Skip to main content

Distributed Average Tracking via Nonsmooth Feedback

  • Chapter
  • First Online:

Abstract

The aim of this chapter is to design a distributed average tracking algorithm via nonsmooth feedback that could work for generic reference signals with bounded derivatives (The results in this chapter are based mainly on [5]. We have rewritten some parts in [5] and added several examples. See full copyright acknowledgement for non-original material at the end of the chapter). The challenge of distributed average tracking algorithm design lies in the presence of multiple reference signals which are time varying and do not converge. For this reason, consensus algorithms cannot be used directly for distributed average tracking, and a redesign is needed to take into account the changing trend of the reference signals. Although the reference signals are time varying, their derivatives are usually bounded. Thus, it is of interest to study the distributed average tracking problem for reference signals with bounded derivatives.

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

Buying options

Chapter
USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   84.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   109.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD   109.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Learn about institutional subscriptions

Notes

  1. 1.

    The results in this chapter are based mainly on [5]. We have rewritten some parts in [5] and added several examples. See full copyright acknowledgement for non-original material at the end of the chapter.

  2. 2.

    A special choice is \(z_i(0)=0\) for all \(i=1,\dots ,n\).

  3. 3.

    Here, we use the term average tracking rather than dynamic consensus to emphasize the tracking nature of the problem.

References

  1. T. Apostol, Mathematical Analysis (Addison-Wesley, 1974)

    Google Scholar 

  2. H. Bai, M. Arcak, J.T. Wen, Adaptive design for reference velocity recovery in motion coordination. Syst. Control Lett. 57(8), 602–610 (2008)

    Article  MathSciNet  Google Scholar 

  3. H. Bai, M. Arcak, J.T. Wen, Adaptive motion coordination: using relative velocity feedback to track a reference velocity. Automatica 45(4), 1020–1025 (2009)

    Article  MathSciNet  Google Scholar 

  4. Y. Cao, W. Ren, Y. Li, Distributed discrete-time coordinated tracking with a time-varying reference state and limited communication. Automatica 45(5), 1299–1305 (2009)

    Article  MathSciNet  Google Scholar 

  5. F. Chen, Y. Cao, W. Ren, Distributed average tracking of multiple time-varying reference signals with bounded derivatives. IEEE Trans. Autom. Control 56(12), 1–6 (2012)

    MathSciNet  MATH  Google Scholar 

  6. R.A. Freeman, P. Yang, K.M. Lynch, Stability and convergence properties of dynamic average consensus estimators, in Proceedings of the IEEE Conference on Decision and Control (2006), pp. 338–343

    Google Scholar 

  7. Y. Hong, G. Chen, L. Bushnell, Distributed observers design for leader-following control of multi-agent networks. Automatica 44(3), 846–850 (2008)

    Article  MathSciNet  Google Scholar 

  8. Y. Hong, J. Hu, L. Gao, Tracking control for multi-agent consensus with an active leader and variable topology. Automatica 42(7), 1177–1182 (2006)

    Article  MathSciNet  Google Scholar 

  9. A. Jadbabaie, J. Lin, A.S. Morse, Coordination of groups of mobile autonomous agents using nearest neighbor rules. IEEE Trans. Autom. Control 48(6), 998–1001 (2003)

    Article  MathSciNet  Google Scholar 

  10. X. Li, X. Wang, G. Chen, Pinning a complex dynamical network to its equilibrium. IEEE Trans. Circ. Syst. I: Fundam. Theory Appl. 51(10), 2074–2087 (2004)

    Article  MathSciNet  Google Scholar 

  11. Z. Li, Z. Duan, G. Chen, L. Huang, Consensus of multiagent systems and synchronization of complex networks: a unified viewpoint. IEEE Trans. Circ. Syst. I: Fundam. Theory Appl. 57(1), 213–224 (2010)

    MathSciNet  Google Scholar 

  12. N.A. Lynch, Distributed Algorithms (Morgan Kaufmann, 1997)

    Google Scholar 

  13. L. Moreau, Stability of multi-agent systems with time-dependent communication links. IEEE Trans. Autom. Control 50(2), 169–182 (2005)

    Article  Google Scholar 

  14. R. Olfati-Saber, R.M. Murray, Consensus problems in networks of agents with switching topology and time-delays. IEEE Trans. Autom. Control 49(9), 1520–1533 (2004)

    Article  MathSciNet  Google Scholar 

  15. W. Ren, R.W. Beard, Consensus seeking in multiagent systems under dynamically changing interaction topologies. IEEE Trans. Autom. Control 50(5), 655–661 (2005)

    Article  MathSciNet  Google Scholar 

  16. D.P. Spanos, R.M. Murray, Distributed sensor fusion using dynamic consensus, in Proceedings of the IFAC World Congress (2005)

    Google Scholar 

  17. D.P. Spanos, R. Olfati-Saber, R.M. Murray, Dynamic consensus on mobile networks, in Proceedings of The IFAC World Congress (2005)

    Google Scholar 

  18. H. Su, X. Wang, Z. Lin, Flocking of multi-agents with a virtual leader. IEEE Trans. Autom. Control 54(2), 293–307 (2009)

    Article  MathSciNet  Google Scholar 

  19. H. Su, X. Wang, Z. Lin, Synchronization of coupled harmonic oscillators in a dynamic proximity network. Automatica 45, 2286–2291 (2009)

    Article  MathSciNet  Google Scholar 

  20. Y. Sun, M.D. Lemmon, Swarming under perfect consensus using integral action, in Proceedings of American Control Conference, pp. 4594–4599 (2007)

    Google Scholar 

  21. P. Yang, R.A. Freeman, K.M. Lynch, Multi-agent coordination by decentralized estimation and control. IEEE Trans. Autom. Control 53(11), 2480–2496 (2008)

    Article  MathSciNet  Google Scholar 

  22. W. Yu, J. Cao, G. Chen, J. Lu, J. Han, W. Wei, Local synchronization of a complex network model. IEEE Trans. Syst. Man, Cybern. Part B: Cybern. 39(1), 230–241 (2009)

    Article  Google Scholar 

  23. W. Yu, G. Chen, J. Lü, On pinning synchronization of complex dynamical networks. Automatica 45(2), 429–435 (2009)

    Article  MathSciNet  Google Scholar 

Download references

Acknowledgements

©2012 IEEE. Reprinted, with permission, from Fei Chen, Yongcan Cao, and Wei Ren. “Distributed average tracking of multiple time-varying reference signals with bounded derivatives.” IEEE Transactions on Automatic Control, vol. 57, no. 12, pp. 3169–3174, 2012.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Fei Chen .

Rights and permissions

Reprints and permissions

Copyright information

© 2020 Springer Nature Switzerland AG

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Chen, F., Ren, W. (2020). Distributed Average Tracking via Nonsmooth Feedback. In: Distributed Average Tracking in Multi-agent Systems. Springer, Cham. https://doi.org/10.1007/978-3-030-39536-0_3

Download citation

Publish with us

Policies and ethics