Skip to main content
SpringerLink
Log in

Asymptotic State Agreement of T–S Fuzzy Multi-agent Systems: A Dynamic Event-Triggered Approach

  • Published:
International Journal of Fuzzy Systems Aims and scope Submit manuscript

Abstract

In this study, the dynamic event-triggered-based distributed control problem are considered for the T–S fuzzy multi-agent systems. To rationalize the use of communication resources and carefully avoid redundant continuous detection, a novel event-triggered mechanism with dynamic update of thresholds is proposed to guarantee both asymptotic stability and Zeno-free. Furthermore, a fuzzy-based distributed controller is designed to deal with state consensus problem of the T–S fuzzy multi-agent systems. Then, sufficient conditions with linear matrix inequalities are developed to establish the stability of the overall closed-loop system by the Lyapunov stability theory. Finally, a numerical simulation and a two-wheel inverted pendulums simulation are presented to illustrate the usefulness and advantages of the proposed method.

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

Access this article

Log in via an institution

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

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9

Similar content being viewed by others

References

  1. Wang, T., Zhang, H., Zhao, Y.L.: Consensus of multi-agent systems under binary-valued measurements and recursive projection algorithm. IEEE Trans. Autom. Control 65(6), 2678–2685 (2020)

    MathSciNet  MATH  Google Scholar 

  2. Wang, Q.S., Duan, Z.S., Wang, J.Y., Wang, Q.Y., Chen, G.R.: An accelerated algorithm for linear quadratic optimal consensus of heterogeneous multi-agent systems. IEEE Trans. Autom. Control 67(1), 421–428 (2021)

    MATH  Google Scholar 

  3. Kazerooni, E.S., Khorasani, K.: Team consensus for a network of unmanned vehicles in presence of actuator faults. IEEE Trans. Control Syst. Technol. 18(5), 1155–1161 (2010)

    Google Scholar 

  4. Kuo, C.W., Tsai, C.C., Lee, C.T.: Intelligent leader-following consensus formation control using recurrent neural networks for small-size unmanned helicopters. IEEE Trans. Syst. Man Cybern. 51(2), 1288–1301 (2021)

    Google Scholar 

  5. Mustafa, A., Modares, H.: Attack analysis and resilient control design for discrete-time distributed multi-agent systems. IEEE Robot. Autom. Lett. 5(2), 369–376 (2020)

    Google Scholar 

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

    MATH  Google Scholar 

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

    MathSciNet  MATH  Google Scholar 

  8. Xu, J., Xie, L., Li, T., Lum, K.Y.: Consensus of multi-agent systems with general linear dynamics via dynamic output feedback control. IET Control Theory Appl. 7(1), 108–115 (2013)

    MathSciNet  Google Scholar 

  9. Yu, W.W., Chen, G.R., Cao, M., Kurths, J.: Second-order consensus for multiagent systems with directed topologies and nonlinear dynamics. IEEE Trans. Syst. Man Cybern. 40(3), 881–891 (2009)

    Google Scholar 

  10. Lu, X., Lu, R., Chen, S., Lu, J.: Finite-time distributed tracking control for multi-agent systems with a virtual leader. IEEE Trans. Circuits Syst. I 60(2), 352–362 (2012)

    MathSciNet  MATH  Google Scholar 

  11. Heemels, W.P.M.H., Donkers, M.C.F., Teel, A.R.: Periodic event-triggered control for linear systems. IEEE Trans. Autom. Control 58(4), 847–861 (2012)

    MathSciNet  MATH  Google Scholar 

  12. Zhang, P.P., Liu, T.F., Jiang, Z.-P.: Tracking control of unicycle mobile robots with event-triggered and self-triggered feedback. IEEE Trans. Autom. Control (2022). https://doi.org/10.1109/TAC.2022.317393

    Article  Google Scholar 

  13. Zhang, P.P., Liu, T.F., Jiang, Z.-P.: Event-triggered stabilization of a class of nonlinear time-delay systems. IEEE Trans. Autom. Control 66(1), 421–428 (2020)

    MathSciNet  MATH  Google Scholar 

  14. Li, H., Tie, M., Wang, Y.: Event-triggered sliding mode control using the interval Type-2 fuzzy logic for steer-by-wire systems with actuator fault. Int. J. Fuzzy Syst. 24(7), 3104–3117 (2022)

    Google Scholar 

  15. Xing, S.Y., Sun, W.B., Deng, F.Q.: Event-triggered control for uncertain stochastic systems under triple network attacks. J. Franklin Inst. 359(16), 8869–8894 (2022)

    MathSciNet  MATH  Google Scholar 

  16. Astrom K.J.: Event based control. In: Analysis and Design of Nonlinear Control Systems, pp. 127–147. Springer, Heidelberg (2008)

  17. Lunze, J., Lehmann, D.: A state-feedback approach to event-based control. Automatica 46(1), 211–215 (2010)

    MathSciNet  MATH  Google Scholar 

  18. Zhang, J., Feng, G.: Event-driven observer-based output feedback control for linear systems. Automatica 50(7), 1852–1859 (2014)

