Skip to main content
SpringerLink
Log in

Leader-following Adaptive Guaranteed-performance Consensus Control for Multi-agent Systems With Exogenous Disturbance

  • Regular Papers
  • Control Theory and Applications
  • Published:
International Journal of Control, Automation and Systems Aims and scope Submit manuscript

Abstract

In this paper, the problem of leader-following distributed guaranteed-performance consensus for multi-agent systems (MASs) subject to exogenous disturbances is investigated. First, a disturbance observer is designed for each follower, which can be used to efficiently estimate the external disturbances. Next, an adaptive distributed state feedback consensus protocol with guaranteed performance constraints is proposed based on the above proposed observer. Most of the existing literature on guaranteed performance does not consider unknown disturbances. Unlike existing schemes, consensus control with fully distributed guaranteed-performance is accomplished using this protocol, which solves the consensus control problem of exogenous disturbances. The consensus criterion of adaptive guaranteed-performance is given by using Riccati inequality, and the adjustment method of consensus control gain is given by using linear matrix inequality for leader-following situation. At last, the derived analytical results are validated by presenting a simulation example.

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

Similar content being viewed by others

References

  1. Y.-W. Wang, Z.-H. Zeng, X.-K. Liu, and Z.-W. Liu, “Input-to-state stability of switched linear systems with unstabilizable modes under DoS attacks,” Automatica, vol. 146, 110607, 2022.

    Article  MathSciNet  Google Scholar 

  2. X. Zheng, H. Li, C. K. Ahn, and D. Yao, “NN-based fixed-time attitude tracking control for multiple unmanned aerial vehicles with nonlinear faults,” IEEE Transactions on Aerospace and Electronic Systems, vol. 59, no. 2, pp. 1738–1748, 2023.

    Google Scholar 

  3. X.-K. Liu, Y.-W. Wang, P. Lin, and P. Wang, “Distributed supervisory secondary control for a DC microgrid,” IEEE Transactions on Energy Conversion, vol. 35, no. 4, pp. 1736–1746, 2020.

    Article  ADS  Google Scholar 

  4. X. Du, X. Zhan, J. Wu, and H. Yan, “Performance analysis of MIMO information time-delay system under bandwidth, cyber-attack, and Gaussian white noise,” IEEE Transactions on Systems, Man, and Cybernetics: Systems, vol. 53, no. 4, pp. 2329–2338, 2023.

    Article  Google Scholar 

  5. X. Zhang, J. Wu, X. Zhan, T. Han, and H. Yan, “Observer-based adaptive time-varying formation-containment tracking for multiagent system With bounded unknown input,” IEEE Transactions on Systems, Man, and Cybernetics: Systems, vol. 53, no. 3, pp. 1479–1491, 2023.

    Article  Google Scholar 

  6. Z. Liu, X. Zhan, T. Han, and H. Yan, “Distributed adaptive finite-time bipartite containment control of linear multiagent systems,” IEEE Transactions on Circuits and Systems II: Express Briefs, vol. 69, no. 11, pp. 4354–4358, 2022.

    Google Scholar 

  7. Z.-W. Liu, X. Yu, Z.-H. Guan, B. Hu, and C. Li, “Pulse-modulated intermittent control in consensus of multiagent systems,” IEEE Transactions on Systems, Man, and Cybernetics: Systems, vol. 47, no. 5, pp. 783–793, 2017.

    Article  Google Scholar 

  8. Z.-W. Liu, G. Wen, X. Yu, Z.-H. Guan, and T. Huang, “Delayed impulsive control for consensus of multiagent systems with switching communication graphs,” IEEE Transactions on Cybernetics, vol. 50, no. 7, pp. 3045–3055, 2020.

    Article  PubMed  Google Scholar 

  9. G. Lin, H. Li, C. K. Ahn, and D. Yao, “Event-based finite-time neural control for human-in-the-loop UAV attitude systems,” IEEE Transactions on Neural Networks and Learning Systems, pp. 1–11, 2022, DOI: https://doi.org/10.1109/TNNLS.2022.3166531

  10. X. Wang, H. Wu, and J. Cao, “Global leader-following consensus in finite time for fractional-order multi-agent systems with discontinuous inherent dynamics subject to nonlinear growth,” Nonlinear Analysis: Hybrid Systems, vol. 37, 100888, 2020.

