access icon free Topology optimised fixed-time consensus for multi-UAV system in a multipath fading channel

Time-varying connectivity is one of main challenges faced by controlling a team of unmanned aerial vehicles (UAVs) in the multipath fading channel, incurring low accuracy and significant convergence time of formation control law. To address this issue, in this study, a topology optimised based decentralised consensus is developed for controlling a multi-UAV system in a multipath fading channel, in which a formation structure reconfiguration scheme is proposed as well as a transmission power allocating algorithm to guarantee the control accuracy in a limited convergence time. In particular, the objective function for topology optimisation is well-designed by considering the second eigenvalue of Laplacian matrix of topology as a feasible index of connectivity degree. To improve the efficiency of information transmission, a specified consensus protocol is proposed with well-tailored packet format and signalling procedure for control messages. Through the comparative simulation results, the proposed consensus can achieve high convergence accuracy and less convergence time in a multipath fading channel, indicating high resilience of the proposed protocol under a multipath fading channel.

Inspec keywords: multipath channels; eigenvalues and eigenfunctions; autonomous aerial vehicles; fading channels; multi-robot systems; resource allocation; mobile robots; protocols

Other keywords: signalling procedure; fixed-time consensus; decentralised consensus; time-varying connectivity; consensus protocol; packet format; information transmission; objective function; second eigenvalue; multipath fading channel; topology optimisation; unmanned aerial vehicles; topology optimised fixed-time consensus; formation structure reconfiguration; formation control law; Laplacian matrix; multiUAV system; transmission power allocating algorithm; convergence time

Subjects: Protocols; Robotics; Radio links and equipment; Linear algebra (numerical analysis); Linear algebra (numerical analysis); Protocols

