Jia-Xi Chen, Jun-Min Li. Global FLS-based Consensus of Stochastic Uncertain Nonlinear Multi-agent Systems. International Journal of Automation and Computing, vol. 18, no. 5, pp.826-837, 2021. https://doi.org/10.1007/s11633-021-1279-y
Citation: Jia-Xi Chen, Jun-Min Li. Global FLS-based Consensus of Stochastic Uncertain Nonlinear Multi-agent Systems. International Journal of Automation and Computing, vol. 18, no. 5, pp.826-837, 2021. https://doi.org/10.1007/s11633-021-1279-y

Global FLS-based Consensus of Stochastic Uncertain Nonlinear Multi-agent Systems

doi: 10.1007/s11633-021-1279-y
More Information
  • Author Bio:

    Jia-Xi Chen received the M. Sc. and Ph. D. degrees in applied mathematics from Xidian University, China in 2018 and 2020, respectively. He is currently a lecturer at Department of Applied Mathematics, Xidian University, China. His research interests include adaptive control, multi-agent systems and Takagi-Sugeno (T-S) fuzzy systems.E-mail: jxchen208@163.comORCID iD: 0000-0002-9667-811X

    Jun-Min Li received the M. Sc. degree in applied mathematics from Xidian University, China in 1990, and the Ph. D. degree in systems engineering from Xi′an Jiao Tong University, China in 1997. He is currently a professor at Department of Applied Mathematics, Xidian University, China. His research interests include adaptive control, learning control of MAS, hybrid system control theory and networked control system. E-mail: jmli@mail.xidian.edu.cn (Corresponding author)ORCID iD: 0000-0001-8409-6465

  • Received Date: 2020-10-25
  • Accepted Date: 2021-01-22
  • Publish Online: 2021-03-20
  • Publish Date: 2021-10-01
  • Using graph theory, matrix theory, adaptive control, fuzzy logic systems and other tools, this paper studies the leader-follower global consensus of two kinds of stochastic uncertain nonlinear multi-agent systems (MAS). Firstly, the fuzzy logic systems replaces the feedback compensator as the feedforward compensator to describe the uncertain nonlinear dynamics. Secondly, based on the network topology, all followers are divided into two categories: One is the followers who can obtain the leader signal, and the other is the follower who cannot obtain the leader signal. Thirdly, based on the adaptive control method, distributed control protocols are designed for the two types of followers. Fourthly, based on matrix theory and stochastic Lyapunov stability theory, the stability of the closed-loop systems is analyzed. Finally, three simulation examples are given to verify the effectiveness of the proposed control algorithms.

     

