Skip to main content
SpringerLink
Log in

Adaptive neural network consensus tracking control for uncertain multi-agent systems with predefined accuracy

  • Original paper
  • Published:
Nonlinear Dynamics Aims and scope Submit manuscript

Abstract

This paper proposes the consensus tracking control problem for a class of uncertain nonlinear multi-agent systems. By using a group of nonnegative functions, an adaptive neural network controller is addressed based on the technique of backstepping. Compared with existing results about uncertain nonlinear multi-agent systems, the advantage of the proposed scheme is that it can ensure the consensus of multi-agent systems within a given accuracy by using two nth-order continuous differentiable functions. Finally, simulation results confirm the correctness of the proposed scheme.

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. Uny Cao, Y., Fukunaga, A.S., Kahng, A.: Cooperative mobile robotics: antecedents and directions. Auton. Rob. 4(1), 7–27 (1997)

    Google Scholar 

  2. Popa, D.O., Sanderson, A.C., Komerska, R.J., Mupparapu, S.S., Richard Blidberg, D., Chappel, S.G.: Adaptive sampling algorithms for multiple autonomous underwater vehicles. In: 2004 IEEE/OES Autonomous Underwater Vehicles (IEEE Cat. No. 04CH37578), pp. 108–118. IEEE (2004)

  3. Ge, X., Han, Q.-L.: Consensus of multiagent systems subject to partially accessible and overlapping markovian network topologies. IEEE Trans. Cybern. 47(8), 1807–1819 (2016)

    Google Scholar 

  4. Ma, L., Wang, Z., Han, Q.-L., Liu, Y.: Consensus control of stochastic multi-agent systems: a survey. Sci. China Inf. Sci. 60(12), 120201 (2017)

    MathSciNet  Google Scholar 

  5. Ning, B., Han, Q.-L.: Prescribed finite-time consensus tracking for multiagent systems with nonholonomic chained-form dynamics. IEEE Trans. Autom. Control 64(4), 1686–1693 (2018)

    MathSciNet  MATH  Google Scholar 

  6. Ning, B., Han, Q.-L., Zuo, Z., Jin, J., Zheng, J.: Collective behaviors of mobile robots beyond the nearest neighbor rules with switching topology. IEEE Trans. Cybern. 48(5), 1577–1590 (2017)

    Google Scholar 

  7. Hua, C.-C., You, X., Guan, X.-P.: Leader-following consensus for a class of high-order nonlinear multi-agent systems. Automatica 73, 138–144 (2016)

    MathSciNet  MATH  Google Scholar 

  8. You, X., Hua, C., Guan, X.: Event-triggered leader-following consensus for nonlinear multiagent systems subject to actuator saturation using dynamic output feedback method. IEEE Trans. Autom. Control 63(12), 4391–4396 (2018)

    MathSciNet  MATH  Google Scholar 

  9. Tian, B., Hanchen, L., Zuo, Z., Yang, W.: Fixed-time leader-follower output feedback consensus for second-order multiagent systems. IEEE Trans. Cybern. 49(4), 1545–1550 (2018)

    Google Scholar 

  10. Zhang, Y., Sun, J., Liang, H., Li, H.: Event-triggered adaptive tracking control for multiagent systems with unknown disturbances. IEEE Trans. Cybern. 50(3), 890–901 (2020)

    Google Scholar 

  11. Lin, P., Jia, Y.: Consensus of a class of second-order multi-agent systems with time-delay and jointly-connected topologies. IEEE Trans. Autom. Control 55(3), 778–784 (2010)

    MathSciNet  MATH  Google Scholar 

  12. Guoqiang, H.: Robust consensus tracking of a class of second-order multi-agent dynamic systems. Syst. Control Lett. 61(1), 134–142 (2012)

    MathSciNet  MATH  Google Scholar 

  13. Huang, N., Duan, Z., Chen, G.R.: Some necessary and sufficient conditions for consensus of second-order multi-agent systems with sampled position data. Automatica 63, 148–155 (2016)

    MathSciNet  MATH  Google Scholar 

  14. Shen, H., Wang, Y., Xia, J., Park, J.H., Wang, Z.: Fault-tolerant leader-following consensus for multi-agent systemssubject to semi-markov switching topologies: an event-triggeredcontrol scheme. Nonlinear Anal Hybrid Syst. 34, 92–107 (2019)

    MathSciNet  MATH  Google Scholar 

  15. Li, G., Ren, C.-E., Chen, C.L.P., Shi, Z.: Adaptive iterative learning consensus control for second-order multi-agent systems with unknown control gains. Neurocomputing (2020)

  16. Wang, L., Xiao, F.: Finite-time consensus problems for networks of dynamic agents. IEEE Trans. Autom. Control 55(4), 950–955 (2010)

