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Consensus of nonlinear multi-agent systems with fuzzy modelling uncertainties via state-constraint hybrid impulsive protocols

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Abstract

In this paper, the nonlinear multi-agent system which contains uncertainty and is controlled by state-constraint impulsive protocol is taken into consideration. For the uncertainty of the multi-agent system, it is replaced by fuzzy logic system approximately and a judgement strategy which only contains the relative information with neighbors is proposed in this paper. In order to do research in state-constraint impulsive protocol, three kinds of impulsive control protocols which conclude partial input saturation, double actuator saturation and single actuator saturation are discussed. Then, some sufficient conditions of the system are obtained to reach consensus. Finally, some numerical simulation examples are provided to prove the effectiveness of the theoretical analysis.

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References

  1. Zhu Y, Li S, Ma J, Zheng Y (2018) Bipartite consensus in networks of agents with antagonistic interactions and quantization. IEEE Trans Circuits Syst II Express Briefs 65(12):2012–2016

    Article  Google Scholar 

  2. Ma J, Ye M, Zheng Y, Zhu Y (2019) Analysis of hybrid multi-agent systems: a game-theoretic approach. Int J Robust Nonlinear Control 29(6):1840–1853

    Article  MATH  Google Scholar 

  3. Han Y, Li C, Zhang W, Ahmad H (2018) Impulsive consensus of multiagent systems with limited bandwidth based on encoding–decoding. IEEE Trans Cybern. https://doi.org/10.1109/TCYB.2018.2863108

    Article  Google Scholar 

  4. Xu Z, Li C, Han Y (2019) Leader-following fixed-time quantized consensus of multi-agent systems via impulsive control. J Frankl Inst 356(1):441–456

    Article  MathSciNet  MATH  Google Scholar 

  5. Liu C, Tian Y (2019) Formation control of multi-agent systems with heterogeneous communication delays. Int J Syst Sci 40(7):627–636

    MathSciNet  MATH  Google Scholar 

  6. Guan Z, Wu Y (2012) Consensus analysis based on impulsive systems in multiagent networks. IEEE Trans Circuits Syst I 59(1):170–178

    Article  MathSciNet  Google Scholar 

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

    Article  MathSciNet  MATH  Google Scholar 

  8. Olfati-Saber R, Fax J, Murray R (2007) Consensus and cooperation in networked multi-agent systems. Proc IEEE 95(1):215–233

    Article  MATH  Google Scholar 

  9. Ma T, Zhang Z (2018) Adaptive consensus of multi-agent systems via odd impulsive control. Neurocomputing 321:139–145

    Article  Google Scholar 

  10. Hong Y, Hu J, Gao L (2006) Tracking control for multi-agent consensus with an active leader and variable topology. Automatica 42(7):1177–1182

    Article  MathSciNet  MATH  Google Scholar 

  11. Han Y, Li C (2018) Second-order consensus of discrete-time multi-agent systems in directed networks with nonlinear dynamics via impulsive protocols. Neurocomputing 286:51–57

    Article  Google Scholar 

  12. Wen G, Chen P, Liu C, Liu Y (2015) Neural-network-based adaptive leader-following consensus control for second-order non-linear multi-agent systems. IET Control Theory Appl 9(13):1927–1934

    Article  MathSciNet  Google Scholar 

  13. Mei J, Ren W (2016) Distributed consensus of second-order multi-agent systems with heterogeneous unknown inertias and control gains under a directed graph. IEEE Trans Autom Control 61(8):2019–2034

    Article  MathSciNet  MATH  Google Scholar 

  14. Zheng Y, Ma J, Wang L (2018) Consensus of hybrid multi-agent systems. IEEE Trans Neural Netw Learn Syst 9(4):1359–1365

    Article  Google Scholar 

  15. Feng Y, Li C (2017) Sandwich control systems with impulse time windows. Int J Mach Learn Cybern 8(6):2009–2015

    Article  Google Scholar 

  16. Zhu X, Guo H, Yun X (2011) Theory and application of impulsive differential equations, vol 32. Science Press, Tokyo, pp 1253–1264

    Google Scholar 

  17. Li C, Zhou Y, Wang H (2017) Stability of nonlinear systems with variable-time impulses: B-equivalence method. Int J Control Autom Syst 15(5):2072–2079