    MathSciNet  MATH  Google Scholar 

  19. Yang, R., Zhang, H., Feng, G., Yan, H., Wang, Z.: Robust cooperative output regulation of multi-agent systems via adaptive event-triggered control. Automatica 102, 129–136 (2019)

    MathSciNet  MATH  Google Scholar 

  20. Girard, A.: Dynamic triggering mechanisms for event-triggered control. IEEE Trans. Autom. Control 60(7), 1992–1997 (2014)

    MathSciNet  MATH  Google Scholar 

  21. Yesmin, A., Behera, A.K., Bera, M.K., Bandyopadhyay, B.: Dynamic event-triggering based design of sliding mode control. Int. J. Robust Nonlinear Control 31(12), 5910–5925 (2021)

    MathSciNet  Google Scholar 

  22. Li, X.Y., Ma, D.Z., Xie, X.P., Sun, Q.Y.: Fault-tolerant synchronization of chaotic systems with fuzzy sampled data controller based on adaptive event-triggered scheme. Int. J. Fuzzy Syst. 22(3), 917–929 (2020)

    Google Scholar 

  23. Wu, X., Mu, X.: New design on distributed event-based sliding mode controller for disturbed second-order multiagent systems. IEEE Trans. Autom. Control 67(5), 2590–2596 (2022)

    MathSciNet  MATH  Google Scholar 

  24. Cao, L., Li, H., Dong, G., Lu, R.Q.: Event-triggered control for multiagent systems with sensor faults and input saturation. IEEE Trans. Syst. Man Cybern. 51(6), 3855–3866 (2022)

    Google Scholar 

  25. Wang, X.-L., Yang, G.-H.: Piecewise attack strategy design for T-S fuzzy cyber-physical systems. IEEE Trans. Syst. Man Cybern. (2022). https://doi.org/10.1109/TSMC.2022.3146282

    Article  Google Scholar 

  26. Jin, Z.H., Wang, Z.X., Zhang, X.F.: Cooperative control problem of Takagi-Sugeno fuzzy multiagent systems via observer based distributed adaptive sliding mode control. J. Franklin Inst. 359(8), 3405–3426 (2022)

    MathSciNet  MATH  Google Scholar 

  27. Zhang, J.-X., Yang, G.-H.: Fuzzy adaptive output feedback control of uncertain nonlinear systems with prescribed performance. IEEE Trans. Cybern. 48(5), 1342–1354 (2018)

    Google Scholar 

  28. Li, R.C., Zhang, X.F.: Adaptive sliding mode observer design for a class of T-S fuzzy descriptor fractional order systems. IEEE Trans. Fuzzy Syst. 28(9), 1951–1959 (2020)

    Google Scholar 

  29. Zhang, X.F., Zhao, Z.L.: Normalization and stabilization for rectangular singular fractional order T-S fuzzy systems. Fuzzy Sets Syst. 38, 140–153 (2020)

    MathSciNet  MATH  Google Scholar 

  30. Zhang, R.M., Zeng, D.Q., Park, J.H., Lam, H.-K., Xie, X.P.: Fuzzy sampled-data control for synchronization of T-S fuzzy reaction-diffusion neural networks with additive time-varying delays. IEEE Trans. Cybern. 51(5), 2384–2397 (2021)

    Google Scholar 

  31. Teh, C.Y., Kerk, Y.W., Tay, K.M., Lim, C.P.: On modeling of data-driven monotone zero-order TSK fuzzy inference systems using a system identification framework. IEEE Trans. Fuzzy Syst. 26(6), 3860–3874 (2018)

    Google Scholar 

  32. Li, Z., Pan, Y., Ma, J.: Disturbance observer-based fuzzy adaptive containment control of nonlinear multi-agent systems with input quantization. Int. J. Fuzzy Syst. 24, 574–586 (2022)

    Google Scholar 

  33. Jin, Z.H., Qin, Z.Y., Zhang, X.F., Guan, C.: A leader-following consensus problem via a distributed observer and fuzzy input-to-output small-gain theorem. IEEE Trans. Control Netw. Syst. 9(1), 62–74 (2022)

    MathSciNet  Google Scholar 

  34. Zhou, Q., Li, H.Y., Wu, C.W., Wang, L.J., Ahn, C.K.: Adaptive fuzzy control of nonlinear systems with unmodeled dynamics and input saturation using small-gain approach. IEEE Trans. Syst. Man Cybern. 47(8), 1979–1989 (2017)

    Google Scholar 

  35. Jin, Z.H., Wang, Z.X., Li, J.W.: Input-to-state stability of the nonlinear fuzzy systems via small-gain theorem and decentralized sliding mode control. IEEE Trans. Fuzzy Syst. 30(8), 2993–3008 (2022)

    Google Scholar 

  36. Zhang, J.-X., Yang, G.-H.: Fault-tolerant output-constrained control of unknown Euler-Lagrange systems with prescribed tracking accuracy. Automatica 111, 108606 (2020)

