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SCIENCE CHINA Information Sciences, Volume 60, Issue 7: 079202(2017) https://doi.org/10.1007/s11432-016-0341-7

Iterative learning control approach for consensus of multi-agent systems with regular linear dynamics

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  • ReceivedSep 21, 2016
  • AcceptedNov 18, 2016
  • PublishedJan 17, 2017

Abstract


Funded by

National Natural Science Foundation of China(~11371013)


Acknowledgment

Acknowledgments

This work was supported by National Natural Science Foundation of China (Grant No.~11371013) and Natural Science Foundation of Suzhou University of Science and Technology in 2016.


References

[1] Arimoto S, Kawamura S, Miyazaki F. Bettering operation of robots by learning. J Robot Syst, 1984, 1: 123-140 CrossRef Google Scholar

[2] Xu J X, Tan Y. On the P-type and Newton-type ILC schemes for dynamic systems with non-affine-in-input factors. Automatica, 2002, 38: 1237-1242 CrossRef Google Scholar

[3] Ma C Q, Zhang J F. Necessary and sufficient conditions for consensusability of linear multi-agent systems. IEEE Trans Automat Control, 2010, 55: 1263-1268 CrossRef Google Scholar

[4] Hu J, Lin Y S. Consensus control for multi-agent systems with double-integrator dynamics and time-delays. IET Control Theory Appl, 2010, 4: 109-118 CrossRef Google Scholar

[5] Yang S P, Xu J X. Multi-agent consensus tracking with input sharing by iterative learning control. In: Proceedings of European Control Conference, Strasbourg, 2014. 868--873. Google Scholar

[6] Meng D Y, Jia Y M. Formation control for multi-agent systems through an iterative learning design approach. Int J Robust Nonlinear Control, 2014, 24: 340-361 CrossRef Google Scholar

[7] Meng D, Jia Y. Finite-time consensus for multi-agent systems via terminal feedback iterative learning. IET Control Theory Appl, 2011, 5: 2098-2110 CrossRef Google Scholar

[8] Sun M X, Huang B J. Iterative Learning Control (in Chinese). Beijing: National Defense Industry Press, 1999. Google Scholar

[9] Meng D Y, Jia Y M, Du J P, et al. Tracking control over a finite interval for multi-agent systems with a time-varying reference trajectory. Syst Control Lett, 2012, 61: 807-818 CrossRef Google Scholar

[10] Yang S P, Xu J X, Huang D Q. Iterative learning control for multi-agent systems consensus tracking. In: Proceedings of the 51st IEEE Conference on Decision and Control, Maui, 2012. 4672--4677. Google Scholar

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