Location via proxy:   [ UP ]  
[Report a bug]   [Manage cookies]                
Skip to main content
Springer Nature Link
Log in

Output consensus for heterogeneous nonlinear multi-agent systems based on T-S fuzzy model

  • Published:
Journal of Systems Science and Complexity Aims and scope Submit manuscript

Abstract

In this paper, the output consensus problem of general heterogeneous nonlinear multi-agent systems subject to different disturbances is considered. A kind of Takagi-Sukeno fuzzy modeling method is used to describe the nonlinear agents’ dynamics. Based on the model, a distributed fuzzy observer and controller are designed based on parallel distributed compensation scheme and internal reference models such that the heterogeneous nonlinear multi-agent systems can achieve output consensus. Then a necessary and sufficient condition is presented for the output consensus problem. And it is shown that the consensus trajectory of the global fuzzy model is determined by the network topology and the initial states of the internal reference models. Finally, some simulations are given to illustrate and verify the effectiveness of the proposed scheme.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  1. Kuriki Y and Namerikawa T, Consensus-based cooperative formation control with collision avoidance for a multi-UAV system, 2014 American Control Conference, 2014, 2077–2082.

    Chapter  Google Scholar 

  2. Zuo Z Q, Zhang J, and Wang Y J, Adaptive fault-tolerant tracking control for linear and lipschitz nonlinear multi-agent systems, IEEE Transactions on Industrial Electronics, 2015, 62(6): 3923–3931.

    Google Scholar 

  3. Su H S, Wang X F, and Lin Z L, Flocking of multi-agents with a virtual leader, IEEE Trans. Autom. Control, 2009, 54(2): 293–307.

    Article  MathSciNet  Google Scholar 

  4. Lin Z Y, Wang L, Han Z, et al., Distributed formation control of multi-agent systems using complex Laplacian, IEEE Trans. Autom. Control, 59(7): 1765–1777.

  5. Zhu S, Chen C L, Li W, et al., Distributed optimal consensus filter for target tracking in heterogeneous sensor networks, IEEE Trans. Cybernetics, 2013, 43(6): 1963–1976.

    Article  Google Scholar 

  6. Olfati-Saber R and Murray R, Consensus problems in networks of agents with switching topology and time-delays, IEEE Trans. Autom. Control, 2004, 49(9): 1520–1533.

    Article  MathSciNet  MATH  Google Scholar 

  7. Yu W W, Ren W, Zheng W, et al., Distributed control gains design for consensus in multi-agent systems with second-order nonlinear dynamics, Automatica, 2013, 49(7): 2107–2115.

    Article  MathSciNet  MATH  Google Scholar 

  8. Wang J K, Fu J K, and Zhang G S, Finite-time consensus problem for multiple non-holonomic agents with communication delay, Journal of Systems Science and Complexity, 2015, 28(3): 559–569.

    Article  MathSciNet  MATH  Google Scholar 

  9. You K Y and Xie L H, Network topology and communication data rate for consensusability of discrete-time multi-agent systems, IEEE Trans. Autom. Control, 2011, 56(10): 1520–1533.

    MathSciNet  Google Scholar 

  10. You K Y and Xie L H, Consensusability of discrete-time multi-agent systems over directed graphs, Proc. 30th Chinese Control Conference, Yantai, China, 2011, 6413–6418.

    Google Scholar 

  11. Hua C C, Yang Y N, and Liu P, Output-feedback adaptive control of networked teleoperation system with time-varying delay and bounded inputs, IEEE/ASME Transactions on Mechatronics, 2015, 20(5): 2009–2020.

    Article  Google Scholar 

  12. Yang X, Luo H, Krueger M, et al., Online monitoring system design for roll eccentricity in rolling mills, IEEE Transactions on Industrial Electronics, 2016, 63(4): 2559–2568.

    Article  Google Scholar 

  13. Xi J, Shi Z, and Zhong Y, Output consensus analysis and design for high-order linear swarm systems: Partial stability method, Automatica, 2012, 48(9): 2335–2343.

    Article  MathSciNet  MATH  Google Scholar 

  14. Zhang H, Feng H, Yan H, et al., Observer-based output feedback event-triggered control for consensus of multi-agent systems, IEEE Trans. Industrial Electronics, 2014, 61(9): 4885–4893.

    Article  Google Scholar 

  15. Wang J H, Liu Z X, Hu X M, Consensus control design for multi-agent systems using relative output feedback, Journal of Systems Science and Complexity, 2014, 27(2): 237–251.

    Article  MathSciNet  MATH  Google Scholar 

  16. Xiong X, Yu W, Lü J, et al., Fuzzy modelling and consensus of nonlinear multi-agent systems with variable structure, IEEE Trans. Circuits Syst. I, Reg. Papers, 2014, 61(4): 1183–1191.

    Article  Google Scholar 

  17. Zhao Y, Li B, Qin J, et al., H ℞ consensus and synchronization of nonlinear systems based on a novel fuzzy model, IEEE Trans. Cybern., 2013, 43(6): 2157–2169.

    Article  Google Scholar 

  18. Kim H, Shim H, and Seo J H, Output consensus of heterogeneous uncertain linear multi-agent systems, IEEE Trans. Autom. Control, 2011, 56(1): 200–206.

