Skip to main content
SpringerLink
Log in

Adaptive Variable Impulsive Synchronization of Multi-agent Systems

  • Original Article
  • Published:
Journal of Electrical Engineering & Technology Aims and scope Submit manuscript

Abstract

This paper is concerned with the adaptive synchronization of multi-agent systems via variable impulsive control method. Based on the theory of impulsive differential equations, adaptive control technique, Lyapunov stability theory and comparison system method, some novel adaptive synchronization conditions with impulsive time window are given to realize the synchronization of multi-agent nonlinear systems with uncertain parameters. Different from the existing investigations of impulsive synchronization of multi-agent systems, the proposed impulsive control protocol with impulsive time window is more effective in practical systems. Finally, two simulation results demonstrate the validity of the theoretical results.

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

Similar content being viewed by others

References

  1. Wang YW, Liu XK, Xiao JW, Shen YJ (2018) Output formation-containment of interacted heterogeneous linear systems by distributed hybrid active control. Automatica 93:26–32

    Article  MathSciNet  Google Scholar 

  2. Ma TD (2015) Synchronization of multi-agent stochastic impulsive perturbed chaotic delayed neural networks with switching topology. Neurocomputing 151:1392–1406

    Article  Google Scholar 

  3. Ma TD, Lewis FL, Song YD (2016) Exponential synchronization of nonlinear multi-agent systems with time delays and impulsive disturbances. Int J Robust Nonlinear Control 26:1615–1631

    Article  MathSciNet  Google Scholar 

  4. Wang YW, Wei YW, Liu XK, Zhou N, Cassandras CG (2018) Optimal persistent monitoring using second-order agents with physical constraints. IEEE Trans Autom Control. https://doi.org/10.1109/tac.2018.2879946

    Article  MATH  Google Scholar 

  5. Yang DS, Pang YH, Zhou BW, Li K (2019) Fault diagnosis for energy internet using correlation processing-based convolutional neural networks. IEEE Trans Syst Man Cybern Syst 49:1739–1748. https://doi.org/10.1109/tsmc.2019.2919940

    Article  Google Scholar 

  6. Lewis FL, Cui B, Ma TD, Song YD, Zhao CH (2016) Heterogeneous multi-agent systems: reduced-order synchronization and geometry. IEEE Trans Autom Control 61(5):1391–1396

    Article  MathSciNet  Google Scholar 

  7. Liu ZW, Saberi A, Stoorvogel AA, Zhang MR (2018) Passivity-based state synchronization of homogeneous multi-agent systems via static protocol in the presence of input saturation. Int J Robust Nonlinear Control 28(7):2720–2741

    Article  Google Scholar 

  8. Cui B, Zhao CH, Ma TD, Feng C (2017) Leaderless and leader-following consensus of multi-agent chaotic systems with unknown time delays and switching topologies. Nonlinear Anal Hybrid Syst 24:115–131

    Article  MathSciNet  Google Scholar 

  9. Liu ZW, Stoorvogel AA, Saberi A, Nojavanzadeh D (2019) Squared-down passivity based state synchronization of homogeneous continuous-time multi-agent systems via static protocol in presence of time-varying topology. Int J Robust Nonlinear Control 29:3821–3840. https://doi.org/10.1002/rnc.4585

    Article  MATH  Google Scholar 

  10. Ma TD, Li T, Cui B (2018) Coordination of fractional-order nonlinear multi-agent systems via distributed impulsive control. Int J Syst Sci 49(1):1–14

    Article  MathSciNet  Google Scholar 

  11. Wang FY, Liu ZX, Cheng ZQ (2018) A novel leader-following consensus of multi-agent systems with smart leader. Int J Control Autom Syst 16(4):1483–1492

    Article  Google Scholar 

  12. Ma TD, Cui B, Wang YJ, Liu K (2019) Stochastic synchronization of delayed multi-agent networks with intermittent communications: an impulsive framework. Int J Robust Nonlinear Control 29(13):4537–4561

