A journal of IEEE and CAA , publishes high-quality papers in English on original theoretical/experimental research and development in all areas of automation
Advanced Search
Volume 8 Issue 4
Apr.  2021

IEEE/CAA Journal of Automatica Sinica

  • JCR Impact Factor: 15.3, Top 1 (SCI Q1)
    CiteScore: 23.5, Top 2% (Q1)
    Google Scholar h5-index: 77， TOP 5
Turn off MathJax
Article Contents
Article Navigation >  IEEE/CAA Journal of Automatica Sinica  > 2021  >  8(4): 890-904
Hongbo Zhao, Yongming Wen, Sentang Wu, and Jia Deng, "Dynamic Evaluation Strategies for Multiple Aircrafts Formation Using Collision and Matching Probabilities," IEEE/CAA J. Autom. Sinica, vol. 8, no. 4, pp. 890-904, Apr. 2021. doi: 10.1109/JAS.2020.1003198
Citation: Hongbo Zhao, Yongming Wen, Sentang Wu, and Jia Deng, "Dynamic Evaluation Strategies for Multiple Aircrafts Formation Using Collision and Matching Probabilities," IEEE/CAA J. Autom. Sinica, vol. 8, no. 4, pp. 890-904, Apr. 2021. doi: 10.1109/JAS.2020.1003198
shu

Dynamic Evaluation Strategies for Multiple Aircrafts Formation Using Collision and Matching Probabilities

doi: 10.1109/JAS.2020.1003198
  • 1.

    Beihang University, Beijing 100191, China

  • 2.

    Science and Technology on Information Systems Engineering Laboratory, Beijing Institute of Control & Electronics Technology, Beijing 100038, China

Funds:  This work was supported by the Industrial Technology Development Program (B1120131046)
More Information
    Abstract
  • Configuration evaluation is a key technology to be considered in the design of multiple aircrafts formation (MAF) configurations with high dynamic properties in engineering applications. This paper deduces the relationship between relative velocity, dynamic safety distance and dynamic adjacent distance of formation members, then divides the formation states into collision-state and matching-state. Meanwhile, probability models are constructed based on the binary normal distribution of relative distance and relative velocity. Moreover, configuration evaluation strategies are studied by quantitatively analyzing the denseness and the basic capabilities according to the MAF collision-state probability and the MAF matching-state probability, respectively. The scale of MAF is grouped into 5 levels, and previous lattice-type structures are extended into four degrees by taking the relative velocities into account to instruct the configuration design under complex task conditions. Finally, hardware-in-loop (HIL) simulation and outfield flight test results are presented to verify the feasibility of these evaluation strategies.

     

