Skip to main content
Log in

Containment control of continuous-time multi-agent systems with general linear dynamics under time-varying communication topologies

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

Abstract

This paper devotes itself to the containment tracking problem of general linear high-order multiagent systems (MASs) under time-varying communications. For general linear MASs with time-varying weightunbalanced digraph, the containment control problem is difficult and challenging, because the Lyapunov method is not an effective approach in this case. Under the assumption that the graph topology uniformly and jointly has a directed spanning forest, we show that when the exponentially unstable mode associated with each agent’s self-dynamics is weak enough, the followers can asymptotically tend to the dynamical convex hull spanned by the leaders, i.e. the containment can be achieved. Moreover, the least convergence rate is explicitly specified. Simulations are also provided to demonstrate the effectiveness of our result.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Similar content being viewed by others

References

  1. M. Cao, A. S. Morse, and B. D. Anderson, “Reaching a consensus in a dynamically changing environment: Convergence rates, measurement delays, and asynchronous events,” SIAM Journal on Control and Optimization, vol. 47, no. 2, pp. 601–623, 2008.

    Article  MathSciNet  MATH  Google Scholar 

  2. W. Ni and D. Z. Cheng, “Leader-following consensus of multi-agent systems under fixed and switching topologies,” Systems & Control Letters, vol. 59, no. 3, pp. 209–217, 2010.

    Article  MathSciNet  MATH  Google Scholar 

  3. 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, 2005.

    Article  MathSciNet  Google Scholar 

  4. D. V. Dimarogonas, M. Egerstedt, and K. J. Kyriakopoulos, “A leader-based containment control strategy for multiple unicycles,” Proc. of 45th IEEE Conference on Decision and Control, pp. 5968–5973, 2006.

    Chapter  Google Scholar 

  5. H. Y. Liu, G. M. Xie, and L. Wang, “Necessary and sufficient conditions for containment control of networked multi-agent systems,” Automatica, vol. 48, no. 7, pp. 1415–1422, 2012.

    Article  MathSciNet  MATH  Google Scholar 

  6. G. Notarstefano, M. Egerstedt, and M. Haque, “Containment in leader-follower networks with switching communication topologies,” Automatica, vol. 47, no. 5, pp. 1035–1040, 2011.

    Article  MathSciNet  MATH  Google Scholar 

  7. Y. C. Cao, W. Ren, and M. Egerstedt, “Distributed containment control with multiple stationary or dynamic leaders in fixed and switching directed networks,” Automatica, vol. 48, no. 8, pp. 1586–1597, 2012.

    Article  MathSciNet  MATH  Google Scholar 

  8. Y. C. Lou and Y. G. Hong, “Target containment control of multi-agent systems with random switching interconnection topologies,” Automatica, vol. 48, no. 5, pp. 879–885, 2012.

    Article  MathSciNet  MATH  Google Scholar 

  9. X. W. Mu, B. J. Zheng, and K. Liu, “L 2-Linfinity containment control of multi-agent systems with markovian switching topologies and non-uniform time-varying delays,” IET Control Theory & Applications, vol. 8, no. 10, pp. 863–72, 2014.

    Article  MathSciNet  Google Scholar 

  10. X. W. Mu, B. J. Zheng, and K. Liu, “Containment control of second-order discrete-time multi-agent systems with markovian missing data,” IET Control Theory & Applications, vol. 9, no. 8, pp. 1229–1237, 2015.

    Article  MathSciNet  Google Scholar 

  11. H. Y. Liu, G. M. Xie, and L. Wang, “Containment of linear multi-agent systems under general interaction topologies,” Systems & Control Letters, vol. 61, no. 4, pp. 528–534, 2012.

    Article  MathSciNet  MATH  Google Scholar 

  12. Z. K. Li, W. Ren, X. D. Liu, and M. Y. Fu, “Distributed containment control of multi-agent systems with general linear dynamics in the presence of multiple leaders,” International Journal of Robust and Nonlinear Control, vol. 23, no. 5, pp. 534–547, 2013.

    Article  MathSciNet  MATH  Google Scholar 

  13. Q. Ma and G. Y. Miao, “Distributed containment control of linear multi-agent systems,” Neurocomputing, vol. 133, no. 2014, pp. 399–403, 2014.

    Article  Google Scholar 

  14. Z. K. Li, Z. S. Duan, W. Ren, and G. Feng, “Containment control of linear multi-agent systems with multiple leaders of bounded inputs using distributed continuous controllers,” International Journal of Robust and Nonlinear Control, vol. 25, no. 13, pp. 2101–2121, 2015.

    MathSciNet  MATH  Google Scholar 

  15. H. Haghshenas, M. A. Badamchizadeh, and M. Baradarannia, “Containment control of heterogeneous linear multiagent systems,” Automatica, vol. 54, no. 2015, pp. 210–216, 2015.

    Article  MathSciNet  MATH  Google Scholar 

  16. X. W. Mu, Z. Yang, K. Liu and J. R. Mu, “Containment control of general multi-agent systems with directed random switching topology,” Journal of the Franklin Institute, vol. 352, no. 10, pp. 4067–4080, 2015.

    Article  MathSciNet  Google Scholar 

  17. J. H. Qin and C. B. Yu, “Exponential consensus of general linear multi-agent systems under directed dynamic topology,” Automatica, vol. 50, no. 9, pp. 2327–2333, 2014.

    Article  MathSciNet  MATH  Google Scholar 

  18. S. Yin, S. X. Ding, X. C. Xie, and H. Luo, “A review on basic data-driven approaches for industrial process monitoring,” IEEE Transactions on Industrial Electronics, vol. 61, no. 11, pp. 6418–6428, 2014.

