计算机科学 ›› 2021, Vol. 48 ›› Issue (6): 288-295.doi: 10.11896/jsjkx.201000137

• 计算机网络 • 上一篇    下一篇

基于矩阵论的一致性控制算法收敛速度分析

黄鑫权, 刘爱军, 梁小虎, 王桁   

  1. 陆军工程大学通信工程学院天基信息系统教研室 南京210007
  • 收稿日期:2020-10-25 修回日期:2020-12-22 出版日期:2021-06-15 发布日期:2021-06-03
  • 通讯作者: 刘爱军(liuaj.cn@163.com)
  • 基金资助:
    国家自然科学基金(61671476,61901516);江苏省自然科学基金(BK20180578);中国博士后科学基金(2019M651648)

Matrix Theory Aided Convergence Analysis of Consensus Behavior in FANET with Beacon Loss

HUANG Xin-quan, LIU Ai-jun, LIANG Xiao-hu, WANG Heng   

  1. Department of Space-based Information System,College of Communication Engineering,Army Engineering University,Nanjing 210007,China
  • Received:2020-10-25 Revised:2020-12-22 Online:2021-06-15 Published:2021-06-03
  • About author:HUANG Xin-quan,born in 1993,postgraduate.His main research interests include multi-agent systems and flying ad-hoc network.(huangxinquan1993@sina.com)
    LIU Ai-jun,born in 1970,professor.His main research interests include satellite communication system theory,signal processing,channel coding and information theory.
  • Supported by:
    National Natural Science Foundation of China(61671476, 61901516),Natural Science Foundation of Jiangsu Province of China(BK20180578) and China Postdoctoral Science Foundation(2019M651648).

摘要: 空中自组网(Flying Ad-Hoc Network,FANET)是支撑无人机集群系统(Unmanned Aerial Vehicle Swarm,UAV swarm)的关键技术,它由数量庞大且具有无线通信能力的小型无人机构成。FANET中的信标帧业务在实现集群一致性控制应用的过程中扮演着重要角色。然而,实际应用中FANET无线链路的不可靠性将会导致信标帧出现丢包现象,进而影响一致性控制算法的收敛速度(或收敛时间),即集群所有状态值趋于一致的快慢程度。从理论上分析一致性控制算法收敛性能与信标帧丢包率之间的解析关系,对一致性控制算法在未来FANET中的应用具有举足轻重的意义。针对上述研究背景,文中提出了一种基于随机有向图模型和矩阵论的收敛性能分析模型。该模型将每个周期内FANET中的信息流抽象为随机有向图,并采用指示矩阵来表示该随机有向图的拉普拉斯矩阵,有效地用矩阵多项式对一致性收敛过程进行建模。随后,基于矩阵运算和矩阵谱半径的相关知识,该模型给出了最终期望收敛值的解析表达式。利用该最终期望收敛值,所提模型定义了新的收敛速度量化方法。与现有收敛速度分析工作不同,文中通过评估所有节点的初始状态值收敛到期望收敛值的快慢来对收敛速度进行量化,而不是根据收敛到每个周期网络的平均状态值来进行量化。基于矩阵运算和矩阵谱半径相关知识,所提模型给出了该收敛速度与信标帧丢包率之间的耦合关系,并根据该耦合关系推导出了收敛时间的表达式。仿真结果表明,所提收敛性能分析模型能够准确地描述实际FANET中收敛速度随时间的变化情况。此外,该模型能够准确描述实际FANET中每条链路的平均丢包率、状态值初始分布以及无人机节点个数的变化趋势对收敛时间的影响。同时,相比现有收敛性能分析模型,所提模型得到的收敛性能曲线更接近实际FANET中的收敛性能曲线。

关键词: 空中自组网, 收敛时间, 收敛速度, 随机有向图, 一致性控制算法

Abstract: Flying Ad-Hoc Network(FANET) which is a field of wireless Ad-Hoc network formed by small unmanned aerial vehicles(UAVs),is critical in achieving UAV swarm system.Beacon mechanism in FANET plays the fundamental role in performing consensus behavior of UAV swarm.However,the wireless link failures of practical FANET will introduce beacon loss which will affect the convergence rate or convergence time,which describe how fast all UAV states reach the common value.To achieve optimal consensus behavior,it is important to know how beacon mechanism affects the consensus behavior.To solve above-mentioned problem,this paper has investigated the analytical relation between the convergence rate/time of consensus behavior and the beacon loss probability.In the analytical work,information flow topology at each period is modeled by random directed graph,and one indicator matrix weighted is designed to model the Laplacian matrix of the graph.Based on the knowledge of matrix theory and spectrum radius of a matrix,the analytical work in this paper firstly gives an analytical expression of expected consensus va-lue of the consensus process.Utilizing the expected consensus value,a novel quantification of convergence rate based on the expected final consensus value is provided.Different from existing analytical work,the convergence rate is quantified based on the expected consensus value,rather than to the average value of all states at each period.Finally,utilizing the knowledge of matrix theory and spectrum radius of a matrix,the proposed analytical work analyzes the relation between the convergence rate/time and the beacon loss probability.Simulation results show that,the proposed analytical model can accurately capture the convergence rate along with time in practical FANETs.Moreover,the proposed model can accurately capture the effect of average link failure probability on each link,initial state distribution and the number of UAVs on the convergence time.Moreover,compared with exis-ting analytical model,the proposed analytical model can capture the convergence performance in practical FANET more precisely.

