Nonlinear Dynamics

, Volume 93, Issue 2, pp 863–871 | Cite as

Distributed fault detection and isolation for leader–follower multi-agent systems with disturbances using observer techniques

  • Yue Quan
  • Wen Chen
  • Zhihai Wu
  • Li Peng
Original Paper


For the purpose of fault detection and isolation for leader–follower multi-agent systems with disturbances, a sliding-mode observer is designed using local information and suitable auxiliary information received from neighbor agents. By means of the observer, each agent can estimate the overall state of follower agents even if they are not directly connected. Then, using the relative output estimations, a residual vector is developed to detect and isolate the fault occurring on any follower agent of the leader–follower multi-agent systems. By the end, numerical simulations are employed to verify the effectiveness of the theoretical results.


Leader–follower multi-agent systems Fault detection and isolation Sliding-mode observer 



This work is supported in part by National Natural Science Foundation of China under Grant 61374047 and 61203147, in part by US National Science Foundation under Grant EPCN 1507096, and in part by Natural Science Foundation of Jiangsu Higher Education Institutions under Grant 16KJB520003.


  1. 1.
    Olfati-Saber, R., Fax, J.A., Murray, R.M.: Consensus and cooperation in networked multi-agent systems. Proc. IEEE 95, 215–233 (2007)CrossRefzbMATHGoogle Scholar
  2. 2.
    Liu, C.L., Tian, Y.P.: Survey on consensus problem of multi-agent systems with time delays. Control Decis. 24, 1600–1601 (2009)MathSciNetGoogle Scholar
  3. 3.
    Oh, K.K., Park, M.C., Ahn, H.S.: A survey of multi-agent formation control. Automatica 53(C), 424–440 (2015)MathSciNetCrossRefzbMATHGoogle Scholar
  4. 4.
    Cao, Y., Yu, W., Ren, W., Chen, G.: An overview of recent progress in the study of distributed multi-agent coordination. IEEE Trans. Ind. Inform. 9, 427–438 (2013)CrossRefGoogle Scholar
  5. 5.
    Boem, F., Ferrari, R.M.G., Parisini, T., Polycarpou ,M.M.: Distributed fault detection for uncertain nonlinear systems: a network delay compensation strategy. In: Proceedings of the American Control Conference, Washington, DC, USA, June, pp. 3549–3554 (2013)Google Scholar
  6. 6.
    Zhang, Q., Zhang, X.: Distributed sensor fault diagnosis in a class of interconnected nonlinear uncertain systems. Ann. Rev. Control 37, 170–179 (2013)CrossRefGoogle Scholar
  7. 7.
    Panda, M., Khilar, P.M.: Distributed self fault diagnosis algorithm for large scale wireless sensor networks using modified three sigma edit test. Ad Hoc Netw. 25(PA), 170–184 (2015)CrossRefGoogle Scholar
  8. 8.
    Yan, X.G., Edwards, C.: Robust decentralized actuator fault detection and estimation for large-scale systems using a sliding mode observer. Int. J. Control 81, 591–606 (2008)MathSciNetCrossRefzbMATHGoogle Scholar
  9. 9.
    Zhang, Q.: Distributed sensor fault detection and isolation for multimachine power systems. Int. J. Robust Nonlinear Control 24, 1403–1430 (2014)CrossRefzbMATHGoogle Scholar
  10. 10.
    Meskin, N., Khorasani, K.: Actuator fault detection and isolation for a network of unmanned vehicles. IEEE Trans. Autom. Control 54, 835–840 (2009)MathSciNetCrossRefzbMATHGoogle Scholar
  11. 11.
    Zhang, D., Zhang, W., Yu, L., Wang, Q.G.: Distributed fault detection for a class of large-scale systems with multiple incomplete measurements. J. Frankl. Inst. 352, 3730–3749 (2015)MathSciNetCrossRefGoogle Scholar
  12. 12.
    Wang, G., Yi, C.: Fault estimation for nonlinear systems by an intermediate estimator with stochastic failure. Nonlinear Dyn. 89, 1–10 (2017)CrossRefGoogle Scholar
  13. 13.
    Wang, H., Ye, D., Yang, G.H.: Actuator fault diagnosis for uncertain TS fuzzy systems with local nonlinear models. Nonlinear Dyn. 76, 1977–1988 (2014)MathSciNetCrossRefzbMATHGoogle Scholar
  14. 14.
    Sundaram, S., Hadjicostis, C.N.: Distributed function calculation via linear iterative strategies in the presence of malicious agents. IEEE Trans. Autom. Control 56, 1495–1508 (2011)MathSciNetCrossRefzbMATHGoogle Scholar
  15. 15.
    Pasqualetti, F., Drfler, F., Bullo, F.: Attack detection and identification in cyber–physical systems. IEEE Trans. Autom. Control 58, 2715–2729 (2013)MathSciNetCrossRefzbMATHGoogle Scholar
  16. 16.
