, ZHANG Dalong. Tight formation flight control of UAVs based on pigeon inspired algorithm optimization by quantum behavior[J]. ACTA AERONAUTICAET ASTRONAUTICA SINICA, 2020, 41(8): 323722-323722.

参考文献 23

[1]	JENNIFER L H, JAMES E M, NORMA V C. The NASA Dryden flight test approach to an aerial refueling system:NASA/TM-2005-212859[R]. Washington, D.C.:NASA, 2015.
[2]	BANGASH Z, SANCHEZ R, AHMED A, et al. Aerodynamics of formation flight[C]//42nd AIAA Aerospace Sciences Meeting and Exhibit. Reston:AIAA, 2004.
[3]	BUZOGANY L E, PACHTER M, AZZO J J D. Automated control of aircraft in formation flight[C]//Guidance, Navigation and Control Conference. Reston:AIAA, 1992.
[4]	PROUD A W, MEIR P, JOHN J D A. Close formation flight control:AIAA-1999-4207[R]. Reston:AIAA, 1999:1231-1246.
[5]	PACHTER M, D'SZZO J J, PROUD A W. Tight formation flight control[J]. Journal of Guidance, Control, and Dynamic, 2001, 24(2):246-254.
[6]	ZHANG Q R, LIU H H T. Robust nonlinear control of close formation flight[EB/OL]. (2019-04-16)[2019-12-01].https:arxiv.org/abs/1094.07479v1.
[7]	YU Z, QU Y, ZHANG Y. Safe control of trailing UAV in close formation flight against actuator fault and wake vortex effect[J]. Aerospace Science and Technology, 2018, 77(6):189-205.
[8]	牟勇飚. 无人机编队中的气动耦合问题研究[D]. 西安:西北工业大学, 2006:30-45. MOU Y B. Research on aerodynamic coupling in UAV formation[D]. Xi'an:Northwestern Polytechnical University, 2006:30-45(in Chinese).
[9]	赵锋, 杨伟, 杨朝旭. 无人机紧密编队飞行控制仿真研究[J]. 航空科学技术, 2012(5):18-21. ZHAO F, YANG W, YANG C X. Close formation flight control of UAVs[J]. Aeronautical Science Technology, 2012(5):18-21(in Chinese).
[10]	刘成功. 无人机仿生紧密编队飞行控制技术研究[D]. 南京:南京航空航天大学, 2009:21-33. LIU C G. Research on biomimetic close formation flight control of UAVs[D]. Nanjing:Nanjing University of Aeronautics and Astronautics, 2009:21-33(in Chinese).
[11]	万婧. 无人机自主编队飞行控制系统设计方法及应用研究[D]. 上海:复旦大学, 2009:39-63. WAN J. Research on design method and application of UAV autonomous formation flight control system[D]. Shanghai:Fudan University, 2009:39-63(in Chinese).
[12]	ENGELBRECHT A, 谭营. 计算群体智能基础[M]. 北京:清华大学出版社, 2009:1-15. ENGELBRECHT A, TAN Y. Fundamentals of computational swarm intelligence[M]. Beijing:Tsinghua University Press, 2009:1-15(in Chinese).
[13]	LIU C G, YAN X H, LIU C Y, et al. The wolf colony algorithm and its application[J]. Chinese Journal of Electronics, 2011, 20(2):210-216.
[14]	PAN W T. A new fruit fly optimization algorithm:Taking the financial distress model as an example[J]. Knowledge-Based Systems, 2012, 26:69-74.
[15]	KARABOGA D. An idea based on honey bee swarm for numerical optimization[D]. Erciyes:Erciyes University,2005.
[16]	DUAN H B, QIAO P. Pigeon-inspired optimization:A new swarm intelligence optimizer for air robot path planning[J]. International Journal of Intelligent Computing & Cybernetics, 2014, 7:24-37.
[17]	段海滨, 邱华鑫, 范彦铭. 基于捕食逃逸鸽群优化的无人机紧密编队协同控制[J]. 中国科学:技术科学, 2015, 45(6):559-572. DUAN H B, QIU H X, FAN Y M. Unmanned aerial vehicle close formation cooperative control based onpredatory escaping pigeon-inspired optimization[J]. Scientia Sinica Technologica, 2015, 45(6):559-572(in Chinese).
[18]	王晶, 顾维博, 窦立亚. 基于Leader-Follower的多无人机编队轨迹跟踪设计[J].航空学报,2020,41(S1):723728. WANG J, GU W B, DOU L Y. Leader-Follower formation control of multiple UAVs with trajectory tracking design[J]. Acta Aeronautica et Astronautica Sinica,2020,41(S1):723728(in Chinese).
[19]	张民, 夏卫政, 黄坤, 等. 基于Leader-Follower编队的无人机协同跟踪地面目标制导律设计[J]. 航空学报, 2018, 39(2):321497. ZHANG M, XIA W Z, HUANG K, et al. Guidance law for cooperative tracking of a ground target based on leader-follower formation of UAVs[J]. Acta Aeronautica et Astronautica Sinica, 2018, 39(2):321497(in Chinese).
[20]	杨宇. 多机器人编队群集运动控制的研究[D]. 武汉:华中科技大学, 2007:21-40. YANG Y. Research on formation and flocking control of multiple robots[D]. Wuhan:Huazhong University of Science and Technology, 2007:21-40(in Chinese).
[21]	孙俊. 量子行为粒子群优化:原理及其应用[M]. 北京:清华大学出版社, 2011:31-51. SUN J. Quantum-behaved PSO:Principles and applications[M]. Beijing:Tsinghua University Press, 2011:31-51(in Chinese).
[22]	周雨鹏. 基于鸽群算法的函数优化问题求解[D]. 长春:东北师范大学, 2016:6-23. ZHOU Y P. A pigeon-inspired algorithm for function optimization problems[D]. Changchun:Northeast Normal University, 2016:6-23(in Chinese).
[23]	郭瑞, 赵汝鑫, 吴海舟, 等. 具有收缩因子的自适应鸽群算法用于函数优化问题[J]. 物联网技术, 2017(5):91-94. GUO R, ZHAO R X, WU H Z, et al. Adaptive pigeon group algorithm with contraction factor for function optimization[J]. Internet of Things Technologies, 2017(5):91-94(in Chinese).

E-mail：hkxb@buaa.edu.cn

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基于量子行为鸽群优化的无人机紧密编队控制

Tight formation flight control of UAVs based on pigeon inspired algorithm optimization by quantum behavior

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