    Article  Google Scholar 

  13. Wang YW, Lei Y, Bian T, Guan ZH (2019) Distributed control of nonlinear multi-agent systems with unknown and nonidentical control directions via event-triggered communication. IEEE Trans Cybern 1:1. https://doi.org/10.1109/tcyb.2019.2908874

    Article  Google Scholar 

  14. Shen Y, Li F, Zhang D, Wang YW, Liu Y (2019) Event-triggered output feedback H∞ control for networked control systems. Int J Robust Nonlinear Control 29(1):166–179

    Article  MathSciNet  Google Scholar 

  15. Liu ZW, Zhang MR, Saberi A, Stoorvogel AA (2018) State synchronization of multi-agent systems via static or adaptive nonlinear dynamic protocols. Automatica 95:316–327

    Article  MathSciNet  Google Scholar 

  16. Fu J, Bai JF, Lai JJ, Li PD, Yu M, Lam HK (2019) Adaptive fuzzy control of a magnetorheological elastomer vibration isolation system with time-varying sinusoidal excitations. J Sound Vib 456:386–406

    Article  Google Scholar 

  17. Morstyn T, Savkin AV, Hredzak B, Agelidis VG (2018) Multi-agent sliding mode control for state of charge balancing between battery energy storage systems distributed in a DC microgrid. IEEE Trans Smart Grid 9(5):4735–4743

    Article  Google Scholar 

  18. Zhao L, Yu JP, Lin C, Yu HS (2017) Distributed adaptive fixed-time consensus tracking for second-order multi-agent systems using modified terminal sliding mode. Appl Math Comput 312:23–35

    MathSciNet  MATH  Google Scholar 

  19. Wang Y, Ma ZJ, Zheng S, Chen GR (2017) Pinning control of lag-consensus for second-order nonlinear multi-agent systems. IEEE Trans Cybern 47(8):2203–2211

    Article  Google Scholar 

  20. Lu JQ, Ding CD, Lou JG, Cao JD (2015) Outer synchronization of partially coupled dynamical networks via pinning impulsive controllers. J Franklin Inst 352(11):5024–5041

    Article  MathSciNet  Google Scholar 

  21. Huang WC, Huang YW, Chen SB (2018) Robust consensus control for a class of second-order multi-agent systems with uncertain topology and disturbances. Neurocomputing 313:426–435

    Article  Google Scholar 

  22. Yang DS, Li T, Zhang HG, Xie XP (2019) Event-trigger-based robust control for nonlinear constrained-input systems using reinforcement learning method. Neurocomputing 349:158–170

    Article  Google Scholar 

  23. Ao WG, Ma TD, Sanchez RV, Gan HT (2019) Finite-time and fixed-time impulsive synchronization of chaotic systems. J Franklin Inst. https://doi.org/10.1016/j.jfranklin.2019.07.023

    Article  Google Scholar 

  24. Ma TD, Zhang ZL, Cui B (2018) Adaptive consensus of multi-agent systems via odd impulsive control. Neurocomputing 321:139–145

    Article  Google Scholar 

  25. Ma TD, Fu J (2011) On the exponential synchronization of stochastic impulsive chaotic delayed neural networks. Neurocomputing 74(5):857–862

    Article  Google Scholar 

  26. Li XD, Wu JH (2018) Sufficient stability conditions of nonlinear differential systems under impulsive control with state-dependent delay. IEEE Trans Autom Control 63(1):306–311

    Article  MathSciNet  Google Scholar 

  27. Ma TD, Zhang ZL, Cui B (2019) Variable impulsive consensus of nonlinear multi-agent systems. Nonlinear Anal Hybrid Syst 31:1–18

    Article  MathSciNet  Google Scholar 

  28. Wang X, Wang H, Li CD, Huang TW (2017) Stability analysis of hybrid neural networks with impulsive time window. Int J Biomath 10(1):1750011