    • FullText(HTML)
  • loading
    • References(41)
  • [1]
    S. T. Wu, Cooperative Guidance & Control of Missile Autonomous Formation. Beijing: National Defense Industry Press, 2015.
    [2]
    S. T. Wu, Cooperative Flight Control System. Beijing: Science Press, Jun. 2018.
    [3]
    W. Zha, J. Chen, and Z. Peng, “Dynamic multi-team antagonistic games model with incomplete information and its application to multi-UAV,” IEEE/CAA J. Autom. Sinica, vol. 2, no. 1, pp. 74–84, Jan. 2015. doi: 10.1109/JAS.2015.7032908
    [4]
    Y. Hong, J. Hu, and L. Gao, “Tracking control for multi-agent consensus with an active leader and variable topology,” Automatica, vol. 42, no. 7, pp. 1177–1182, 2007.
    [5]
    X. Zhang, X. Liu, and Z. Feng, “Distributed containment control of singular heterogeneous multi-agent systems,” J. Franklin Institute-Engineering and Applied Mathematics, vol. 357, no. 3, pp. 1378–1399, 2020.
    [6]
    Q. Ali and S. Montenegro, “Explicit model following distributed control scheme for formation flying of mini UAVs,” IEEE Access, vol. 4, pp. 397–406, 2016. doi: 10.1109/ACCESS.2016.2517203
    [7]
    P. K. C. Wang, “Navigation strategies for multiple autonomous mobile robots moving in formation,” J. Robot. Syst., vol. 8, pp. 177–195, 1991.
    [8]
    W. Ni and D. Cheng, “Leader-following consensus of multi-agent systems under fixed and switching topologies,” Syst. Control Lett., vol. 59, no. 3–4, pp. 209–217, 2010.
    [9]
    G. Notarstefano, M. Egerstedt, and M. Haque, “Containment in leader-follower networks with switching communication topologies,” Automatica, vol. 47, no. 5, pp. 1035–1040, 2011. doi: 10.1016/j.automatica.2011.01.077
    [10]
    D. Herrera, F. Roberti, M. Toibero, and R. Carelli, “Human interaction dynamics for its use in mobile robotics: Impedance control for leader-follower formation,” IEEE/CAA J. Autom. Sinica, vol. 4, no. 4, pp. 696–703, Oct. 2017. doi: 10.1109/JAS.2017.7510631
    [11]
    M. A. Lewis and K. H. Tan, “High precision formation control of mobile robots using virtual structures,” Autonomous Robots, vol. 4, no. 4, pp. 387–403, 1997.
    [12]
    N. Ganganath, C. T. Cheng, X. F. Wang, and C. Tse, “Community-based informed agents selection for flocking with a virtual leader,” Int. J. Control Automation and Systems, vol. 15, pp. 394–403, Feb. 2017. doi: 10.1007/s12555-015-0170-4
    [13]
    R. C. Arkin and T. Balch, “Behavior-based formation control for multirobot teams,” IEEE Trans. Robot. Autom., vol. 14, pp. 926–939, 1998. doi: 10.1109/70.736776
    [14]
    D. D. Xu, X. N. Zhang, Z. Q. Zhu, C. L. Chen, and P. Yang, “Behavior-based formation control of swarm robots,” Mathematical Problems in Engineering, Article No. 2014. DOI: 10.1155/2014/205759
    [15]
    G. Antonelli, F. Arrichiello, and S. Chiaverini, “Experiments of formation control with multirobot systems using the nullspace-based behavioral control,” IEEE Trans. Control Syst. Technol., vol. 17, no. 5, pp. 1173–1182, Apr. 2009. doi: 10.1109/TCST.2008.2004447
    [16]
    C. Du, X. Liu, W. Ren, P. Lu, and H. Liu, “Finite-time consensus for linear multi-agent systems via event-triggered strategy without continuous communication,” IEEE Trans. Control of Network Systems, vol. 7, no. 1, Article No. 2020.
    [17]
    Q. Liu, T. Zhou, S. Guo, Z. Wang, D. Wang, and W. Wang, “Distributed containment control of multi-agent systems under asynchronous switching and stochastic disturbances,” IET Control Theory &Applications, vol. 13, no. 8, pp. 1105–1112, 2019.
    [18]
    C. Huang, G. S. Zhai, and G. S. Xu, “Necessary and sufficient conditions for consensus in third order multi-agent systems,” IEEE/CAA J. Autom. Sinica, vol. 5, no. 6, pp. 1044–1053, Nov. 2018. doi: 10.1109/JAS.2018.7511222
    [19]
    J. Jin and N. Gans, “Collision-free formation and heading consensus of nonholonomic robots as a pose regulation problem,” Robotics and Autonomous Systems, vol. 95, pp. 25–36, 2017. doi: 10.1016/j.robot.2017.05.008
    [20]
    P. Anton and T. Roberto, “A tutorial on modeling and analysis of dynamic social networks. Part II,” Annual Reviews in Control, vol. 45, pp. 166–190, Mar. 2018. doi: 10.1016/j.arcontrol.2018.03.005
    [21]
    W. He and Y. Dong, “Adaptive fuzzy neural network control for a constrained robot using impedance learning,” IEEE Trans. Neural Networks and Learning Systems, vol. 29, no. 4, pp. 1174–1186, 2017.
    [22]
    H. J. Gao, W. He, C. Zhou, and C. Y. Sun, “Neural network control of a two-link flexible robotic manipulator using assumed mode method,” IEEE Trans. Industrial Informatics, vol. 15, no. 2, pp. 755–765, 2018.
    [23]
    Y. Wu and R. Lu, “Event-based control for network systems via integral quadratic constraints,” IEEE Trans. Circuits and Systems I: Regular Papers, vol. 65, no. 4, pp. 1386–1394, 2018.
    [24]
    Y. Wu, R. Lu, P. Shi, H. Su, and Z. Wu, “Sampled-data synchronization of complex networks with partial couplings and T-S fuzzy nodes,” IEEE Trans. Fuzzy Systems, vol. 26, no. 2, pp. 782–793, Apr. 2018. doi: 10.1109/TFUZZ.2017.2688490