    Article  MathSciNet  Google Scholar 

  19. Li S B, Feng G, Luo X Y, et al., Output consensus of heterogeneous linear discrete-time multi-agent systems with structural uncertainties, IEEE Trans. Cybernetics, 2015, 45(12): 2868–2879.

    Article  Google Scholar 

  20. Li S B, Feng G, Luo X Y, et al., Output consensus of heterogeneous linear multi-agent systems subject to different disturbances, Asian Journal of Control, 2016, 18(2): 757–762.

    Article  MathSciNet  MATH  Google Scholar 

  21. Godsil C and Royle G, Algebraic Graph Theory, Springer Verlag, New York, 2001.

    Book  MATH  Google Scholar 

  22. Lin P, Jia Y, and Li L, Distributed robust H ℞ consensus control in directed networks of agents with time delay, Systems & Control Letters, 2008, 57(8): 643–653.

    Article  MathSciNet  MATH  Google Scholar 

  23. Mohar B, Eigenvalues, diameter, and mean distance in graphs, Graphs and Combinatories, 1991, 7(1): 53–64.

    Article  MathSciNet  MATH  Google Scholar 

  24. Meda-Campaña J A, Gómez-Mancilla J C, and Castillo-Toledo B, Exact output regulation for nonlinear systems described by Takagi–Sugeno fuzzy models, IEEE Trans. Fuzzy Syst., 2012, 20(2): 235–247.

    Article  Google Scholar 

  25. Meda-Campaña J A, Castillo-Toledo B, and Zúñiga V, On the nonlinear fuzzy regulation for under-actuated systems, Proc. IEEE Int. Conf. Fuzzy Syst., Vancouver, BC, Canada, Jul. 16–21, 2006, 2195–2202.

    Google Scholar 

  26. Su Y F and Huang J, Cooperative output regulation with application to multi-agent consensus under switching network, IEEE Trans. Systems, Man, and Cybern., Part B, 2012, 42(3): 864–875.

    Article  Google Scholar 

  27. Huang J, Nolinear Output Regulation: Theory and Applications, SIAM, Phildelphia, 2004.

    Book  Google Scholar 

  28. Li Z, Duan Z S, Chen G R, et al., Consensus of multiagent systems and synchronization of complex networks: A unified viewpoint, IEEE Trans. Syst., Man, Cybern. A, Syst., Humans, 2010, 57(1): 213–224.

    MathSciNet  Google Scholar 

  29. He W and Ge S, Robust adaptive boundary control of a vibrating string under unknown timevarying disturbance, IEEE Trans. Control Syst. Tech., 2012, 20(1): 48–58.

    Google Scholar 

  30. Yang Z, Fukushima Y, and Qin P, Decentralized adaptive robust control of robot manipulators using disturbance observers, IEEE Trans. Control Syst. Tech., 2012, 20(5): 1357–1365.

    Article  Google Scholar 

  31. Tuna S E, LQR-based coupling gain for synchronization of linear systems, Available: http://arxiv.org/abs /0801.3390.

  32. Li Z, Ren W, Liu X, et al., Consensus of multi-agent systems with general linear and Lipschitz nonlinear dynamics using distributed adaptive protocols, IEEE Trans. Autom. Control, 2013, 58(7): 1786–1791.

    Article  MathSciNet  Google Scholar 

  33. Li Z, Ren W, Liu X, et al., Distributed consensus of linear multi-agent systems with adaptive dynamic protocols, Automatica, 2013, 49(7): 1986–1995.

    Article  MathSciNet  MATH  Google Scholar 

  34. Ma X and Sun Z, Output tracking and regulation of nonlinear system based on Takagi-Sugeno fuzzy model, IEEE Trans. Systems, Man, and Cybern., Part B, 2000, 30(1): 47–59.

    Article  Google Scholar 

  35. Misra V, Gong W, and Towsley D, Fluid-based analysis of a network of AQM routers supporting TCP flows with an application to RED, Proceeding of ACM/SIGCOMM, Sweden, 2000, 151–160.

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. School of Electrical Engineering, Yanshan University, Qinhuangdao, 066004, China

    Xiaolei Li, Xiaoyuan Luo & Shaobao Li

  2. Polytechnic School of Engineering, New York University, New York, NY, 11201, USA

    Xiaoyuan Luo

  3. Institute of Electronic, Information and Electrical Engineering, Shanghai Jiao Tong University, Shanghai, 200240, China

    Xinping Guan

Authors
  1. Xiaolei Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Xiaoyuan Luo
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Shaobao Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Xinping Guan
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Xiaoyuan Luo.

Additional information

This work is supported in part by the National Natural Science Foundation of China under Grant Nos. 61375105 and 61403334, Chinese Postdoctoral Science Fundation under Grant No. 2015M581318.

This paper was recommended for publication by Editor FENG Gang.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Li, X., Luo, X., Li, S. et al. Output consensus for heterogeneous nonlinear multi-agent systems based on T-S fuzzy model. J Syst Sci Complex 30, 1042–1060 (2017). https://doi.org/10.1007/s11424-016-5243-9

Download citation

  • Received:

  • Revised:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11424-016-5243-9

Keywords

Search

Navigation