Skip to main content
SpringerLink
Log in

A Novel Leader-following Consensus of Multi-agent Systems with Smart Leader

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

Abstract

This article studies the leader-following consensus problem for mixed-order multi-agent systems with a leader. Different from the traditional leader which is independent of all the other agents, the leader, called smart leader, can obtain and utilize the feedback information from its neighbors at some disconnected time intervals. A new distributed consensus control protocol based on intermittent control is developed for leader-following consensus with a smart leader. Moreover, the smart leader can adjust the control protocol based on the feedback information from its neighbors. With the aid of Lyapunov function, some sufficient conditions are derived for leader-following consensus of multi-agent systems with mixed-order dynamics under fixed directed topology. In addition, the similar results are obtained under switching directed topology. Finally, simulation results are provided to verify the correctness and effectiveness of 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

Similar content being viewed by others

References

  1. C. M. Topaz and A. L. Bertozzi, “Swarming patterns in a two-dimensional kinematic model for biological groups,” SIAM Journal on Applied Mathematics, vol. 65, no. 1, pp. 152–174, September 2004.

    Article  MathSciNet  MATH  Google Scholar 

  2. I. D. Couzin, J. Krause, N. R. Franks, and S. A. Levin, “Effective leadership and decision-making in animal groups on the move,” Nature, vol. 433, no. 7025, pp. 513–516, February 2005.

    Article  Google Scholar 

  3. M. Nagy, Z. Akos, D. Biro, and T. Vicsek, “Hierarchical group dynamics in pigeon flocks,” Nature, vol. 464, no. 7290, pp. 890–893, April 2010.

    Article  Google Scholar 

  4. F. Giulietti, L. Pollini, and M. Innocenti, “Autonomous formation flight,” IEEE Control Systems, vol. 20, no. 6, pp. 34–44, December 2000.

    Article  Google Scholar 

  5. R. W. Beard, J. Lawton, and F. Y. Hadaegh, “A coordination architecture for spacecraft formation control,” IEEE Transactions on Control Systems Technology, vol. 9, no. 6, pp. 777–790, November 2001.

    Article  Google Scholar 

  6. A. Pant, P. Seiler, and K. Hedrick, “Mesh stability of lookahead interconnected systems,” IEEE Transactions on Automatic Control, vol. 47, no. 2, pp. 403–407, February 2002.

    Article  MathSciNet  MATH  Google Scholar 

  7. R.W. Beard, T.W. McLain, M. A. Goodrich, and E. P. Anderson, “Coordinated target assignment and intercept for unmanned air vehicles,” IEEE Transactions on Robotics and Automation, vol. 18, no. 6, pp. 911–922, December 2002.

    Article  Google Scholar 

  8. J. A. Fax and R. M. Murray, “Information flow and cooperative control of vehicle formations,” IEEE Transactions on Automatic Control, vol. 49, no. 9, pp. 1465–1476, September 2004.

    Article  MathSciNet  MATH  Google Scholar 

  9. J. Cortes and F. Bullo, “Coordination and geometric optimization via distributed dynamical systems,” SIAM Journal on Control and Optimization, vol. 44, no. 5, pp. 1543–1574, November 2005.

    Article  MathSciNet  MATH  Google Scholar 

  10. R. Olfati-Saber, “Flocking for multi-agent dynamic systems: algorithms and theory,” IEEE Transactions on Automatic Control, vol. 51, no. 3, pp. 401–420, March 2006.

    Article  MathSciNet  MATH  Google Scholar 

  11. N. E. Leonard, D. A. Paley, F. Lekien, and R. E. Davis, “Collective motion, sensor networks, and ocean sampling,” Proceedings of the IEEE, vol. 95, no. 1, pp. 48–74, January 2007.

    Article  Google Scholar 

  12. H. T. Zhang, C. Zhai, and Z. Chen, “A general alignment repulsion algorithm for flocking of multi-agent systems,” IEEE Transactions on Automatic Control, vol. 56, no. 2, pp. 430–435, February 2011.

    Article  MathSciNet  MATH  Google Scholar 

  13. T. F. Liu and Z. P. Jiang, “Distributed formation control of nonholonomic mobile robots without global position measurements,” Automatica, vol. 49, no. 2, pp. 592–600, 2013.