Key words: Consensus control algorithm, Convergence rate, Convergence time, Flying Ad-Hoc Network, Random directed graph

中图分类号: 

  • TN927
[1]KURIKI Y,NAMERIKAWA T.Consensus-based cooperativeformation control with collision avoidance for a multi-UAV system[C]//Proc.American Control Conf.(ACC’14).2014:2077-2082.
[2]REN W,BEARD R W,ATKINS E M.Information consensus in multivehicle cooperative control[J].IEEE Control Systems Magazine,2007,27(2):71-82.
[3]XU W,DUAN F Y,ZHANG Q J,et al.A new fast consensus algorithm applied in rendezvous of multi-UAV[C]//Proc.Chinese Control and Decision Conf.(CCDC’15).2015:55-60.
[4]SEMNANI S H,BASIR O A.Semi-flocking algorithm for motion control of mobile sensors in large-scale surveillance systems[J].IEEE Transactions on Cybernetics,2015,45(1):129-137.
[5]GARIN F,SCHENATO L.A survey on distributed estimation and control applications using linear consensus algorithms[M].Networked Control Systems.Springer,2010:75-107.
[6]LIU Q,WANG Z,HE X,et al.On Kalman-consensus filtering with random link failures over sensor networks[J].IEEE Transactions on Automatic Control,2018,63(8):2701-2708.
[7]BEKMEZCI I,SAHINGOZ O K,TEMEL C.Flying ad-hoc networks(FANETs):A survey[J].Ad-Hoc Networks,2013,11(3):1254-1270.
[8]FAGNANI F,ZAMPIERI S.Average consensus with packetdrop communication[J].SIAM Journal on Control and Optimization,2009,48(1):102-133.
[9]ZHOU J,WANG Q.Characterizing convergence speed for consensus seeking over dynamically switching directed random networks[C]//Proc.American Control Conference.2009:629-634.
[10]HATANO Y,MESBAHI M.Agreement over random networks[J].IEEE Transactions on Automatic Control,2005,50(11):1867-1872.
[11]HALE M T,EGERSTEDT M.Convergence rate estimates for consensus over random graphs[C]//Proc.American Control Conf.(ACC’17),2017:1024-1029.
[12]PATTERSON S,BAMIEH B,EL ABBADI A.Convergencerates of distributed average consensus with stochastic link fai-lures[J].IEEE Transactions on Automatic Control,2010,55(4):880-892.
[13]SU L.On the Convergence Rate of Average Consensus and Distributed optimization over Unreliable Networks[C]//2018 52nd Asilomar Conference on Signals,Systems,and Computers.2018:43-47.
[14]SABER R O,MURRAY R M.Consensus protocols for networks of dynamic agents[C]//Proc.American Control Conference.2003:951-956.
[15]CHEN Y,QI D,ZHANG J,et al.Study on Distributed Dynamic Average Consensus Algorithm[C]//2019 7th International Conference on Information,Communication and Networks(ICICN).2019:225-229.
[16]YI J,CHAI L,ZHANG J.Average Consensus by Graph Filtering:New Approach,Explicit Convergence Rate,and Optimal Design[J].IEEE Transactions on Automatic Control,2020,65(1):191-206.
[17]ASADI M M,KHOSRAVI M,AGHDAM A G,et al.Expected Convergence Rate to Consensus in Asymmetric Networks:Analy-sis and Distributed Estimation[J].IEEE Transactions on Systems,Man,and Cybernetics:Systems,2020,50(3):972-987.
[18]DAS A,YI Y,PATTERSON S,et al.Convergence Rate of Consensus in a Network of Networks[C]//2018 IEEE Conference on Decision and Control(CDC).2018:459-465.
[19]DONG X,YU B,SHI Z,et al.Time-varying formation control for unmanned aerial vehicles:Theories and applications[J].IEEE Transactions on Control Systems Technology,2015,23(1):340-348.