    Shames, I., Teixeira, A.M.H., Sandberg, H., Johansson, K.H.: Distributed fault detection for interconnected second-order systems. Automatica 47, 2757–2764 (2011)MathSciNetCrossRefzbMATHGoogle Scholar
  17. 17.
    Teixeira, A., Shames, I., Sandberg, H., Johansson, K.H.: Distributed fault detection and isolation resilient to network model uncertainties. IEEE Trans. Cybern. 44, 2024–2037 (2014)CrossRefGoogle Scholar
  18. 18.
    Shi, J., He, X., Wang, Z., Zhou, D.: Distributed fault detection for a class of second-order multi-agent systems: an optimal robust observer approach. IET Control Theory Appl. 8, 1032–1044 (2014)MathSciNetCrossRefGoogle Scholar
  19. 19.
    Liu, X., Gao, X., Han, J.: Observer-based fault detection for high-order nonlinear multi-agent systems. J. Frankl. Inst. 353, 72–94 (2016)MathSciNetCrossRefGoogle Scholar
  20. 20.
    Quan, Y., Chen, W., Wu, Z., Peng, L.: Observer-based distributed fault detection and isolation for heterogeneous discrete-time multi-agent systems with disturbances. IEEE Access 4, 4652–4658 (2016)CrossRefGoogle Scholar
  21. 21.
    Arrichiello, F., Marino, A., Pierri, F.: Observer-based decentralized fault detection and isolation strategy for networked multirobot systems. IEEE Trans. Control Syst. Technol. 23, 1465–1476 (2015)CrossRefGoogle Scholar
  22. 22.
    Marino, A., Pierri, F., Chiacchio, P., Chiaverini, S.: Distributed fault detection and accommodation for a class of discrete-time linear systems. In: Proceedings of IEEE International Conference on Information and Automation, Lijiang, China, August, pp. 469–474 (2015)Google Scholar
  23. 23.
    Hong, Y., Chen, G., Bushnell, L.: Distributed observers design for leader-following control of multi-agent networks. Automatica 44, 846–850 (2008)MathSciNetCrossRefzbMATHGoogle Scholar
  24. 24.
    Wu, Z., Peng, L., Xie, L., Wen, J.: Stochastic bounded consensus tracking of leader–follower multi-agent systems with measurement noises based on sampled-data with small sampling delay. Phys. A 392, 918–928 (2013)MathSciNetCrossRefGoogle Scholar
  25. 25.
    Xu, X., Chen, S., Huang, W., Gao, L.: Leader-following consensus of discrete-time multi-agent systems with observer-based protocols. Neurocomputing 118, 334–341 (2013)CrossRefGoogle Scholar
  26. 26.
    Djaidja, S., Wu, Q.H., Fang, H.: Leader-following consensus of double-integrator multi-agent systems with noisy measurements. Int. J. Control Autom. Syst. 13, 17–24 (2015)CrossRefGoogle Scholar
  27. 27.
    Hu, J., Hong, Y.: Leader-following coordination of multi-agent systems with coupling time delays. Physica A 374, 853–863 (2007)CrossRefGoogle Scholar
  28. 28.
    Zhou, B., Liao, X.: Leader-following second-order consensus in multi-agent systems with sampled data via pinning control. Nonlinear Dyn. 78, 555–569 (2014)MathSciNetCrossRefzbMATHGoogle Scholar
  29. 29.
    Li, W., Chen, Z., Liu, Z.: Leader-following formation control for second-order multiagent systems with time-varying delay and nonlinear dynamics. Nonlinear Dyn. 72, 803–812 (2013)MathSciNetCrossRefzbMATHGoogle Scholar
  30. 30.
    Peng, Z., Wang, D., Li, T., et al.: Leaderless and leader-follower cooperative control of multiple marine surface vehicles with unknown dynamics. Nonlinear Dyn. 74, 95–106 (2013)MathSciNetCrossRefzbMATHGoogle Scholar
  31. 31.
    Zhang, K., Liu, G., Jiang, B.: Robust unknown input observer-based fault estimation of leader-follower linear multi-agent systems. Circuits Syst. Signal Process. 36(2), 1–18 (2016)Google Scholar
  32. 32.
    Antonelli, G., Arrichiello, F., Caccavale, F., Marino, A.: A decentralized controller–observer scheme for multi-agent weighted centroid tracking. IEEE Trans. Autom. Control 58, 1310–1316 (2013)MathSciNetCrossRefzbMATHGoogle Scholar
  33. 33.
    Hu, G.D., Liu, M.: The weighted logarithmic matrix norm and bounds of the matrix exponential. Linear Algebra Appl. 390, 145–154 (2004)MathSciNetCrossRefzbMATHGoogle Scholar

Copyright information

© Springer Science+Business Media B.V., part of Springer Nature 2018

Authors and Affiliations

  1. 1.School of Electrical and Electronic EngineeringAnhui Science and Technology UniversityFengyangPeople’s Republic of China
  2. 2.Jiangnan UniversityWuxiPeople’s Republic of China
  3. 3.Division of Engineering TechnologyWayne State UniversityDetroitUSA

Personalised recommendations