    MathSciNet  Google Scholar 

  11. F. Sun, X. Liao, and J. Kurths, “Mean-square consensus for heterogeneous multi-agent systems with probabilistic time delay,” Information Sciences, vol. 543, pp. 112–124, 2021.

    Article  MathSciNet  Google Scholar 

  12. J. Fu, G. Wen, T. Huang, and Z. Duan, “Consensus of multi-agent systems with heterogeneous input saturation levels,” IEEE Transactions on Circuits and Systems II: Express Briefs, vol. 66, no. 6, pp. 1053–1057, 2019.

    Google Scholar 

  13. X. Liu, K. Zhang, and W.-C. Xie, “Consensus seeking in multi-agent systems via hybrid protocols with impulse delays,” Nonlinear Analysis: Hybrid Systems, vol. 25, pp. 90–98, 2017.

    MathSciNet  Google Scholar 

  14. X. Jiang, G. Xia, and Z. Feng, “Non-fragile consensus control for singular multi-agent systems with Lipschitz nonlinear dynamics,” Neurocomputing, vol. 351, pp. 123–133, 2019.

    Article  Google Scholar 

  15. K. Li and X. Mu, “Necessary and sufficient conditions for leader-following consensus of multi-agent systems with random switching topologies,” Nonlinear Analysis: Hybrid Systems, vol. 37, 100905, 2020.

    MathSciNet  Google Scholar 

  16. H. Liang, L. Chen, Y. Pan, and H.-K. Lam, “Fuzzy-based robust precision consensus tracking for uncertain networked systems with cooperative-antagonistic interactions,” IEEE Transactions on Fuzzy Systems, vol. 31, no. 4, pp. 1362–1376, 2023.

    Article  Google Scholar 

  17. Y. Wu, Q. Liang, Y. Zhao, J. Hu, and L. Xiang, “Adaptive bipartite consensus control of general linear multi-agent systems using noisy measurements,” European Journal of Control, vol. 59, pp. 123–128, 2021.

    Article  MathSciNet  Google Scholar 

  18. H. Zhang, Y. Liu, and Y. Wang, “Observer-based finite-time adaptive fuzzy control for nontriangular nonlinear systems with full-state constraints,” IEEE Transactions on Cybernetics, vol. 51, no. 3, pp. 1110–1120, 2021.

    Article  PubMed  Google Scholar 

  19. H. Zhang, Y. Liu, J. Dai, and Y. Wang, “Command filter based adaptive fuzzy finite-time control for a class of uncertain nonlinear systems with hysteresis,” IEEE Transactions on Fuzzy Systems, vol. 29, no. 9, pp. 2553–2564, 2021.

    Article  Google Scholar 

  20. L. Cao, Y. Pan, H. Liang, and T. Huang, “Observer-based dynamic event-triggered control for multiagent systems with time-varying delay,” IEEE Transactions on Cybernetics, vol. 53, no. 5, pp. 3376–3387, 2023.

    Article  PubMed  Google Scholar 

  21. J. Zhang, H. Zhang, S. Sun, and Z. Gao, “Leader-follower consensus control for linear multi-agent systems by fully distributed edge-event-triggered adaptive strategies,” Information Sciences, vol. 555, pp. 314–338, 2021.

    Article  MathSciNet  Google Scholar 

  22. Z.-W. Liu, Y.-L. Shi, H. Yan, B.-X. Han, and Z.-H. Guan, “Secure consensus of multiagent systems via impulsive control subject to deception attacks,” IEEE Transactions on Circuits and Systems II: Express Briefs, vol. 70, no. 1, pp. 166–170, 2023.

    Google Scholar 

  23. Z. Gao, H. Zhang, J. Duan, and Y. Cai, “Guaranteed-performance consensus for descriptor nonlinear multiagent systems based on distributed nonlinear consensus protocol,” Neurocomputing, vol. 383, pp. 359–367, 2020.

