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Volume 10 Issue 3
Mar.  2023

IEEE/CAA Journal of Automatica Sinica

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J. Wang, S. Y. Li, and Y. Y. Zou, “Connectivity-maintaining consensus of multi-agent systems with communication management based on predictive control strategy,” IEEE/CAA J. Autom. Sinica, vol. 10, no. 3, pp. 700–710, Mar. 2023. doi: 10.1109/JAS.2023.123081
Citation: J. Wang, S. Y. Li, and Y. Y. Zou, “Connectivity-maintaining consensus of multi-agent systems with communication management based on predictive control strategy,” IEEE/CAA J. Autom. Sinica, vol. 10, no. 3, pp. 700–710, Mar. 2023. doi: 10.1109/JAS.2023.123081

Connectivity-maintaining Consensus of Multi-agent Systems With Communication Management Based on Predictive Control Strategy

doi: 10.1109/JAS.2023.123081
Funds:  This work was supported by the National Key Research and Development Program of China (2018AAA0101701) and the National Natural Science Foundation of China (62173224, 61833012)
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  • This paper studies the connectivity-maintaining consensus of multi-agent systems. Considering the impact of the sensing ranges of agents for connectivity and communication energy consumption, a novel communication management strategy is proposed for multi-agent systems so that the connectivity of the system can be maintained and the communication energy can be saved. In this paper, communication management means a strategy about how the sensing ranges of agents are adjusted in the process of reaching consensus. The proposed communication management in this paper is not coupled with controller but only imposes a constraint for controller, so there is more freedom to develop an appropriate control strategy for achieving consensus. For the multi-agent systems with this novel communication management, a predictive control based strategy is developed for achieving consensus. Simulation results indicate the effectiveness and advantages of our scheme.

     