    Article  Google Scholar 

  18. Ma F, Li C, Huang T (2011) Iterative learning control design of nonlinear multiple time-delay systems. Appl Math Comput 218(8):4333–4340

    MathSciNet  MATH  Google Scholar 

  19. Zhou J, Xiang L, Liu Z (2007) Synchronization in complex delayed dynamical networks with impulsive effects. Physica A 384(2):684–692

    Article  Google Scholar 

  20. Li L, Li C, Li H (2018) An analysis and design for time-varying structures dynamical networks via state constraint impulsive control. Int J Control 5(1):1–23

    Article  Google Scholar 

  21. Jiang X, Zhan X, Guan Z, Zhang X, Li Y (2015) Neimark–Sacker bifurcation analysis on a numerical discretization of Gause-type predator–prey model with delay. J Frankl Inst 352(1):1–15

    Article  MathSciNet  MATH  Google Scholar 

  22. Sun Y (2012) Average consensus in networks of dynamic agents with uncertain topologies and time-varying delays. J Frankl Inst 349(3):1061–1073

    Article  MathSciNet  MATH  Google Scholar 

  23. Zhan X, Guan Z, Yuan F (2013) Optimal tracking performance and design of networked control systems with packet dropout. J Frankl Inst 350(10):3205–3216

    Article  MathSciNet  MATH  Google Scholar 

  24. Jiang X, Guan Z, Zhang X (2017) Performance limitations of networked control systems with quantization and packet dropouts. ISA Trans 10(8):98–106

    Article  Google Scholar 

  25. He X, Li C, Pan X (2014) Impulsive control and Hopf bifurcation of a three-dimensional chaotic system. J Vib Control 20(9):1361–1368

    Article  MathSciNet  MATH  Google Scholar 

  26. Li D, Wang Z, Zhou J (2008) A note on chaotic synchronization of time-delay secure communication systems. Chaos Solitons Fractals 38(4):1217–1224

    Article  MathSciNet  MATH  Google Scholar 

  27. Zhang Q, Zhao J (2012) Projective and lag synchronization between general complex networks via impulsive control. Nonlinear Dyn 67(4):2519–2525

    Article  MathSciNet  MATH  Google Scholar 

  28. Guan Z, Zhang H (2008) Stabilization of complex network with hybrid impulsive and switching control. Chaos Solitons Fractals 37(13):1372–1382

    Article  MathSciNet  MATH  Google Scholar 

  29. Ling G, Guan Z, Chen J, Lai Q (2019) Chaotifying stable linear complex networks via single pinning impulsive strategy. Int J Bifurc Chaos 29(02):1950024

    Article  MathSciNet  MATH  Google Scholar 

  30. Liu N, Fang J, Deng WuZ, Ding G (2018) Synchronization for a class of fractional-order linear complex networks via impulsive control. Int J Control Autom Syst 16(6):2839–2844

    Article  Google Scholar 

  31. Ren C, Chen L, Chen P (2016) Adaptive fuzzy leader-following consensus control for stochastic multiagent systems with heterogeneous nonlinear. IEEE Trans Fuzzy Syst 25(1):181–190

    Article  Google Scholar 

  32. Yang S, Li C, Huang T, Zhang W (2019) Fixed-time consensus of complex dynamical networks with nonlinear coupling and fuzzy state-dependent uncertainties. Fuzzy Sets Syst 365:81–97

    Article  MathSciNet  MATH  Google Scholar 

Download references

Acknowledgements

This work was supported by National Natural Science Foundation of China (61873213, 61633011), and in part by National Key Research and Development Project (2018AAA0100101).

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Authors and Affiliations

  1. Chongqing Key Laboratory of Nonlinear Circuits and Intelligent Information Processing, College of Electronic and Information Engineering, Southwest University, Chongqing, 400715, People’s Republic of China

    Le You, Chuandong Li & Yiyan Han

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  1. Le You
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  2. Chuandong Li
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  3. Yiyan Han
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Correspondence to Chuandong Li.

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You, L., Li, C. & Han, Y. Consensus of nonlinear multi-agent systems with fuzzy modelling uncertainties via state-constraint hybrid impulsive protocols. Int. J. Mach. Learn. & Cyber. 11, 2653–2664 (2020). https://doi.org/10.1007/s13042-020-01140-4

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