    Article  Google Scholar 

  19. S. Yin, X. W. Li, H. J. Gao, and O. Kaynak, “Data-based techniques focused on modern industry: An overview,” IEEE Transactions on Industrial Electronics, vol. 62, no. 1, pp. 657–667, 2015.

    Article  Google Scholar 

  20. S. Yin and Z. H. Huang, “Performance monitoring for vehicle suspension system via fuzzy positivistic cmeans clustering based on accelerometer measurements,” IEEE/ASME Transactions on Mechatronics, vol. 20, no. 5, pp. 2613–2620, 2014.

    Article  Google Scholar 

  21. S. Yin, X. P. Zhu, and O. Kaynak, “Improved pls focused on key-performance-indicator-related fault diagnosis,” IEEE Transactions on Industrial Electronics, vol. 62, no. 3, pp. 1651–1658, 2015.

    Article  Google Scholar 

  22. S. Yin, G. Wang, and X. Yang, “Robust pls approach for kpi-related prediction and diagnosis against outliers and missing data,” International Journal of Systems Science, vol. 45, no. 7, pp. 1375–1382, 2014.

    Article  MATH  Google Scholar 

  23. H. Y. Li, Y. B. Gao, L. G. Wu, and H. K. Lam, “Fault detection for t-s fuzzy time-delay systems: Delta operator and input-output methods,” IEEE Transactions on Cybernetics, vol. 45, no. 2, pp. 229–241, 2015.

    Article  Google Scholar 

  24. H. Y. Li, H. J. Gao, P. Shi, and X. D. Zhao, “Fault-tolerant control of markovian jump stochastic systems via the augmented sliding mode observer approach,” Automatica, vol. 50, no. 7, pp. 1825–1834, 2014.

    Article  MathSciNet  MATH  Google Scholar 

  25. T. Wang, H. J. Gao, and J. B. Qiu, “A combined adaptive neural network and nonlinear model predictive control for multirate networked industrial process control,” IEEE Transactions on Neural Networks and Learning Systems, vol. 27, no. 2, pp. 416–425, 2016.

    Article  MathSciNet  Google Scholar 

  26. Y. C. Cao, D. Stuart, W. Ren, and Z. Y. Meng, “Distributed containment control for multiple autonomous vehicles with double-integrator dynamics: Algorithms and experiments,” IEEE Transactions on Control Systems Technology, vol. 19, no. 4, pp. 929–938, 2011.

    Article  Google Scholar 

  27. Z. Kan, J. R. Klotz, E. L. Pasiliao, and W. E. Dixon, “Containment control for a social network with state-dependent connectivity,” Automatica, vol. 56, no. 2015, pp. 86–92, 2015.

    Article  MathSciNet  MATH  Google Scholar 

  28. H. J. Gao, T. W. Chen, and J. Lam, “A new delay system approach to network-based control,” Automatica, vol. 44, no. 1, pp. 39–52, 2008.

    Article  MathSciNet  MATH  Google Scholar 

  29. T. Wang, Y. F. Zhang, J. B. Qiu, and H. J. Gao, “Adaptive fuzzy backstepping control for a class of nonlinear systems with sampled and delayed measurements,” IEEE Transactions on Fuzzy Systems, vol. 23, no. 2, pp. 302–312, 2015.

    Article  Google Scholar 

  30. T. Y. Chai, L. Zhao, J. B. Qiu, F. Z. Liu, and J. L. Fan, “Integrated network-based model predictive control for setpoints compensation in industrial processes,” IEEE Transactions on Industrial Informatics, vol. 9, no. 1, pp. 417–426, 2013.

    Article  Google Scholar 

  31. H. Y. Li, Y. B. Gao, P. Shi and H. K. Lam, “Observer-based fault detection for nonlinear systems with sensor fault and limited communication capacity,” IEEE Transactions on Automatic Control, vol. 61, no. 9, pp. 2745–2751, 2015.

    Article  MathSciNet  Google Scholar 

  32. H. Y. Li, C. W. Wu, S. Yin and H. K. Lam, “Observerbased fuzzy control for nonlinear networked systems under unmeasurable premise variables,” IEEE Transactions on Fuzzy Systems, vol. 24, no. 5, pp. 1233–1245, 2015.

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Xiao-wu Mu.

Additional information

Recommended by Associate Editor Choon Ki Ahn under the direction of Editor PooGyeon Park. This work is supported by The National Natural Science Foundation of China under grants 11571322.

Zhe Yang received her B.S. and M.S. degrees from the Department of Mathematics of Zhengzhou University in 2010 and 2012, respectively. Now she is a PhD candidate of School of Mathematics and Statistics of Zhengzhou University. Her research interests include nonlinear control theory, switched systems and network control systems.

Xiao-wu Mu received his B.S. and Ph.D. degrees from the Department of Mathematics of Peking University, in 1983 and 1991, respectively. Now he is a Professor of Zhengzhou University. His research interests include stochastic systems, hybrid systems, nonlinear control, network control systems.

Kai Liu was born in Henan Province, China, in 1984. He received the B.S. degree in computing science from School of Mathematics, Shandong University, Jinan, China, in 2005, and his M.E. and Ph.D. degrees in theory of control from Zhengzhou University, Zhengzhou, China, in 2008 and 2015, respectively. Since 2008, he has been with College of Science, Henan Institution of Engineering, Zhengzhou, China, where he is currently a lecturer. His current research interests include quantized control, limited data rate, networked control, and multi-agent system.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Yang, Z., Mu, Xw. & Liu, K. Containment control of continuous-time multi-agent systems with general linear dynamics under time-varying communication topologies. Int. J. Control Autom. Syst. 15, 442–449 (2017). https://doi.org/10.1007/s12555-015-0205-x

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12555-015-0205-x

Keywords

Navigation