    MathSciNet  MATH  Google Scholar 

  37. Cheng, W., Xue, H., Liang, H., Wang, W.: Prescribed performance adaptive fuzzy control of stochastic nonlinear multi-agent systems with input hysteresis and saturation. Int. J. Fuzzy Syst. 24, 91–104 (2022)

    Google Scholar 

  38. Zhang, J.-X., Yang, G.-H.: Fault-tolerant fixed-time trajectory tracking control of autonomous surface vessels with specified accuracy. IEEE Trans. Industr. Electron. 67(6), 4889–4899 (2020)

    Google Scholar 

  39. Xing, S.Y., Zheng, W.X., Deng, F.Q., Chang, C.L.: An enhanced input delay approach to sampled-data stabilization for nonlinear stochastic singular systems based on T-S fuzzy models. IEEE Trans. Fuzzy Syst. 30(8), 2943–2956 (2022)

    Google Scholar 

  40. Chen, M., Yan, H., Zhang, H., Chi, M., Li, Z.: Dynamic event-triggered asynchronous control for nonlinear multiagent systems based on T-S fuzzy models. IEEE Trans. Fuzzy Syst. 29(9), 2580–2592 (2021)

    Google Scholar 

  41. Yao, D., Li, H., Lu, R., Shi, Y.: Distributed sliding-mode tracking control of second-order nonlinear multiagent systems: an event-triggered approach. IEEE Trans. Cybern. 50(9), 3892–3902 (2020)

    Google Scholar 

  42. Li, Y.X., Yang, G.H., Tong, S.: Fuzzy adaptive distributed event-triggered consensus control of uncertain nonlinear multiagent systems. IEEE Trans. Syst. Man Cybern. 49(9), 1777–1786 (2018)

    Google Scholar 

  43. Li, G., Yang, R.: Observer-based hybrid-triggered control for nonlinear networked control systems with disturbances. Int. J. Fuzzy Syst. (2022). https://doi.org/10.1007/s40815-022-01336-6

    Article  Google Scholar 

  44. Liu, J., Yin, T., Xie, X., Tan, E., Fei, S.: Event-triggered state estimation for T-S fuzzy neural networks with stochastic cyber-attacks. Int. J. Fuzzy Syst. 21(2), 532–544 (2019)

    MathSciNet  Google Scholar 

  45. Jin, Z.H., Sun, X.J., Qin, Z.Y., Ahn, C.K.: Fuzzy small-gain approach for the distributed optimization of T-S fuzzy cyber-physical systems. IEEE Trans. Cybern. (2022). https://doi.org/10.1109/TCYB.2022.3202576

    Article  Google Scholar 

  46. Jin, Z.H., Ahn, C.K., Li, J.: Momentum-based distributed continuous-time nonconvex optimization of nonlinear multi-agent systems via timescale separation. IEEE Trans. Netw. Sci. Eng. (2022). https://doi.org/10.1109/TNSE.2022.3225409

    Article  Google Scholar 

  47. Huang, C.-H., Wang, W.-J., Chiu, C.-H.: Design and implementation of fuzzy control on a two-wheel inverted pendulum. IEEE Trans. Industr. Electron. 58(7), 2988–3001 (2011)

    Google Scholar 

  48. Zhang, D.-W., Han, Q.-L., Jia, X.: Network-based output tracking control for T-S fuzzy systems using an event-triggered communication scheme. Fuzzy Sets Syst. 273(15), 26–48 (2015)

    MathSciNet  MATH  Google Scholar 

Download references

Acknowledgements

This work is supported by National Nature Science Foundation of China under Grant 62103289.

Author information

Author notes

  1. Hua Li, Dayu Zhang, Zhenghong Jin and Yuzhong Wang have contributed equally to this work.

Authors and Affiliations

  1. The School of Electronics and Information, Northwestern Polytechnical University, Xi’an, 710072, Shanxi, China

    Zhixiao Sun

  2. The Department of Industrial Engineering, Yonsei University, Seoul, 03722, Korea

    Hua Li

  3. School of Engineering, Huzhou University, Huzhou, 313000, Zhejiang, China

    Dayu Zhang

  4. The State Key Laboratory of Synthetical Automation for Process Industries, Northeastern University, Shenyang, 110819, Liaoning, China

    Zhenghong Jin

  5. The School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore, 639798, Singapore

    Zhenghong Jin

  6. The Department of Electronic and Electrical Engineering, Southern University of Science and Technology, Shenzhen, 518055, China

    Yuzhong Wang

Authors
  1. Zhixiao Sun
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Hua Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Dayu Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Zhenghong Jin
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Yuzhong Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding authors

Correspondence to Dayu Zhang or Zhenghong Jin.

Ethics declarations

Conflict of interest

We declare that we do not have any commercial or associative interest that represents a conflict of interest in connection with the work submitted.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Sun, Z., Li, H., Zhang, D. et al. Asymptotic State Agreement of T–S Fuzzy Multi-agent Systems: A Dynamic Event-Triggered Approach. Int. J. Fuzzy Syst. 25, 2476–2487 (2023). https://doi.org/10.1007/s40815-023-01512-2

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s40815-023-01512-2

Keywords

Search

Navigation