    Article  Google Scholar 

  24. X. Jiang, G. Xia, Z. Feng, and Z. Jiang, “Non-fragile guaranteed-performance H∞ leader-following consensus of Lipschitz nonlinear multi-agent systems with switching topologies,” Nonlinear Analysis: Hybrid Systems, vol. 38, 100913, 2020.

    MathSciNet  Google Scholar 

  25. Z. Zhang, S. Zhang, H. Li, and W. Yan, “Cooperative robust optimal control of uncertain multi-agent systems,” Journal of the Franklin Institute, vol. 357, no. 14, pp. 9467–9483, 2020.

    Article  MathSciNet  Google Scholar 

  26. J Zhang, D.-W. Ding, Y. Ren, and X. Sun, “Distributed robust group output synchronization control for heterogeneous uncertain linear multi-agent systems,” ISA Transactions, vol. 134, pp. 108–121, 2023.

    Article  PubMed  Google Scholar 

  27. N. Li, X. Wu, J. Feng, Y. Xu, and J. Lü, “Fixed-time synchronization of coupled neural networks with discontinuous activation and mismatched parameters,” IEEE Transactions on Neural Networks and Learning Systems, vol. 32, no. 6, pp. 2470–2482, 2021.

    Article  MathSciNet  CAS  PubMed  Google Scholar 

  28. Y.-Y. Qian, L. Liu, and G. Feng, “Distributed event-triggered adaptive control for consensus of linear multiagent systems with external disturbances,” IEEE Transactions on Cybernetics, vol. 50, no. 5, pp. 2197–2208, 2020.

    Article  PubMed  Google Scholar 

  29. A. Mei, G. Wen, Z. Peng, A. Rahmani, and T. Huang, “Vortex formation control for linear multiagent systems with unknown leader input and external disturbances,” IEEE Transactions on Circuits and Systems II: Express Briefs, vol. 70, no. 2, pp. 636–640, 2023.

    Google Scholar 

  30. H. Su, Y. Ye, Y. Qiu, Y. Cao and M. Z. Q. Chen, “Semiglobal output consensus for discrete-time switching networked systems subject to input saturation and external disturbances,” IEEE Transactions on Cybernetics, vol. 49, no. 11, pp. 3934–3945, 2019.

    Article  PubMed  Google Scholar 

  31. W. Jia and J. Wang, “Partial-nodes-based distributed fault detection and isolation for second-order multiagent systems with exogenous disturbances,” IEEE Transactions on Cybernetics, vol. 52, no. 4, pp. 2518–2530, 2022.

    Article  PubMed  Google Scholar 

  32. Y. Wu and L. Liu, “Distributed average tracking for linear heterogeneous multi-agent systems with external disturbances,” IEEE Transactions on Network Science and Engineering, vol. 8, no. 4, pp. 3491–3500, 2021.

    Article  MathSciNet  Google Scholar 

  33. X. Ruan, J. Feng, C. Xu, and J. Wang, “Observer-based dynamic event-triggered strategies for leader-following consensus of multi-agent systems with disturbances,” IEEE Transactions on Network Science and Engineering, vol. 7, no. 4, pp. 3148–3158, 2020.

    Article  MathSciNet  Google Scholar 

  34. Y. Wu, J. Hu, L. Xiang, Q. Liang, and K. Shi, “Finite-time output regulation of linear heterogeneous multi-agent systems,” IEEE Transactions on Circuits and Systems II: Express Briefs, vol. 69, no. 3, pp. 1248–1252, 2022.

    Google Scholar 

  35. X. Jiang, G. Xia, and Z. Feng, “Non-fragile consensus control for singular multi-agent systems with Lipschitz nonlinear dynamics,” Neurocomputing, vol. 351, pp. 123–133, 2019.

    Article  Google Scholar 

  36. Z. Gao, H. Zhang, J. Duan, and Y. Cai, “Guaranteed-performance consensus for descriptor nonlinear multiagent systems based on distributed nonlinear consensus protocol,” Neurocomputing, vol. 383, pp. 359–367, 2020.