    MathSciNet  MATH  Google Scholar 

  17. Xiaoqing, L., Renquan, L., Chen, S., Jinhu, L.: Finite-time distributed tracking control for multi-agent systems with a virtual leader. IEEE Trans. Circuits Syst. I Regul. Pap. 60(2), 352–362 (2012)

  18. Liu, X., Lam, J., Wenwu, Y., Chen, G.: Finite-time consensus of multiagent systems with a switching protocol. IEEE Trans. Neural Netw. Learn. Syst. 27(4), 853–862 (2015)

    MathSciNet  Google Scholar 

  19. Zou, W., Shi, P., Xiang, Z., Shi, Y.: Finite-time consensus of second-order switched nonlinear multi-agent systems. IEEE Trans. Neural Netw. Learn. Syst. 31(5), 1757–1762 (2019)

    MathSciNet  Google Scholar 

  20. Zhang, B., Jia, Y.: Fixed-time consensus protocols for multi-agent systems with linear and nonlinear state measurements. Nonlinear Dyn. 82(4), 1683–1690 (2015)

    MathSciNet  MATH  Google Scholar 

  21. Hong, H., Wenwu, Y., Wen, G., Xinghuo, Y.: Distributed robust fixed-time consensus for nonlinear and disturbed multiagent systems. IEEE Trans. Syst. Man Cybern. Syst. 47(7), 1464–1473 (2016)

    Google Scholar 

  22. Zuo, Z., Tian, B., Defoort, M., Ding, Z.: Fixed-time consensus tracking for multiagent systems with high-order integrator dynamics. IEEE Trans. Autom. Control 63(2), 563–570 (2017)

    MathSciNet  MATH  Google Scholar 

  23. Ding, D., Wang, Z., Shen, B., Wei, G.: Event-triggered consensus control for discrete-time stochastic multi-agent systems: the input-to-state stability in probability. Automatica 62, 284–291 (2015)

    MathSciNet  MATH  Google Scholar 

  24. Yi, X., Wenlian, L., Chen, T.: Pull-based distributed event-triggered consensus for multiagent systems with directed topologies. IEEE Trans. Neural Netw. Learn. Syst. 28(1), 71–79 (2015)

    MathSciNet  Google Scholar 

  25. Yang, D., Ren, W., Liu, X., Chen, W.: Decentralized event-triggered consensus for linear multi-agent systems under general directed graphs. Automatica 69, 242–249 (2016)

    MathSciNet  MATH  Google Scholar 

  26. Li, X.-M., Zhou, Q., Li, P., Li, H., Lu, R.: Event-triggered consensus control for multi-agent systems against false data-injection attacks. IEEE Trans. Cybern. 50(5), 1856–1866 (2019)

    Google Scholar 

  27. Chen, M., Shao, S.-Y., Jiang, B.: Adaptive neural control of uncertain nonlinear systems using disturbance observer. IEEE Trans. Cybern. 47(10), 3110–3123 (2017)

    Google Scholar 

  28. Li, Y., Tong, S.: Adaptive neural networks prescribed performance control design for switched interconnected uncertain nonlinear systems. IEEE Trans. Neural Netw. Learn. Syst. 29(7), 3059–3068 (2017)

    MathSciNet  Google Scholar 

  29. 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. Syst. 49(9), 1777–1786 (2018)

    Google Scholar 

  30. Yoo, S.J.: Distributed adaptive containment control of uncertain nonlinear multi-agent systems in strict-feedback form. Automatica 49(7), 2145–2153 (2013)

    MathSciNet  MATH  Google Scholar 

  31. Jin, X., Wang, S., Qin, J., Zheng, W.X., Kang, Y.: Adaptive fault-tolerant consensus for a class of uncertain nonlinear second-order multi-agent systems with circuit implementation. IEEE Trans. Circuits Syst. I Regul. Pap. 65(7), 2243–2255 (2017)

  32. Li, R., Yang, G.-H.: Consensus control of a class of uncertain nonlinear multiagent systems via gradient-based algorithms. IEEE Trans. Cybern. 49(6), 2085–2094 (2018)

    Google Scholar 

  33. Liu, W., Huang, J.: Event-triggered cooperative global robust practical output regulation for second-order uncertain nonlinear multiagent systems. IEEE Trans. Neural Netw. Learn. Syst. 29(11), 5486–5498 (2018)

    MathSciNet  Google Scholar 

  34. Chen, W., Shuzhi Sam, G., Jian, W., Maoguo, G.: Globally stable adaptive backstepping neural network control for uncertain strict-feedback systems with tracking accuracy knowna priori. IEEE Trans. Neural Netw. Learn. Syst. 26(9), 1842–1854 (2014)

    Google Scholar 

  35. Jian, W., Chen, W., Li, J.: Fuzzy-approximation-based global adaptive control for uncertain strict-feedback systems with a priori known tracking accuracy. Fuzzy Sets Syst. 273, 1–25 (2015)

