AIMS Mathematics, 2020, 5(3): 2780-2800. doi: 10.3934/math.2020179

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Adaptive neural networks event-triggered fault-tolerant consensus control for a class of nonlinear multi-agent systems

1 School of Engineering, Bohai University, Jinzhou 121013, Liaoning, China
2 School of Mathematics and Physics, Bohai University, Jinzhou 121013, Liaoning, China

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This paper studies the event-triggered fault-tolerant control problem of nonlinear multi-agent systems. The goal is to ensure the stability of event-based sampling multi-agent systems when the actuator faults occurs. The neural networks approximate property is used to approximate unknown ideal control parameters, which can reduce the exact requirements of control parameters. Based on the states information of neighboring agents, a distributed fault-tolerant consensus controller is designed for the leaderless multi-agent systems. Moreover, an event-triggered mechanism with special definition of event-triggered error is applied to reduce the amount of communications. In addition, the Zeno behaviour is avoided. By using Lyapunov stability theory, it is proved that all signals are bounded in the closed-loop systems. Finally, a numerical simulation result is presented to prove the effectiveness of the proposed method.
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# References

1. J. Qin, Q. Ma, Y. Shi, et al. Recent advances in consensus of multi-agent systems: A brief survey, IEEE Trans. Ind. Electron., 64 (2016), 4972-4983.

2. J. Zhang, X. Chen, G. Gu, State Consensus for Discrete-time Multi-agent Systems over Timevarying Graphs, arXiv preprint arXiv:1710.03398, 2017.

3. X. Liang, C. Xu, D. Wang, Adaptive neural network control for marine surface vehicles platoon with input saturation and output constraints, AIMS Math., 5 (2020), 587-602.

4. P. Colli, G. Gilardi, J. Sprekels, Distributed optimal control of a nonstandard nonlocal phase field system, AIMS Math., 1 (2016), 225-260.

5. H. Liang, L. Zhang, Y. Sun, et al. Containment control of semi-markovian multi-agent systems with switching topologies, IEEE Trans. Syst. Man Cybern. Syst., 2019.

6. W. Wang, H. Liang, Y. Pan, et al. Prescribed performance adaptive fuzzy containment control for nonlinear multi-agent systems using disturbance observer, IEEE Trans. Cybern., 2020.

7. Y. Zhang, H. Liang, H. Ma, et al. Distributed adaptive consensus tracking control for nonlinear multi-agent systems with state constraints, Appl. Math. Comput., 326 (2018), 16-32.

8. W. Liu, S. Zhou, Y. Qi, et al. Leaderless consensus of multi-agent systems with lipschitz nonlinear dynamics and switching topologies, Neurocomputing, 173 (2016), 1322-1329.

9. D. Yang, X. Li, J. Qiu, Output tracking control of delayed switched systems via state-dependent switching and dynamic output feedback, Nonlinear Anal. Hybrid. Syst., 32 (2019), 294-305.

10. W. Sun, S. Su, J. Xia, et al. Adaptive fuzzy tracking control of flexible-joint robots with full-state constraints, IEEE Trans. Syst. Man Cybern. Syst., 49 (2018), 2201-2209.

11. M. Tong, W. Lin, X. Huo, et al. A model-free fuzzy adaptive trajectory tracking control algorithm based on dynamic surface control, Int. J. Adv. Robot. Syst., 16 (2019), 1729881419894417.

12. Y. Han, C. Li, Z. Zeng, et al. Exponential consensus of discrete-time nonlinear multi-agent systems via relative state-dependent impulsive protocols, Neural Networks, 108 (2018), 192-201.

13. K. Liu, H. Zhu, J. Lu, Bridging the gap between transmission noise and sampled data for robust consensus of multi-agent systems, IEEE Trans. Circuits Syst. I, 62 (2015), 1836-1844.

14. W. Wang, H. Liang, Y. Zhang, et al. Adaptive cooperative control for a class of nonlinear multiagent systems with dead zone and input delay, Nonlinear Dyn., 96 (2019), 2707-2719.

15. Q. Zhou, W. Wang, H. Liang, et al. Observer-based event-triggered fuzzy adaptive bipartite containment control of multi-agent systems with input quantization, IEEE Trans. Fuzzy Syst., 2019.

16. X. Li, Q. Zhou, P. Li, et al. Event-triggered consensus control for multi-agent systems against false data injection attacks, IEEE Trans. Cybern., 2019.

17. Q. Zhou, W. Wang, H. Ma, et al. Event-triggered fuzzy adaptive containment control for nonlinear multi-agent systems with unknown Bouc-Wen hysteresis input, IEEE Trans. Fuzzy Syst., 2019.

18. Z. Gu, P. Shi, D. Yue, et al. Decentralized adaptive event-triggered Hfiltering for a class of networked nonlinear interconnected systems, IEEE Trans. Cybern., 49 (2018), 1570-1579.

19. Z. Chen, Q. Han, Y. Yan, et al. How often should one update control and estimation: review of networked triggering techniques, Sci. China Inf. Sci., 63 (2020).

20. L. Xing, C. Wen, Z. Liu, et al. Event-triggered adaptive control for a class of uncertain nonlinear systems, IEEE Trans. Autom. Control, 62 (2016), 2071-2076.

21. P. Tabuada, Event-triggered real-time scheduling of stabilizing control tasks, IEEE Trans. Autom. Control, 52 (2007), 1680-1685.

22. D. V. Dimarogonas, E. Frazzoli, K. H. Johansson, Distributed eventtriggered control for multiagent systems, IEEE Trans. Autom. Control, 57 (2011), 1291-1297.