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  • [1]
    R. Olfati-Saber, J. A. Fax, and R. M. Murray, “Consensus and cooperation in networked multi-agent systems,” Proc. the IEEE, vol. 95, no. 1, pp. 215–233, 2007. doi: 10.1109/JPROC.2006.887293
    [2]
    G. Wen and W. X. Zheng, “On constructing multiple Lyapunov functions for tracking control of multiple agents with switching topologies,” IEEE Trans. Autom. Control, vol. 64, no. 9, pp. 3796–3803, 2019. doi: 10.1109/TAC.2018.2885079
    [3]
    Z. X. Liu, Y. B. Li, F. Y. Wang, and Z. Q. Chen, “Reduced-order observer-based leader-following formation control for discrete-time linear multi-agent systems,” IEEE/CAA J. Autom. Sinica, vol. 8, no. 10, pp. 1715–1723, 2021.
    [4]
    Q. L. Wei, X. Wang, X. N. Zhong, and N. Q. Wu, “Consensus control of leader-following multi-agent systems in directed topology with heterogeneous disturbances,” IEEE/CAA J. Autom. Sinica, vol. 8, no. 2, pp. 423–431, 2021. doi: 10.1109/JAS.2021.1003838
    [5]
    J. K. Ni, P. Shi, Y. Zhao, and Z. H. Wu, “Fixed-time output consensus tracking for high-order multi-agent systems with directed network topology and packet dropout,” IEEE/CAA J. Autom. Sinica, vol. 8, no. 4, pp. 817–836, 2021. doi: 10.1109/JAS.2021.1003916
    [6]
    Y. X. Su, Q. L. Wang, and C. Y. Sun, “Self-triggered consensus control for linear multi-agent systems with input saturation,” IEEE/CAA J. Autom. Sinica, vol. 7, no. 1, pp. 150–157, 2020. doi: 10.1109/JAS.2019.1911837
    [7]
    Z. T. Li, L. X. Gao, W. H. Chen, and Y. Xu, “Distributed adaptive cooperative tracking of uncertain nonlinear fractional-order multi-agent systems,” IEEE/CAA J. Autom. Sinica, vol. 7, no. 1, pp. 292–300, 2020. doi: 10.1109/JAS.2019.1911858
    [8]
    X. W. Dong, B. C. Yu, Z. Y. Shi, and Y. S. Zhong, “Time-varying formation control for unmanned aerial vehicles: Theories and applications,” IEEE Trans. Control Systems Technology, vol. 23, no. 1, pp. 340–348, 2015. doi: 10.1109/TCST.2014.2314460
    [9]
    D. Panagou, D. M. Stipanovic, and P. G. Voulgaris, “Distributed coordination control for multi-robot networks using Lyapunov-like barrier functions,” IEEE Trans. Autom. Control, vol. 61, no. 3, pp. 617–632, 2016. doi: 10.1109/TAC.2015.2444131
    [10]
    H.-T. Zhang, Z. M. Cheng, G. R. Chen, and C. G. Li, “Model predictive flocking control for second-order multi-agent systems with input constraints,” IEEE Trans. Circuits and Systems I: Regular Papers, vol. 62, no. 6, pp. 1599–1606, 2015. doi: 10.1109/TCSI.2015.2418871
    [11]
    L. F. Zhou and S. Y. Li, “Distributed model predictive control for consensus of sampled-data multi-agent systems with double-integrator dynamics,” IET Control Theory and Applications, vol. 9, no. 12, pp. 1774–1780, 2015.
    [12]
    Y. Y. Chen, R. Yu, Y. Zhang, and C. L. Liu, “Circular formation flight control for unmanned aerial vehicles with directed network and external disturbance,” IEEE/CAA J. Autom. Sinica, vol. 7, no. 2, pp. 505–516, 2020. doi: 10.1109/JAS.2019.1911669
    [13]
    H. S. Su, X. F. Wang, and G. R. Chen, “Rendezvous of multiple mobile agents with preserved network connectivity,” Systems and Control Letters, vol. 59, no. 5, pp. 313–322, 2010.
    [14]
    Y. C. Cao, W. Ren, D. W. Casbeer, and C. Schumacher, “Finite-time connectivity-preserving consensus of networked nonlinear agents with unknown Lipschitz terms,” IEEE Trans. Autom. Control, vol. 61, no. 6, pp. 1700–1705, 2016. doi: 10.1109/TAC.2015.2479926
    [15]
    C. Wang, C. L. Liu, and S. Liu, “Robust fixed-time connectivity-preserving consensus for second-order multi-agent systems with external disturbances,” IET Control Theory &Applications, vol. 14, no. 17, pp. 2674–2681, 2020.
    [16]
    Y. Dong and S. Y. Xu, “A novel connectivity-preserving control design for rendezvous problem of networked uncertain nonlinear systems,” IEEE Trans Neural Netw Learn Syst, vol. 31, no. 12, pp. 5127–5137, 2020. doi: 10.1109/TNNLS.2020.2964017
    [17]
    Q. Wang, M. Hempstead, and W. Yang, “A realistic power consumption model for wireless sensor network devices,” in Proc. 3rd Annu. IEEE Communications Society Sensor and Ad Hoc Communications and Networks, 2006, pp. 286–295.
    [18]
    Y. C. Cao, P. Eduardo, and J. Hong, “Towards energy-efficient communication management in the distributed control of networked cyber-physical systems,” in Proc. American Control Conf., 2017, pp. 3999–4004.
    [19]
    J. Hong, J. Votion, Y. C. Cao, and Y. F. Jin, “Adaptive communication and control co-design for multi-agent coordination with second-order dynamics,” in Proc. American Control Conf., 2019, pp. 5322–5327.
    [20]
    Z. M. Cheng, H.-T. Zhang, M.-C. Fan, and G. R. Chen, “Distributed consensus of multi-agent systems with input constraints: A model predictive control approach,” IEEE Trans. Circuits and Systems I: Regular Papers, vol. 62, no. 3, pp. 825–834, 2015. doi: 10.1109/TCSI.2014.2367575
    [21]
    H. P. Li and W. S. Yan, “Receding horizon control based consensus scheme in general linear multi-agent systems,” Automatica, vol. 56, pp. 12–18, 2015. doi: 10.1016/j.automatica.2015.03.023
    [22]
    J. Y. Zhan and X. Li, “Consensus of sampled-data multiagent networking systems via model predictive control,” Automatica, vol. 49, no. 8, pp. 2502–2507, 2013. doi: 10.1016/j.automatica.2013.04.037
    [23]
    B. Zhu, K. X. Guo, and L. H. Xie, “A new distributed model predictive control for unconstrained doubleintegrator multiagent systems,” IEEE Trans. Autom. Control, vol. 63, no. 12, pp. 4367–4374, 2018. doi: 10.1109/TAC.2018.2819429
    [24]
    H. Y. Li and X. Li, “Distributed model predictive consensus of heterogeneous time-varying multi-agent systems: With and without self-triggered mechanism,” IEEE Trans. Circuits and Systems I: Regular Papers, vol. 67, no. 12, pp. 5358–5368, 2020. doi: 10.1109/TCSI.2020.3008528
    [25]
    J. Wang, S. Y. Li, and Y. Y. Zou, “Predictive control for consensus problem of multi-agents system with communication management,” in Proc. 40th Chinese Control Conf., 2021, pp. 5610–5615.
    [26]
    Y. C. Cao and W. Ren, “Sampled-data discrete-time coordination algorithms for double-integrator dynamics under dynamic directed interaction,” Int. J. Control, vol. 83, no. 3, pp. 506–515, 2009.
    [27]
    Z. Y. Zuo, J. W. Song, and Q.-L. Han, “Coordinated planar path-following control for multiple nonholonomic wheeled mobile robots,” IEEE Trans. Cybernetics, pp. 1–10, 2021.

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    Highlights

    • A scheme including a communication management strategy and a predictive control based strategy is designed for the multi-agent systems that the connectivity-maintaining consensus can be achieved and the communication energy can be saved
    • The proposed novel communication management strategy is not coupled with controller but only impose a constraint for controller, so there is more freedom to develop an appropriate control strategy for the system, and with this strategy, the connectivity can be guaranteed and the communication energy can be saved
    • A predictive control based strategy is designed with this novel communication management strategy, and compared to the related literature, the scheme in this paper can save more communication energy

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