    Article  MathSciNet  Google Scholar 

  29. Zou LM, Peng Y, Feng YM, Tu ZW (2018) Impulsive control of nonlinear systems with impulse time window and bounded gain error. Nonlinear Anal Model Control 23(1):40–49

    Article  MathSciNet  Google Scholar 

  30. Feng YM, Yu JZ, Li CD, Huang TW, Che HJ (2017) Linear impulsive control system with impulse time windows. J Vib Control 23(1):111–118

    Article  MathSciNet  Google Scholar 

  31. Zhou YH, Li CD, Huang TW, Wang X (2017) Impulsive stabilization and synchronization of Hopfield-type neural networks with impulse time window. Neural Comput Appl 28(4):775–782

    Article  Google Scholar 

  32. Feng YM, Li CD, Huang TW (2016) Periodically multiple state-jumps impulsive control systems with impulse time windows. Neurocomputing 193:7–13

    Article  Google Scholar 

  33. Wang X, Wang H, Li CD, Huang TW (2016) Synchronization of coupled delayed switched neural networks with impulsive time window. Nonlinear Dyn 84(3):1747–1757

    Article  MathSciNet  Google Scholar 

  34. Sun JT, Zhang YP, Wu QD (2003) Less conservative conditions for asymptotic stability of impulsive control systems. IEEE Trans Autom Control 48(5):829–831

    Article  Google Scholar 

  35. Tan J, Li CD, Huang TW (2018) Comparison system method for a class of stochastic systems with variable-time impulses. Int J Control Autom Syst 16(2):702–708

    Article  Google Scholar 

  36. Feng YM, Li CD (2017) Comparison system of impulsive control system with impulse time windows. J Intell Fuzzy Syst 32(6):4197–4204

    Article  Google Scholar 

  37. Tan J, Li CD, Huang TW (2015) Stability of impulsive systems with time window via comparison method. Int J Control Autom Syst 13(6):1346–1350

    Article  Google Scholar 

  38. Ma TD, Li T, Cui B (2018) Adaptive impulsive consensus of multi-agent systems. Int J Robust Nonlinear Control 28(6):2031–2046

    Article  Google Scholar 

  39. Ai XL, Yu JQ, Jia ZY, Shen YC, Ma PB, Yang D (2017) Adaptive robust consensus tracking for nonlinear second-order multi-agent systems with heterogeneous uncertainties. Int J Robust Nonlinear Control 27(18):5082–5096

    Article  MathSciNet  Google Scholar 

  40. Ma TD, Yu TT, Cui B (2018) Adaptive synchronization of multi-agent systems via variable impulsive control. J Franklin Inst 355(15):7490–7508

    Article  MathSciNet  Google Scholar 

  41. Wang R, Dong XW, Li QD, Ren Z (2017) Distributed adaptive control for time-varying formation of general linear multi-agent systems. Int J Syst Sci 48(16):3491–3503

    Article  MathSciNet  Google Scholar 

  42. Shilnik LP (1994) Chua’s circuit: rigorous result and future problems. Int J Bifurc Chaos 3:489–519

    Article  MathSciNet  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Electric Power Research Institute of Guangxi Power Grid Co., Ltd., Nanning, 530023, China

    Like Gao, Jing Xiao, Xiaoxuan Guo, Shifeng Ou, Yangjun Zhou, Ning Wu, Yubin Feng & Zhiyang Yao

Authors
  1. Like Gao
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Jing Xiao
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Xiaoxuan Guo
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Shifeng Ou
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Yangjun Zhou
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Ning Wu
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Yubin Feng
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Zhiyang Yao
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Xiaoxuan Guo.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Gao, L., Xiao, J., Guo, X. et al. Adaptive Variable Impulsive Synchronization of Multi-agent Systems. J. Electr. Eng. Technol. 15, 259–268 (2020). https://doi.org/10.1007/s42835-019-00315-6

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s42835-019-00315-6

Keywords

Search

Navigation