23. Y. Zhang, J. Sun, H. Liang, et al. Event-triggered adaptive tracking control for multi-agent systems with unknown disturbances, IEEE Trans. Cybern., 2018.

24. Y. Li, G. Yang, S. Tong, Fuzzy adaptive distributed event-triggered consensus control of uncertain nonlinear multiagent systems, IEEE Trans. Syst. Man Cybern. Syst., 2018.

25. C. Nowzari, E. Garcia, J. Cortés, Event-triggered communication and control of networked systems for multi-agent consensus, Automatica, 105 (2019), 1-27.

26. M. Ajina, D. Tabatabai, C. Nowzari, Asynchronous distributed event-triggered coordination for multiagent coverage control, IEEE Trans. Cybern., 2019.

27. S. Zhu, Y. Liu, Y. Lou, et al. Stabilization of logical control networks: An event-triggered control approach, Sci. China Inf. Sci., 63 (2020), 1-11.

28. L. Cao, H. Li, G. Dong, et al. Event-triggered control for multiagent systems with sensor faults and input saturation, IEEE Trans. Syst. Man Cybern. Syst., 2019.

29. L. Zhang, H. Liang, Y. Sun, et al. Adaptive event-triggered fault detection scheme for semimarkovian jump systems with output quantization, IEEE Trans. Syst. Man Cybern. Syst., 2019.

30. Z. Gu, T. Zhang, Z. Huan, et al. A novel event-triggered mechanism for networked cascade control system with stochastic nonlinearities and actuator failures, J. Franklin Inst., 356 (2019), 1955-1974.

31. M. Liu, X. Cao, P. Shi, Fault estimation and tolerant control for fuzzy stochastic systems, IEEE Trans. Fuzzy Syst., 21 (2012), 221-229.

32. X. Liu, D. Zhai, T. Li, et al. Fuzzy-approximation adaptive faulttolerant control for nonlinear pure-feedback systems with unknown control directions and sensor failures, Fuzzy Sets Syst., 356 (2019), 28-43.

33. C. Deng, G. Yang, Cooperative adaptive output feedback control for nonlinear multi-agent systems with actuator failures, Neurocomputing, 199 (2016), 50-57.

34. Q. Fan, G. Yang, Event-based fuzzy adaptive fault-tolerant control for a class of nonlinear systems, IEEE Trans. Fuzzy Syst., 26 (2018), 2686-2698.

35. X. Li, G. Yang, Neural-network-based adaptive decentralized fault-tolerant control for a class of interconnected nonlinear systems, IEEE Trans. Neural Networks Learn. Syst., 29 (2016), 144-155.

36. H. Yang, P. Shi, X. Li, et al. Fault-tolerant control for a class of t-s fuzzy systems via delta operator approach, Signal Proc., 98 (2014), 166-173.

37. P. Du, H. Liang, S. Zhao, et al. Neural-based decentralized adaptive finite-time control for nonlinear large-scale systems with time-varying output constraints, IEEE Trans. Syst. Man Cybern. Syst., 2019.

38. X. Zhang, X. Li, J. Cao, et al. Design of memory controllers for finite-time stabilization of delayed neural networks with uncertainty, J. Franklin Inst., 355 (2018), 5394-5413.

39. X. Li, B. Zhang, P. Li, et al. Finite-horizon H-infinity state estimation for periodic neural networks over fading channels, IEEE Trans. Neural. Networks. Learn. Syst., 2019.

40. W. Sun, S. Su, J. Xia, et al. Adaptive fuzzy control with high-order barrier Lyapunov functions for high-order uncertain nonlinear systems with full-state constraints, IEEE Trans. Cybern., 2018.

41. Y. Sheng, F. Lewis, Z. Zeng, et al. Lagrange stability and finite-time stabilization of fuzzy memristive neural networks with hybrid time-varying delays, IEEE Trans. Cybern., 2019.

42. H. Liang, Z. Zhang, C. K. Ahn, Event-triggered fault detection and isolation of discrete-time systems based on geometric technique, IEEE Trans. Circuits Syst. II, 2019.

43. X. Li, P. Li, Q. Wang, Input/output-to-state stability of impulsive switched systems, Syst. Control Lett., 116 (2018), 1-7.

44. L. Zhang, H. K. Lam, Y. Sun, et al. Fault detection for fuzzy semi-markov jump systems based on interval type-2 fuzzy approach, IEEE Trans. Fuzzy Syst., 2019.

45. Q. Zhou, P. Du, H. Li, et al. Adaptive fixed-time control of error-constrained pure-feedback interconnected nonlinear systems, IEEE Trans. Syst. Man Cybern. Syst., 2019.

46. L. Liu, Y. Liu, S. Tong, Neural networks-based adaptive nite-time faulttolerant control for a class of strict-feedback switched nonlinear systems, IEEE Trans. Cybern., 49 (2018), 2536-2545.

47. A. Sahoo, H. Xu, S. Jagannathan, Neural network-based event-triggered state feedback control of nonlinear continuous-time systems, IEEE Trans. Neural Networks Learn. Syst., 27 (2015), 497-509.

48. X. Li, X. Yang, T. Huang, Persistence of delayed cooperative models: Impulsive control method, J. Franklin Inst., 342 (2019), 130-146.

49. W. Haddad, V. Chellaboina, S. Nersesov, Impulsive and hybrid dynamical systems: Stability, dissipativity and control, Princeton University Press, 2006.

50. H. Khalil, Nonlinear systems, Upper Saddle River, 2002.