    [25]
    A. Wang, X. Liao, and H. He, “Event-triggered differentially private average consensus for multi-agent network,” IEEE/CAA J. Autom. Sinica, vol. 6, no. 1, pp. 75–83, Jan. 2019. doi: 10.1109/JAS.2019.1911327
    [26]
    C. Viel, S. Bertrand, M. Kieffer, and H. Piet-Lahanier, “Distributed event-triggered control strategies for multi-agent formation stabilization and tracking,” Automatica, vol. 106, pp. 110–116, 2019. doi: 10.1016/j.automatica.2019.04.024
    [27]
    X.-X. Yang, M. Lei, S.-L. Zhou, and G.-Y. Yin, “Complex formation control of large-scale intelligent autonomous vehicles,” Mathematical Problems in Engineering, vol. 2012, no. 6, Article No. 2012. doi: 10.1155/2012/241916
    [28]
    D. B. Wilson, A. H. Goktogan, and S. Sukkarieh, “Vision-aided guidance and navigation for close formation flight,” J. Field Robotics, vol. 33, pp. 661–686, Aug. 2016. doi: 10.1002/rob.21637
    [29]
    D. Li, S. S. Ge, W. He, G. Ma, and L. Xie, “Multilayer formation control of multi-agent systems,” Automatica, vol. 109, Article No. 2019.
    [30]
    H. Liu, Y. Tian, F. L. Lewis, Y. Wan, and K. P. Valavanis, “Robust formation tracking control for multiple quadrotors under aggressive maneuvers,” Automatica, vol. 105, pp. 179–185, 2019. doi: 10.1016/j.automatica.2019.03.024
    [31]
    Q. Wang, Y. Wang, and H. Zhang, “The formation control of multi-agent systems on a circle,” IEEE/CAA J. Autom. Sinica, vol. 5, no. 1, pp. 148–154, Jan. 2018. doi: 10.1109/JAS.2016.7510022
    [32]
    P. Gurfil and E. Kivelevitch, “Flock properties effect on task assignment and formation flying of cooperating unmanned aerial vehicles,” Proc. the Institution of Mechanical Engineers Part G-J. Aerospace Engineering, vol. 221, pp. 401–418, Jun. 2007. doi: 10.1243/09544100JAERO120
    [33]
    G. Dudek, M. R. M. Jenkin, E. Milios, and D. Wilkes, “A taxonomy for multi-agent robotics,” Autonomous Robots, vol. 3, pp. 375–397, 1996.
    [34]
    R. Olfati-Saber, “Flocking for multi-agent dynamic systems: Algorithms and theory,” IEEE Trans. Automatic Control, vol. 51, pp. 401–420, Mar. 2006. doi: 10.1109/TAC.2005.864190
    [35]
    Q. Yuan, J. Zhan, and X. Li, “Outdoor flocking of quadcopter drones with decentralized model predictive control,” ISA Transactions, vol. 71, no. 1, pp. 84–92, 2017.
    [36]
    M. Khalili, X. Zhang, M. A. Gilson, and Y. Cao, “Distributed fault-tolerant formation control of cooperative mobile robots,” IFAC-PapersOnLine, vol. 51, no. 24, pp. 459–464, 2018. doi: 10.1016/j.ifacol.2018.09.617
    [37]
    Z. Yao, S. T. Wu, and Y. M. Wen, “Formation generation for multiple unmanned vehicles using multi-agent hybrid social cognitive optimization based on the internet of things,” Sensors (Basel,Switzerland), vol. 19, no. 7, Article No. 2019.
    [38]
    Y. Wen, S. Wu, W. Liu, J. Deng, and X. Wu, “A collision forecast and coordination algorithm in configuration control of missile autonomous formation,” IEEE Access, vol. 5, pp. 1188–1199, 2017. doi: 10.1109/ACCESS.2017.2652984
    [39]
    H. Zhao, S. Wu, Y. Wen, W. Liu, and X. Wu, “Modeling and flight experiments for swarms of high dynamic UAVs: A stochastic configuration control system with multiplicative noises,” Sensors, vol. 19, Article No. 2019.
    [40]
    C. W. Reynolds, “Flocks, herds, and schools: A distributed behavioral model,” ACM SIGGRAPH Computer Graphics, vol. 21, no. 4, pp. 25–34, 1987. doi: 10.1145/37402.37406
    [41]
    Y. Q. Zhou, Stochastic Processes Theory. Beijing: Beihang University Press, 2013.
    • Related Articles
    • Supplements(0)

Catalog

    通讯作者: 陈斌, bchen63@163.com
    • 1. 

      沈阳化工大学材料科学与工程学院 沈阳 110142

    1. 本站搜索
    2. 百度学术搜索
    3. 万方数据库搜索
    4. CNKI搜索

    Figures(13)  / Tables(6)

    Get Citation
    PDF
    XML

    Article Metrics

    Article views (986) PDF downloads(55) Cited by()

    Highlights

    • This paper focuses on quantitative configuration evaluation strategies for multiple aircrafts formation (MAF) systems based on collision probability and matching probability.
    • Probability models are constructed based on the binary normal distribution of relative distance and relative velocity. The formation states are divided into collision-state, matching-state and isolation-state based on the collision probability and matching probability.
    • Configuration evaluation strategies are studied by quantitatively analyzing the denseness and the basic capabilities according to the MAF collision-state probability and the MAF matching-state probability, respectively.
    • The scale of MAF is grouped into 5 levels, and previous lattice-type structures are extended into four degrees by taking the relative velocities into account to instruct the configuration design under complex task conditions.
    • Hardware-in-loop (HIL) simulation and outfield flight test results are presented to verify the feasibility of these evaluation strategies

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return
    Return