    Article  Google Scholar 

  37. J. Xu, G. Zhang, J. Zeng, J. Xi, and B. Du, “Robust guaranteed cost consensus for high-order discrete-time multi-agent systems with parameter uncertainties and time-varying delays,” IET Control Theory & Applications, vol. 11, no. 5, pp. 647–667, 2017.

    Article  MathSciNet  Google Scholar 

  38. J. Xi, Z. Fan, H. Liu, and T. Zheng, “Guaranteed-cost consensus for multiagent networks with Lipschitz nonlinear dynamics and switching topologies,” International Journal of Robust and Nonlinear Control, vol. 28, no. 7, pp. 2841–2852, 2018.

    Article  MathSciNet  Google Scholar 

  39. Y. Zhao and W. Zhang, “Guaranteed cost consensus protocol design for linear multi-agent systems with sampleddata information: An input delay approach,” ISA Transactions, vol. 67, pp. 87–97, 2017.

    Article  PubMed  Google Scholar 

  40. Y. Li, Y. Wu, and S. He, “Network-based leader-following formation control of second-order autonomous unmanned systems,” Journal of the Franklin Institute, vol. 358, no. 1, pp. 757–775, 2021.

    Article  MathSciNet  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. School of College of Mechatronics and Control Engineering, Hubei Normal University, Huangshi, 435002, China

    Na Zhao, Jie Wu, Xisheng Zhan & Tao Han

  2. School of Information Science and Engineering, East China University of Science and Technology, Shanghai, 200237, China

    Huaicheng Yan

Authors
  1. Na Zhao
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Jie Wu
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Xisheng Zhan
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Tao Han
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Huaicheng Yan
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Xisheng Zhan.

Ethics declarations

There is no conflict of interest in this article.

Additional information

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

This paper was partially supported by the National Natural Science Foundation of China under Grants 62271195, 62303169 and 62072164, and Outstanding Youth Science and Technology Innovation Team in Hubei Province under Grant T2022027 and 2023AFD006.

Na Zhao is pursuing an M.S. degree in the College of Mechatronics and Control Engineering, Hubei Normal University, Huangshi, China. She received her B.S. degree from Taishan University, Tai’an, China in 2021. Her research interests include cooperative control of multi-agent systems and complex networks.

Jie Wu is a professor in the College of Mechatronics and Control Engineering, Hubei Normal University. She received her B.S. and M.S. degrees in control theory and control engineering from the Liaoning Shihua University, Fushun, China, in 2004 and in 2007, respectively. Her research interests include networked control systems, robust control, and complex network.

Xisheng Zhan is a professor in the College of Mechatronics and Control Engineering, Hubei Normal University. He received his B.S. and M.S. degrees in control theory and control engineering from the Liaoning Shihua University, Fushun, China, in 2003 and in 2006, respectively. He received a Ph.D. degree in control theory and applications from the Department of Control Science and Engineering, Huazhong University of Science and Technology, Wuhan, China, in 2012. His research interests include networked control systems and robust control.

Tao Han received his Ph.D. degree from the College of Automation, Huazhong University of Science and Technology, Wuhan, China in 2017, and he is currently a lecturer in the College of Mechatronics and Control Engineering, Hubei Normal University. His research interests include cooperative control of multi-agent systems and complex networks.

Huaicheng Yan is a Professor with the School of Information Science and Engineering, East China University of Science and Technology. He received his Ph.D. degree in control theory and control engineering from the Huazhong University of Science and Technology, Wuhan, China, in 2007. His current research interests include networked systems and multi-agent systems.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Zhao, N., Wu, J., Zhan, X. et al. Leader-following Adaptive Guaranteed-performance Consensus Control for Multi-agent Systems With Exogenous Disturbance. Int. J. Control Autom. Syst. 22, 892–901 (2024). https://doi.org/10.1007/s12555-022-1225-y

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12555-022-1225-y

Keywords

Search

Navigation