[20]XIAO L,BOYD S,LALL S.A scheme for robust distributed sensor fusion based on average consensus[C]//Proc.Fourth International Symposium on Information Processing in Sensor Networks.2005:63-70.
[21]REN W,BEARD R W,ATKINS E M.A survey of consensus problems in multi-agent coordination[C]//Proc.American Control Conference.2005:1859-1864.
[22]XIAO L,BOYD S.Fast linear iterations for distributed averaging[J].Systems & Control Letters,2004,53(1):65-78.
[23]REN W,BEARD R W.Consensus seeking in multiagent systems under dynamically changing interaction topologies[J].IEEE Transactions on Automatic Control,2005,50(5):655-661.
[24]BHATIA R.Matrix analysis[M].Springer Science\& Business Media,2013.
[25]OLFATI-SABER R.Flocking for multi-agent dynamic systems:Algorithms and theory[J].IEEE Transactions on Automatic Control,2006,51(3):401-420.
[1] 刘华玲, 皮常鹏, 刘梦瑶, 汤新.
一种新的优化机制:Rain
New Optimization Mechanism:Rain
计算机科学, 2021, 48(11A): 63-70. https://doi.org/10.11896/jsjkx.201100032
[2] 刘加存, 赵桂艳, 梅其祥.
迭代学习控制的最优学习律和简化学习律的频域研究
Study of Optimal Learning Law and Simplified Learning Law of Iterative Learning Control in Frequency Domain
计算机科学, 2019, 46(2): 327-332. https://doi.org/10.11896/j.issn.1002-137X.2019.02.050
[3] 陈晋音,胡可科,李玉玮.
基于MB-RRT*的无人机多点航迹规划算法研究
Research on UAV Multi-point Navigation Algorithm Based on MB-RRT*
计算机科学, 2018, 45(6A): 85-90.
[4] 张悦,孙惠香,魏政磊,韩博.
具有自适应调整策略的混沌灰狼优化算法
Chaotic Gray Wolf Optimization Algorithm with Adaptive Adjustment Strategy
计算机科学, 2017, 44(Z11): 119-122. https://doi.org/10.11896/j.issn.1002-137X.2017.11A.024
[5] 陈晋音,李玉玮,杜文耀.
基于EB-RRT*的无人机航迹规划算法研究
UAV Navigation Algorithm Research Based on EB-RRT*
计算机科学, 2017, 44(Z11): 72-79. https://doi.org/10.11896/j.issn.1002-137X.2017.11A.014
[6] 陈晋音,施晋,杜文耀,吴洋洋.
基于MB-RRT*的无人机航迹规划算法研究
MB-RRT* Based Navigation Planning Algorithm for UAV
计算机科学, 2017, 44(8): 198-206. https://doi.org/10.11896/j.issn.1002-137X.2017.08.035
[7] 熊聪聪,郝璐萌,王丹,邓雪晨.
一种基于差分策略的群搜索优化算法
Group Search Optimizer Based on Differential Strategies
计算机科学, 2017, 44(2): 250-256. https://doi.org/10.11896/j.issn.1002-137X.2017.02.041
[8] 耿宗科,王长宾,张振国.
基于模糊c-means与自适应粒子群优化的模糊聚类算法
Fuzzy c-means and Adaptive PSO Based Fuzzy Clustering Algorithm
计算机科学, 2016, 43(8): 267-272. https://doi.org/10.11896/j.issn.1002-137X.2016.08.054
[9] 王浩光,余世明.
求解车辆路径问题的改进伊藤算法
Improved ITO Algorithm for Solving VRP
计算机科学, 2015, 42(9): 253-256. https://doi.org/10.11896/j.issn.1002-137X.2015.09.049
[10] 陈海平,张杭,路威,杨柳,周轩.
时变混合系统的自适应动量项快速盲源分离算法
Adaptive Momentum Fast Blind Source Separation Algorithm for Time-varying Mixing System
计算机科学, 2013, 40(Z11): 15-17.
[11] 王得洋,王从银,庄雷,陈鸿昶.
通信网络中一种基于流的异步平均一致性协议
Flow-based Asynchronous Averaging Consensus Protocol on Communication Network
计算机科学, 2013, 40(7): 44-48.
[12] 黄泽霞,黄德才,俞攸红.
动态控制场下一种改进的量子最优控制
Improved Quantum Optimal Control in Dynamic Control Field
计算机科学, 2013, 40(1): 233-235.
[13] 谢振华 李宁 商琳 陈兆乾 陈世福.
一种新型的神经网络构造方法RCBNN

计算机科学, 2005, 32(8): 166-169.
[14] 吴作顺 王石.
一个SPEA改进算法及其收敛性分析

计算机科学, 2005, 32(4): 74-76.
[15] 高鹰 谢胜利.
混沌粒子群优化算法

计算机科学, 2004, 31(8): 13-15.
Viewed
Full text


Abstract

Cited

  Shared   
  Discussed   
No Suggested Reading articles found!