    Article  MathSciNet  MATH  Google Scholar 

  14. Y. L. Wei, J. B. Qiu, H. R. Karimi, and M. Wang, “H¥ model reduction for continuous-time Markovian jump systems with incomplete statistics of mode information,” International Journal of Systems Science, vol. 45, no. 7, pp. 1496–1507, September 2014.

    Article  MathSciNet  MATH  Google Scholar 

  15. Y. L. Wei, J. B. Qiu, and H. R. Karimi, “Quantized H¥ filtering for continuous-time Markovian jump systems with deficient mode information,” Asian Journal of Control, vol. 17, no. 5, pp. 1914–1923, 2015.

    Article  MathSciNet  MATH  Google Scholar 

  16. Y. L. Wei, J. B. Qiu, H. K. Lam, and L. G. Wu, “Approaches to T-S fuzzy-affine-model-based reliable output feedback control for nonlinear It o stochastic systems,” IEEE Transactions on Circuits and Systems, vol. 25, no. 3, pp. 569–583, June 2017.

    Google Scholar 

  17. Y. L. Wei, J. B. Qiu, and H. R. Karimi, “Reliable output feedback control of discrete-time fuzzy affine systems with actuator faults,” IEEE Transactions on Fuzzy Systems, vol. 64, no. 1, pp. 170–181, January 2017.

    Google Scholar 

  18. X. Wang and G. H. Yang, “Adaptive reliable coordination control for linear agent networks with intermittent communication constraints,” IEEE Transactions on Control of Network Systems, DOI:10.1109/TCNS.2017.2687818.

    Google Scholar 

  19. W. Ren and R.W. Beard, “Consensus seeking in multiagent systems under dynamically changing interaction topologies,” IEEE Transactions on Automatic Control, vol. 50, no. 5, pp. 655–661, May 2005.

    Article  MathSciNet  MATH  Google Scholar 

  20. P. Lin and Y. M. Jia, “Consensus of a class of second-order multi-agent systems with time-delay and jointly-connected topologies,” IEEE Transactions on Automatic Control, vol. 55, no. 3, pp. 778–784, March 2010.

    Article  MathSciNet  MATH  Google Scholar 

  21. Y. Z. Song, “Consensus of agents with mixed linear discrete dynamics,” International Journal of Control, Automation and Systems, vol. 14, no. 4, pp. 1139–1143, August 2016.

    Article  Google Scholar 

  22. J. Liu, P. Ming, and S. Li, “Consensus gain conditions of stochastic multi-agent system with communication noise,” International Journal of Control, Automation and Systems, vol. 14, no. 5, pp. 1223–1230, October 2016.

    Article  Google Scholar 

  23. J. H. Qin, H. J. Gao, T. Hayat, and F. E. Alsaadi, “Synchronising second-order multi-agent systems under dynamic topology via reference model-based algorithm,” Journal of Control and Decision, vol. 1, no. 3, pp. 214–225, 2014.

    Article  Google Scholar 

  24. X. Wang and G. H. Yang, “Distributed reliable consensus control for a class of multi-agent systems under switching networks: A topology-based average dwell time approach,” International Journal of Robust and Nonlinear Control, vol. 26, no. 13, pp. 2767–2787, 2016.

    Article  MathSciNet  MATH  Google Scholar 

  25. Y. G. Hong, J. P. Hu, and L. X. Gao, “Tracking control for multi-agent consensus with an active leader and variable topology,” Automatica, vol. 42, no. 7, pp. 1177–1182, 2006.

    Article  MathSciNet  MATH  Google Scholar 

  26. W. Zhu and D. Cheng, “Leader-following consensus of second-order agents with multiple time-varying delays,” Automatica, vol. 46, no. 12, pp. 1994–1999, 2010.

    Article  MathSciNet  MATH  Google Scholar 

  27. S. Djaidja and Q. H. Wu, “Leader-following consensus of single-integrator multi-agent systems under noisy and delayed communication,” International Journal of Control, Automation, and Systems, vol. 14, no. 2, pp. 357–366, April 2016.

