http://iet.metastore.ingenta.com
1887

Consensus tracking control via iterative learning for singular multi-agent systems

Consensus tracking control via iterative learning for singular multi-agent systems

For access to this article, please select a purchase option:

Buy article PDF
$19.95
(plus tax if applicable)
Buy Knowledge Pack
10 articles for $120.00
(plus taxes if applicable)

IET members benefit from discounts to all IET publications and free access to E&T Magazine. If you are an IET member, log in to your account and the discounts will automatically be applied.

Learn more about IET membership 

Recommend Title Publication to library

You must fill out fields marked with: *

Librarian details
Name:*
Email:*
Your details
Name:*
Email:*
Department:*
Why are you recommending this title?
Select reason:
 
 
 
 
 
IET Control Theory & Applications — Recommend this title to your library

Thank you

Your recommendation has been sent to your librarian.

This study considers the consensus tracking problem of singular multi-agent systems by using an iterative learning control approach. Here, the communication among the followers is described by a directed graph, and only a portion of the followers can receive the leader's information. For such singular multi-agent systems, a unified iterative learning algorithm is proposed in both continuous-time domain and discrete-time domain. Furthermore, the convergence condition of the algorithm is presented and analysed. In this study, the main contribution is to extend the iterative learning control theory from multi-agent systems to singular multi-agent systems. It is shown that the algorithm can guarantee the outputs of the followers converge to the leader's trajectory on a finite time interval along the iteration axis. Finally, the provided examples illustrate the effectiveness of the theoretical results.