  • loading
  • [1]
    T. Vicsek, A. Czirok, E. Ben-Jacob, I. Cohen, O. Shochet. Novel type of phase transition in a system of self-driven particles. Physical Review Letters, vol. 75, no. 6, pp. 1226–1229, 1995. DOI: 10.1103/PhysRevLett.75.1226.
    [2]
    S. Liu, L. H. Xie, D. E. Quevedo. Event-triggered quantized communication-based distributed convex optimization. IEEE Transactions on Control of Network Systems, vol. 5, no. 1, pp. 167–178, 2018. DOI: 10.1109/TCNS.2016.2585305.
    [3]
    J. Y. Sun, Z. Y. Geng, Y. Z. Lv, Z. K. Li, Z. T. Ding. Distributed adaptive consensus disturbance rejection for multi-agent systems on directed graphs. IEEE Transactions on Control of Network Systems, vol. 5, no. 1, pp. 629–639, 2018. DOI: 10.1109/TCNS.2016.2641800.
    [4]
    X. W. Li, Y. C. Soh, L. H. Xie. A novel reduced-order protocol for consensus control of linear multiagent systems. IEEE Transactions on Automatic Control, vol. 64, no. 7, pp. 3005–3012, 2019. DOI: 10.1109/TAC.2018.2876390.
    [5]
    J. H. Wang, Z. Liu, C. L. P. Chen, Y. Zhang. Event-triggered fuzzy adaptive compensation control for uncertain stochastic nonlinear systems with given transient specification and actuator failures. Fuzzy Sets and Systems, vol. 365, pp. 1–21, 2019. DOI: 10.1016/j.fss.2018.04.013.
    [6]
    J. X. Chen, J. M. Li, X. X. Yuan. Global fuzzy adaptive consensus control of unknown nonlinear multiagent systems. IEEE Transactions on Fuzzy Systems, vol. 28, no. 3, pp. 510–522, 2020. DOI: 10.1109/TFUZZ.2019.2908771.
    [7]
    P. P. Dai, C. L. Liu, F. Liu. Consensus problem of heterogeneous multi-agent systems with time delay under fixed and switching topologies. International Journal of Automation and Computing, vol. 11, no. 3, pp. 340–346, 2014. DOI: 10.1007/s11633-014-0798-1.
    [8]
    X. L. Chai, Z. H. Gan. Function projective lag synchronization of chaotic systems with certain parameters via adaptive-impulsive control. International Journal of Automation and Computing, vol. 16, no. 2, pp. 238–247, 2019. DOI: 10.1007/s11633-016-1020-4.
    [9]
    J. X. Chen, J. M. Li, X. X. Yuan. Distributed fuzzy adaptive consensus for high-order multi-agent systems with an imprecise communication topology structure. Fuzzy Sets and Systems, vol. 402, pp. 1–15, 2021. DOI: 10.1016/j.fss.2020.03.018.
    [10]
    J. X. Chen, J. M. Li, N. N. Yang. Globally repetitive learning consensus control of unknown nonlinear multi-agent systems with uncertain time-varying parameters. Applied Mathematical Modelling, vol. 89, pp. 348–362, 2021. DOI: 10.1016/j.apm.2020.07.063.
    [11]
    W. Ren, R. Beard. Distributed Consensus in Multi-Vehicle Cooperative Control: Theory and Applications, London, UK: Springer, 2008.
    [12]
    W. Ren, Y. C. Cao. Distributed Coordination of Multi-Agent Networks: Emergent Problems, Models, and Issues, London, UK: Springer, 2011.
    [13]
    Z. H. Qu. Cooperative Control of Dynamical Systems: Applications to Autonomous Vehicles, London, UK: Springer, 2009. DOI: 10.1007/978-1-84882-325-9.
    [14]
    L. F. Ma, Z. D. Wang, Q. L. Han, Y. R. Liu. Consensus control of stochastic multi-agent systems: A survey. Science China Information Sciences, vol. 60, no. 12, Article number 120201, 2017. DOI: 10.1007/s11432-017-9169-4.
    [15]
    G. X. Wen, C. L. P. Chen, Y. J. Liu. Formation control with obstacle avoidance for a class of stochastic multiagent systems. IEEE Transactions on Industrial Electronics, vol. 65, no. 7, pp. 5847–5855, 2018. DOI: 10.1109/TIE.2017.2782229.
    [16]
    W. Q. Li, J. F. Zhang. Distributed practical output tracking of high-order stochastic multi-agent systems with inherent nonlinear drift and diffusion terms. Automatica, vol. 50, no. 12, pp. 3231–3238, 2014. DOI: 10.1016/j.automatica.2014.10.041.
    [17]
    M. Siavash, V. J. Majd, M. Tahmasebi. A practical finite-time back-stepping sliding-mode formation controller design for stochastic nonlinear multi-agent systems with time-varying weighted topology. International Journal of Systems Science, vol. 51, no. 3, pp. 488–506, 2020. DOI: 10.1080/00207721.2020.1716105.
    [18]
    X. You, C. C. Hua, H. N. Yu, X. P. Guan. Leader-following consensus for high-order stochastic multi-agent systems via dynamic output feedback control. Automatica, vol. 107, pp. 418–424, 2019. DOI: 10.1016/j.automatica.2019.06.006.