References

    1. 1)
      • 17. Han, Z., Swindlehurst, A.L., Liu, K.J.R.: ‘Optimization of MANET connectivity via smart deployment/movement of unmanned air vehicles’, IEEE Trans. Veh. Technol., 2009, 58, (7), pp. 35333546.
    2. 2)
      • 25. Dela, J.C., Magwili, G.V., Mundo, J.P.E., et al: ‘Items-mapping and route optimization in a grocery store using Dijkstra's, Bellman-Ford and Floyd-Warshall Algorithms’. 2016 IEEE Region 10 Conf. (TENCON), Singapore, 2016, pp. 243246.
    3. 3)
      • 15. Qiang, W., Yu, W.: ‘Fixed-time consensus of multi-agent systems over undirected networks’. 2016 35th Chinese Control Conf. (CCC), Chengdu, 2016, pp. 77757779.
    4. 4)
      • 19. Chen, S., Lv, J.: ‘A study of the performance of hybrid DS/FH spread spectrum systems under UAV fading channel’. 2009 First Int. Conf. on Information Science and Engineering, Nanjing, 2009, pp. 25142517.
    5. 5)
      • 12. Yi, S., Yanyan, Y., Chenchen, L., et al: ‘Consensus for heterogenous multi-agent systems with second-order linear and nonlinear dynamics’. 2018 Chinese Control and Decision Conf. (CCDC), Shenyang, 2018, pp. 60686071.
    6. 6)
      • 23. Polyakov, A.: ‘Nonlinear feedback design for fixed-time stabilization of linear control systems’, IEEE Trans. Autom. Control, 2012, 8, (57), pp. 21062110.
    7. 7)
      • 10. Olfati-Saber, R., Fax, J.A., Murray, R.M.: ‘Consensus and cooperation in networked multi-agent systems’, Proc. IEEE, 2007, 95, (1), pp. 215233.
    8. 8)
      • 21. Dörfler, F., Simpson-Porco, J.W., Bullo, F.: ‘Electrical networks and algebraic graph theory: models, properties, and applications’, Proc. IEEE, 2018, 5, (106), pp. 9771005.
    9. 9)
      • 3. Mohammad, M., Walid, S., Mehdi, B., et al: ‘Drone small cells in the clouds: design, deployment and performance analysis’. IEEE Global Communications Conf. (GLOBECOM), San Diego, CA, USA, 2015, vol. 12, no. 46, pp. 16.
    10. 10)
      • 6. Aghdam, A.S., Menhaj, M.B., Barazandeh, F., et al: ‘Cooperative load transport with movable load center of mass using multiple quadrotor UAVs’. 2016 4th Int. Conf. on Control, Instrumentation, and Automation (ICCIA), Qazvin, 2016, vol. 11, pp. 2327.
    11. 11)
      • 9. Yang, G., Yang, Q., Kapila, V., et al: ‘Fuel optimal manoeuvres for multiple spacecraft formation reconfiguration using multi-agent optimization’, Int. J. Robust Nonlinear Control, 2002, 12, (2–3), pp. 243283.
    12. 12)
      • 24. Shengxuan, W., Zhigang, S., Li, X.: ‘Distributed robust finite-time attitude containment control for multiple rigid bodies with uncertainties’, Int. J. Robust Nonlinear Control, 2014, 25, (15) pp. 106110.
    13. 13)
      • 20. Beaulieu, N.C., Naseri, M.: ‘A circuit theory model for shadow fading autocorrelation in wireless radio channels’, IEEE Wirel. Commun. Lett., 2019, 2, (8), pp. 161164.
    14. 14)
      • 7. Hu, E., Hu, X., Stotsky, A.: ‘Control of mobile platforms using a virtual vehicle approach’, IEEE Trans. Autom. Control, 2001, 11, (46), pp. 17771782.
    15. 15)
      • 11. Xiwang, D., Liang, H., Qingdong, L., et al: ‘Time-varying formation tracking for second-order multi-agent systems with one leader’. 2015 Chinese Automation Congress (CAC), Wuhan, 2015, pp. 10461051.
    16. 16)
      • 8. Yang, N., Zhou, M.: ‘Autonomous overtaking behavior simulation for autonomous virtual vehicle in virtual traffic environment’. 2008 Int. Conf. on Computer Science and Software Engineering, Hubei, 2008, pp. 11501153.
    17. 17)
      • 13. Bin, Z., Yingmin, J.: ‘Fixed-time consensus protocols for multi-agent systems with linear and nonlinear state measurements’, Nonlinear Dyn., 2015, 82, (4), pp. 16831690.
    18. 18)
      • 4. Liu, L., Xiaolong, L., Chuangchuang, Z.: ‘Distributed cooperative control for UAV swarm formation reconfiguration based on consensus theory’. 2017 2nd Int. Conf. on Robotics and Automation Engineering (ICRAE), Shanghai, China, 2016, vol. 10, no. 22, pp. 264268.
    19. 19)
      • 1. Yulong, Z., Xiao, J., Peishun, Y., et al: ‘Next-generation unmanned aerial vehicle (UAV) cooperative communications’, J. Nanjing Univ. Posts Telecommun., Nat. Sci. Ed., 2017, 6, (37), pp. 4351.
    20. 20)
      • 14. Akbar, S., Mahdi, B., Farzad, H.: ‘Leader-follower fixed-time consensus for multi-agent systems with heterogeneous non-linear inherent dynamics’. 2016 3rd Int. Conf. on Soft Computing and Machine Intelligence (ISCMI), Dubai, 2016, pp. 224228.
    21. 21)
      • 5. Hongmei, Z., Yuanfeng, C., Guangyan, X.: ‘UAV elasticity formation extension consensus control based on joint errors’. 2018 Chinese Automation Congress (CAC), Xi'an, China, 2018, vol. 10, pp. 12341239.
    22. 22)
      • 2. Di, B., Zhou, R., Duan, H.: ‘Potential field based receding horizon motion planning for centrality-aware multiple UAV cooperative surveillance’, Aerosp. Sci. Technol., 2015, 12, (46), pp. 386397.
    23. 23)
      • 22. Hui, Y., Yi, Z.: ‘Multi-agent consensus with a time-varying reference state in directed network with switching topology and time-delay’. 2009 Int. Conf. on Wavelet Analysis and Pattern Recognition, Baoding, 2009, pp. 476481.
    24. 24)
      • 16. Kim, Y., Mesbahi, M.: ‘On maximizing the second smallest eigenvalue of a state-dependent graph laplacian’, IEEE Trans. Autom. Control, 2006, 1, (51), pp. 116120.
    25. 25)
      • 18. Gupta, P., Kumar, P.R.: ‘Critical power for asymptotic connectivity’. Proc. of the 37th IEEE Conf. on Decision and Control, Tampa, 1998, no. 1, pp. 11061110.
http://iet.metastore.ingenta.com/content/journals/10.1049/iet-com.2019.0699
Loading

Related content

content/journals/10.1049/iet-com.2019.0699
pub_keyword,iet_inspecKeyword,pub_concept
6
6
Loading