References

    1. 1)
      • 1. Jadbabaie, A., Lin, J., Morse, A.S.: ‘Coordination of groups of mobile autonomous agents using nearest neighbor rules’, IEEE Trans. Autom. Control, 2003, 48, (6), pp. 9881001.
    2. 2)
      • 2. Fax, J.A., Murray, R.M.: ‘Information flow and cooperative control of vehicle formations’, IEEE Trans. Autom. Control, 2004, 49, (9), pp. 14651476.
    3. 3)
      • 3. Ren, W., Beard, R.W.: ‘Distributed consensus in multi-vehicle cooperative control’ (Springer-Verlag, London, 2008).
    4. 4)
      • 4. Schlanbusch, R., Kristiansen, R., Nicklasson, P.J.: ‘Spacecraft formation reconfiguration with collision avoidance’, Automatica, 2011, 47, (7), pp. 14431449.
    5. 5)
      • 5. Ren, W., Beard, R.W.: ‘Consensus seeking in multiagent systems under dynamically changing interaction topologies’, IEEE Trans. Autom. Control, 2005, 50, (5), pp. 655661.
    6. 6)
      • 6. Hu, J., Lin, Y.: ‘Consensus control for multi-agent systems with double-integrator dynamics and time-delays’, IET Control Theory Applic., 2010, 4, (1), pp. 109118.
    7. 7)
      • 7. He, W., Cao, J.: ‘Consensus control for high-order multi-agent systems’, IET Control Theory Applic., 2011, 5, (1), pp. 231238.
    8. 8)
      • 8. Wen, G., Duan, Z., Yu, W., et al: ‘Consensus in multi-agent systems with communication constraints’, Int. J. Robust Nonlinear Control, 2012, 22, (2), pp. 170182.
    9. 9)
      • 9. Ding, L., Yu, P., Liu, Z., et al: ‘Consensus of second-order multi-agent systems via impulsive control using sampled hetero-information’, Automatica, 2013, 49, (9), pp. 28812886.
    10. 10)
      • 10. Wang, L., Xiao, F.: ‘Finite-time consensus problems for networks of dynamic agents’, IEEE Trans. Autom. Control, 2010, 55, (4), pp. 950955.
    11. 11)
      • 11. Li, S., Du, H., Lin, X.: ‘Finite-time consensus algorithm for multi-agent systems with double-integrator dynamics’, Automatica, 2011, 47, (8), pp. 17061712.
    12. 12)
      • 12. Li, X, Luo, X., Wang, J., et al: ‘Finite-time consensus of nonlinear multi-agent system with prescribed performance’, Nonlinear Dyn., 2018, 91, (4), pp. 23972409.
    13. 13)
      • 13. Chen, X., Jia, Y.: ‘Stereo vision-based formation control of mobile robots using iterative learning’. Proc. Int. Conf. on Humanized Systems, Kyoto, Japan, September 2010, pp. 6267.
    14. 14)
      • 14. Ahn, H.S., Moore, K.L., Chen, Y.: ‘Trajectory-keeping in satellite formation flying via robust periodic learning control’, Int. J. Robust Nonlinear Control, 2010, 20, (14), pp. 16551666.
    15. 15)
      • 15. Bien, Z., Xu, J.X.: ‘Iterative learning control: analysis, design, integration and applications’ (Kluwer Academic Publishers, Dordrecht, 1998).
    16. 16)
      • 16. Xu, J.X., Tan, Y.: ‘Linear and nonlinear iterative learning control’ (Springer-Verlag, Berlin, 2003).
    17. 17)
      • 17. Tharayil, D., Alleyne, A.G.: ‘A survey of iterative learning control: A learning-based method for highperformance tracking control’, IEEE Control Syst. Mag., 2006, 26, (3), pp. 96114.
    18. 18)
      • 18. Ahn, H.S., Chen, Y.: ‘Iterative learning control for multi-agent formation’. Proc. ICROS-SICE Int. Joint Conf., Fukuoka, Japan, August 2009, pp. 31113116.
    19. 19)
      • 19. Liu, Y., Jia, Y.: ‘An iterative learning approach to formation control of multi-agent systems’, Syst. Control Lett., 2012, 61, (1), pp. 148154.
    20. 20)
      • 20. Meng, D., Jia, Y., Du, J., et al: ‘On iterative learning algorithms for the formation control of nonlinear multi-agent systems’, Automatica, 2014, 50, (1), pp. 291296.
    21. 21)
      • 21. Meng, D., Jia, Y., Du, J., et al: ‘High-precision formation control of nonlinear multi-agent systems with switching topologies: A learning approach’, Int. J. Robust Nonlinear Control, 2015, 25, (13), pp. 19932018.
    22. 22)
      • 22. Liu, Y., Jia, Y.: ‘Robust formation control of discrete-time multi-agent systems by iterative learning approach’, Int. J. Syst. Sci., 2015, 46, (4), pp. 625633.
    23. 23)
      • 23. Meng, D., Jia, Y., Du, J., et al: ‘Tracking control over a finite interval for multi-agent systems with a time-varying reference trajectory’, Syst. Control Lett., 2012, 61, (7), pp. 807818.