    [19]
    B. van den Broek, W. Wiegerinck, B. Kappen. Graphical model inference in optimal control of stochastic multi-agent systems. Journal of Artificial Intelligence Research, vol. 32, pp. 95–122, 2008. DOI: 10.1613/jair.2473.
    [20]
    P. S. Ming, S. H. Li, H. T. Shi, Y. Z. Long. Consensus stability analysis of optimal control for stochastic multi-agent system. In Proceedings of the 29th Chinese Control And Decision Conference, IEEE, Chongqing, China, pp. 5550–5555, 2017.
    [21]
    M. Y. Huang, J. H. Manton. Stochastic consensus seeking with noisy and directed inter-agent communication: Fixed and randomly varying topologies. IEEE Transactions on Automatic Control, vol. 55, no. 1, pp. 235–241, 2010. DOI: 10.1109/TAC.2009.2036291.
    [22]
    R. Zhou, J. M. Li. Stochastic consensus of double-integrator leader-following multi-agent systems with measurement noises and time delays. International Journal of Systems Science, vol. 50, no. 2, pp. 365–378, 2019. DOI: 10.1080/00207721.2018.1552769.
    [23]
    Y. Zhang, Y. P. Tian. Consentability and protocol design of multi-agent systems with stochastic switching topology. Automatica, vol. 45, no. 5, pp. 1195–1201, 2009. DOI: 10.1016/j.automatica.2008.11.005.
    [24]
    L. Liu, J. J. Shan. Event-triggered consensus of nonlinear multi-agent systems with stochastic switching topology. Journal of the Franklin Institute, vol. 354, no. 13, pp. 5350–5373, 2017. DOI: 10.1016/j.jfranklin.2017.05.041.
    [25]
    M. Shahvali, J. Askari. Distributed containment output-feedback control for a general class of stochastic nonlinear multi-agent systems. Neurocomputing, vol. 179, pp. 202–210, 2016. DOI: 10.1016/j.neucom.2015.12.014.
    [26]
    X. Y. Guo, H. J. Liang, Y. N. Pan. Observer-based adaptive fuzzy tracking control for stochastic nonlinear multi-agent systems with dead-zone input. Applied Mathematics and Computation, vol. 379, Article number 125269, 2020. DOI: 10.1016/j.amc.2020.125269.
    [27]
    Y. Yang, S. T. Miao, C. Xu, D. Yue, J. Tan, Y. C. Tian. Adaptive neural output consensus control of stochastic nonlinear strict-feedback multi-agent systems. In Proceedings of Australian & New Zealand Control Conference, IEEE, Melbourne, Australia, pp. 385−390, 2018.
    [28]
    Q. Zhou, W. Wang, H. Ma, H. Y. Li. Event-triggered fuzzy adaptive containment control for nonlinear multi-agent systems with unknown Bouc-Wen hysteresis input. IEEE Transactions on Fuzzy Systems, vol. 29, no. 4, pp. 731−741.
    [29]
    F. Wang, Y. Zhang, L. L. Zhang, J. Zhang, Y. Y. Huang. Finite-time consensus of stochastic nonlinear multi-agent systems. International Journal of Fuzzy Systems, vol. 22, no. 1, pp. 77–88, 2020. DOI: 10.1007/s40815-019-00769-w.
    [30]
    S. B. Li, M. J. Er, J. Zhang. Distributed adaptive fuzzy control for output consensus of heterogeneous stochastic nonlinear multiagent systems. IEEE Transactions on Fuzzy Systems, vol. 26, no. 3, pp. 1138–1152, 2018. DOI: 10.1109/TFUZZ.2017.2710949.
    [31]
    C. E. Ren, L. Chen, C. L. P. Chen. Adaptive fuzzy leader-following consensus control for stochastic multiagent systems with heterogeneous nonlinear dynamics. IEEE Transactions on Fuzzy Systems, vol. 25, no. 1, pp. 181–190, 2017. DOI: 10.1109/TFUZZ.2016.2554151.
    [32]
    C. C. Hua, L. L. Zhang, X. P. Guan. Distributed adaptive neural network output tracking of leader-following high-order stochastic nonlinear multiagent systems with unknown dead-zone input. IEEE Transactions on Cybernetics, vol. 47, no. 1, pp. 177–185, 2017. DOI: 10.1109/TCYB.2015.2509482.
    [33]
    F. Wang, B. Chen, C. Lin, X. H. Li. Distributed adaptive neural control for stochastic nonlinear multiagent systems. IEEE Transactions on Cybernetics, vol. 47, no. 7, pp. 1795–1803, 2017. DOI: 10.1109/TCYB.2016.2623898.
    [34]
    Y. G. Hong, J. P. Hu, L. X. Gao. Tracking control for multi-agent consensus with an active leader and variable topology. Automatica, vol. 42, no. 7, pp. 1177–1182, 2006. DOI: 10.1016/j.automatica.2006.02.013.
  • 加载中

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(8)

    Article Metrics

    Article views (77) PDF downloads(15) Cited by()
    Proportional views
    Related

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return
    Return