    MathSciNet  MATH  Google Scholar 

  36. Jian, W., Chen, W., Yang, F., Li, J., Zhu, Q.: Global adaptive neural control for strict-feedback time-delay systems with predefined output accuracy. Inf. Sci. 301, 27–43 (2015)

    MathSciNet  MATH  Google Scholar 

  37. Jian, W., Li, J.: Adaptive fuzzy control for perturbed strict-feedback nonlinear systems with predefined tracking accuracy. Nonlinear Dyn. 83(3), 1185–1197 (2016)

    MathSciNet  MATH  Google Scholar 

  38. Zou, W., Shi, P., Xiang, Z., Shi, Y.: Consensus tracking control of switched stochastic nonlinear multiagent systems via event-triggered strategy. IEEE Trans. Neural Netw. Learn. Syst. 31(3), 1036–1045 (2019)

    MathSciNet  Google Scholar 

  39. Ren, W., Cao, Y.: Distributed Coordination of Multi-agent Networks: Emergent Problems, Models, and Issues. Springer, Berlin (2010)

    MATH  Google Scholar 

  40. Wang, H., Liu, X., Liu, K.: Robust adaptive neural tracking control for a class of stochastic nonlinear interconnected systems. IEEE Trans. Neural Netw. Learn. Syst. 27(3), 510–523 (2015)

    MathSciNet  Google Scholar 

  41. Yoo, S.J., Park, J.B., Choi, Y.H.: Adaptive neural control for a class of strict-feedback nonlinear systems with state time delays. IEEE Trans. Neural Netw. 20(7), 1209–1215 (2009)

    Google Scholar 

  42. Niculescu, S.-I.: Delay Effects on Stability: A Robust Control Approach, vol. 269. Springer, Berlin (2001)

    MATH  Google Scholar 

  43. Lewis, F.L., Yesildirek, A., Liu, K.: Multilayer neural-net robot controller with guaranteed tracking performance. IEEE Trans. Neural Netw. 7(2), 388–399 (1996)

    Google Scholar 

  44. Zhang, H., Lewis, F.L., Zhihua, Q.: Lyapunov, adaptive, and optimal design techniques for cooperative systems on directed communication graphs. IEEE Trans. Ind. Electr. 59(7), 3026–3041 (2012)

    Google Scholar 

  45. Corless, M., Leitmann, G.: Continuous state feedback guaranteeing uniform ultimate boundedness for uncertain dynamic systems. IEEE Trans. Autom. Control 26(5), 1139–1144 (1981)

    MathSciNet  MATH  Google Scholar 

  46. Shen, H., Huo, S., Yan, H., Park, J., Sreeram, V.: Distributed dissipative state estimation for markov jump genetic regulatory networks subject to round-robin scheduling. IEEE Trans. Neural Netw. Learn. Syst. 31(3), 762–771 (2019)

    MathSciNet  Google Scholar 

  47. Shen, H., Huang, Z., Cao, J., Park, J.H.: Exponential h\(\infty \) filtering for continuous-time switched neural networks under persistent dwell-time switching regularity. IEEE Trans. Cybern. 50(6), 2440–2449 (2019)

    Google Scholar 

  48. Zhang, X.-M., Han, Q.-L., Ge, X., Ding, D., Ding, L., Yue, D., Peng, C.: Networked control systems: a survey of trends and techniques. IEEE/CAA J. Autom. Sin. 7(1), 1–17 (2019)

    MathSciNet  Google Scholar 

  49. Xie, X., Yue, D., Park, J.H.: Observer-based state estimation of discrete-time fuzzy systems based on a joint switching mechanism for adjacent instants. IEEE Trans, Cybern (2019)

    Google Scholar 

Download references

Acknowledgements

This work was supported by the National Natural Science Foundation of China under Grant (Nos. 61833005, 61933008).

Author information

Authors and Affiliations

  1. College of Automation and College of Artificial Intelligence, Nanjing University of Posts and Telecommunications, Nanjing, 210023, China

    Dajie Yao, Dong Yue, Nan Zhao & Tingjun Zhang

  2. School of Mechanical and Electrical Engineering, Chizhou University, Chizhou, 247000, China

    Dajie Yao

  3. Institute of Advanced Technology, Nanjing University of Posts and Telecommunications, Nanjing, 210023, China

    Chunxia Dou, Dong Yue, Nan Zhao & Tingjun Zhang

Authors
  1. Dajie Yao
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Chunxia Dou
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Dong Yue
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Nan Zhao
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Tingjun Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Dajie Yao.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest.

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

Yao, D., Dou, C., Yue, D. et al. Adaptive neural network consensus tracking control for uncertain multi-agent systems with predefined accuracy. Nonlinear Dyn 101, 2249–2262 (2020). https://doi.org/10.1007/s11071-020-05885-z

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11071-020-05885-z

Keywords

Search

Navigation