    Article  Google Scholar 

  28. N. Huang, Z. S. Duan, and Y. Zhao, “Leader-following consensus of second-order non-linear multi-agent systems with directed intermittent communication,” IET Control Theory & Applications, vol. 8, no. 10, pp. 782–795, July 2014.

    Article  MathSciNet  Google Scholar 

  29. A. H. Hu, J. D. Cao, and M. Hu, “Consensus of leaderfollowing multi-agent systems in time-varying networks via intermittent control,” International Journal of Control, Automation and Systems, vol. 12, no. 5, pp. 969–976, October 2014.

    Article  Google Scholar 

  30. E. Semsar-Kazerooni and K. Khorasani, “Optimal consensus algorithms for cooperative team of agents subject to partial information,” Automatica, vol. 44, no. 11, pp. 2766–2777, 2008.

    Article  MathSciNet  MATH  Google Scholar 

  31. M.Wu, Y. He, and J. H. She, Stability Analysis and Robust Control of Time-delay Systems, Springer, Berlin, 2010.

    MATH  Google Scholar 

  32. B. Boyd, L. Ghaoui, E. Feron, and V. Balakrishnan, Linear Matrix Inequalities in System and Control Theory, SIAM, Philadelphia, 1994.

    Book  MATH  Google Scholar 

  33. A. Olshevsky and J. N. Tsitsiklis, “On the nonexistence of quadratic Lyapunov functions for consensus algorithms,” IEEE Transactions on Automatic Control, vol. 53, no. 11, pp. 2642–2645, December 2008.

    Article  MathSciNet  MATH  Google Scholar 

  34. G. H. Wen, Z. S. Duan, W. W Yu, and G. R. Chen, “Consensus in multi-agent systems with communication constraints,” International Journal of Robust and Nonlinear Control, vol. 22, no. 2, pp. 170–182, 2012.

    Article  MathSciNet  MATH  Google Scholar 

  35. Z. H. Qu, Cooperative Control of Dynamical Systems: Applications to Autonomous Vehicles, Springer Science & Business Media, 2009.

    MATH  Google Scholar 

  36. H. W. Zhang, F. L. Lewis, and Z. H. Qu, “Lyapunov, adaptive, and optimal design techniques for cooperative systems on directed communication graphs,” IEEE Transactions on Industrial Electronics, vol. 59, no. 7, pp. 3026–3041, July 2012.

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. College of Computer and Control Engineering, Nankai University, Tianjin, 300071, China

    Fu-Yong Wang, Zhong-Xin Liu & Zeng-Qiang Chen

Authors
  1. Fu-Yong Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Zhong-Xin Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Zeng-Qiang Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Zhong-Xin Liu.

Additional information

Recommended by Associate Editor Hyo-Sung Ahn under the direction of Editor Yoshto Ohta. This work is supported by the National Natural Science Foundation of China (Nos. 61573200, 61573199).

Fu-Yong Wang received his B.S. degree in electrical engineering and automation and his M.S. degree in computer application technology from the Ludong University, Yantai, China, in 2013 and 2016, respectively. He is now pursuing the Ph.D. degree in the College of Computer and Control Engineering, Nankai University, Tianjin, China. His research interest covers coordination of multi-agent systems.

Zhong-Xin Liu received his B.S. degree in automation and his Ph.D. degree in control theory and control engineering from the Nankai University, Tianjin, China, in 1997 and 2002, respectively. He has been at Nankai University, where he is currently a Professor in the Department of Automation. His main areas of research are in predictive control, complex networks and multi-agents system.

Zeng-Qiang Chen received his B.S. degree in mathematics, his M.S. and Ph.D. degrees in control theory and control engineering from the Nankai University, Tianjin, China, in 1987, 1990 and 1997, respectively. He has been at Nankai University, where he is currently a Professor in the Department of Automation. His main areas of research are in neural network control, complex networks and multi-agents system.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Wang, FY., Liu, ZX. & Chen, ZQ. A Novel Leader-following Consensus of Multi-agent Systems with Smart Leader. Int. J. Control Autom. Syst. 16, 1483–1492 (2018). https://doi.org/10.1007/s12555-017-0266-0

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12555-017-0266-0

Keywords

Search

Navigation