Solution to continuous time Markov chain model for unmanned aerial vehicle swarm operation

被引：0
|
作者：
Huang S. [1 ]
Xie J. [1 ]
Wei D. [1 ]
Zhang Z. [1 ]
机构：
[1] Air and Missile Defense College, Air Force Engineering University, Xi'an
来源：
Guofang Keji Daxue Xuebao/Journal of National University of Defense Technology | 2022年 / 44卷 / 04期
关键词：
continuous time Markov chain; fourth-order Runge-Kutta method; phased modeling; row compressed storage; unmanned aerial vehicle swarm operation;
D O I：
10.11887/j.cn.202204005
中图分类号：
学科分类号：
摘要：
In order to solve the problem of low computing speed in the process of state transition in the analytical modeling of UAV (unmanned aerial vehicle) swarm operation, a fourth-order Runge-Kutta method based on the row compressed storage was proposed. The UAV swarm operation process was divided into three stages according to the UAV swarm operation style, and continuous time Markov chain model was established for the state transition process of UAV swarm operation in stages. In the meantime, taking the reliability of UAV swarm to complete combat mission as the solving index, the fourth-order Runge-Kutta method was used to solve the Markov model, and the method based on row compressed storage was used to optimize the solving rate owing to the sparsity feature of the rate transfer matrix. Simulation results show that the established continuous time Markov chain model has better effectiveness and feasibility than other models. At the same time, compared with other algorithms, the proposed algorithm has higher computing speed and better reliability requirements to meet the accuracy of results, which further shows the superiority of it. © 2022 National University of Defense Technology. All rights reserved.
引用
收藏
页码：43 / 51
页数：8
相关论文
共 26 条
  • [1] GILES K., A framework for integrating the development of swarm unmanned aerial system doctrine and design, (2017)
  • [2] CHANDLER P R, SWAROOP D, HOWLETT J K, Et al., Complexity in UAV cooperative control, (2004)
  • [3] LIAO C C, TAO W, LIU T., Collaborative task assignment of heterogeneous UAV based on improved contract network[J], Modern Computer, 15, pp. 100-107, (2021)
  • [4] ZHANG R P, FENG Y X, YANG Y K., Hybrid particle swarm algorithm for multi-UAV cooperative task allocation, Acta Aeronautica Astronautica Sinica
  • [5] GU X P, TANG D Q., Multi-heterogeneous UAV task planning based on bacterial foraging algorithm[J], Systems Engineering and Electronics, 43, 11, pp. 3312-3320, (2021)
  • [6] ZHAO C, LIU Y G, CHEN L, Et al., Research and development trend of multi-UAV path planning based on metaheuristic algorithm[J], Control and Decision, 37, 5, pp. 1102-1115, (2022)
  • [7] ZHANG F Z, ZHU Y Q., Task allocation method for multi-UAV cooperative reconnaissance in complex environment[J/OL], Journal of System Simulation
  • [8] ZOU L Y, ZHANG M Z, BAI J R, Et al., A survey of modeling and simulation of UAS swarm operation[J], Tactical Missile Technology, 3, pp. 98-108, (2021)
  • [9] JIN Y D, FENG J B, ZHANG W J., UAV task allocation for hierarchical multiobjective optimization in complex conditions using modified NSGA-III with segmented encoding[J], Journal of Shanghai Jiaotong University (Science), 26, 4, pp. 431-445, (2021)
  • [10] WANG B H, WU T Y, LI W H, Et al., Large-scale UAVs confrontation based on multi-agent reinforcement learning[J], Journal of System Simulation, 33, 8, pp. 1739-1753, (2021)
123