    24. 24)
      • 24. Yang, S., Xu, J.X., Huang, D.: ‘Iterative learning control for multi-agent systems consensus tracking’. Proc. 51st IEEE Conf. on Decision Control, Maui, Hawaii, USA, December 2012, pp. 46724677.
    25. 25)
      • 25. Yang, S., Xu, J.X., Huang, D., et al: ‘Optimal iterative learning control design for multi-agent systems consensus tracking’, Syst. Control Lett., 2014, 69, (7), pp. 8089.
    26. 26)
      • 26. Li, J., Li, J.: ‘Adaptive iterative learning control for consensus of multi-agent systems’, IET Control Theory Applic., 2013, 7, (1), pp. 136142.
    27. 27)
      • 27. Li, J., Li, J.: ‘Iterative learning control approach for a kind of heterogeneous multi-agent systems with distributed initial state learning’, Appl. Math. Comput., 2015, 265, (8), pp. 10441057.
    28. 28)
      • 28. Li, J., Liu, S., Li, J.: ‘Observer-based distributed adaptive iterative learning control for linear multi-agent systems’, Int. J. Syst. Sci., 2017, 48, (14), pp. 29482955.
    29. 29)
      • 29. Fu, Q., Li, X., Du, L., et al: ‘Consensus control for multi-agent systems with quasi-one-sided Lipschitz nonlinear dynamics via iterative learning algorithm’, Nonlinear Dyn., 2018, 91, (4), pp. 26212630.
    30. 30)
      • 30. Luo, D., Wang, J., Shen, D.: ‘Learning formation control for fractional-order multiagent systems’, Math. Meth. Appl. Sci., 2018, 47, (13), pp. 50035014.
    31. 31)
      • 31. Shen, D., Xu, J.X.: ‘Distributed learning consensus for heterogenous high-order nonlinear multi-agent systems with output constraints’, Automatica, 2018, 97, (11), pp. 6472.
    32. 32)
      • 32. Luenberger, D.G., Arbel, A.: ‘Singular dynamic Leontief systems’, Econometrica, 1977, 45, (4), pp. 991995.
    33. 33)
      • 33. Dai, L.: ‘Singular control systems’ (Springer-Verlag, New York, 1989).
    34. 34)
      • 34. Duan, G.R.: ‘Analysis and design of descriptor linear systems’ (Springer-Verlag, New York, 2010).
    35. 35)
      • 35. Yang, X., Liu, G.: ‘Necessary and sufficient consensus conditions of descriptor multi-agent systems’, IEEE Trans. Circuits Syst. I, Regul. Pap., 2012, 59, (11), pp. 26692677.
    36. 36)
      • 36. Yang, X., Liu, G.: ‘Consensus of descriptor multi-agent systems via dynamic compensators’, IET Control Theory Applic., 2014, 8, (6), pp. 389398.
    37. 37)
      • 37. Xi, J., Yu, Y., Liu, G., et al: ‘Guaranteed-cost consensus for singular multi-agent systems with switching topologies’, IEEE Trans. Circuits Syst. I, Regul. Pap., 2014, 61, (5), pp. 15311542.
    38. 38)
      • 38. Yang, X., Liu, G.: ‘Admissible consensus for heterogeneous descriptor multi-agent systems’, Int. J. Syst. Sci., 2016, 47, (12), pp. 28692877.
    39. 39)
      • 39. Yang, X., Liu, G.: ‘Admissible consensus for networked singular multi-agent systems with communication delays’, Int. J. Syst. Sci., 2017, 48, (4), pp. 705714.
    40. 40)
      • 40. Gao, L., Cui, Y., Chen, W., et al: ‘Leader-following consensus for discrete-time descriptor multi-agent systems with observer-based protocols’, Trans. Inst. Meas. Control, 2016, 38, (11), pp. 13531364.
    41. 41)
      • 41. Gao, L., Cui, Y., Chen, W.: ‘Admissible consensus for descriptor multi-agent systems via distributed observer-based protocols’, J. Franklin Inst., 2017, 354, (1), pp. 257276.
    42. 42)
      • 42. Zheng, T., He, M., Xi, J., et al: ‘Leader-following guaranteed-performance consensus design for singular multi-agent systems with Lipschitz nonlinear dynamics’, Neurocomputing, 2017, 266, (11), pp. 651658.
    43. 43)
      • 43. Sun, M., Wang, D.: ‘Iterative learning control with initial rectifying action’, Automatica, 2002, 38, (7), pp. 11771182.
    44. 44)
      • 44. Xu, J.X., Yan, R: ‘On initial conditions in iterative learning control’, IEEE Trans. Autom. Control, 2005, 50, (9), pp. 13491354.
    45. 45)
      • 45. Horn, R.A., Johnson, C.R.: ‘Matrix analysis’ (Cambridge University Press, New York, 1985).
http://iet.metastore.ingenta.com/content/journals/10.1049/iet-cta.2018.5901
Loading

Related content

content/journals/10.1049/iet-cta.2018.5901
pub_keyword,iet_inspecKeyword,pub_concept
6
6
Loading
This is a required field
Please enter a valid email address