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Volume 1 Issue 3
Jul.  2014

IEEE/CAA Journal of Automatica Sinica

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Xiaoli Li, Kang Wang and Dexin Liu, "An Improved Result of Multiple Model Iterative Learning Control," IEEE/CAA J. of Autom. Sinica, vol. 1, no. 3, pp. 315-322, 2014.
Citation: Xiaoli Li, Kang Wang and Dexin Liu, "An Improved Result of Multiple Model Iterative Learning Control," IEEE/CAA J. of Autom. Sinica, vol. 1, no. 3, pp. 315-322, 2014.

An Improved Result of Multiple Model Iterative Learning Control

Funds:

This work was supported by National Natural Science Foundation of China (61074055, 61473034), Fundamental Research Funds for the Central Universities (FRFTP-12-005B), Program for New Century Excellent Talents in Universities (NCET-11-0578) and Specialized Research Fund for the Doctoral Program of Higher Education (SRFDP) (20130006110008).

  • For system operating repetitively, iterative learning control (ILC) has been tested as an effective method even with estimated models. However, the control performance may deteriorate due to sudden system failure or the adoption of imprecise model. The multiple model iterative learning control (MMILC) method shows great potential to improve the transient response and control performance. However, in existed MMILC, the stability can be guaranteed only by finite switching or very strict conditions about coefficient matrix, which make the application of MMILC a little difficult. In this paper, an improved MMILC method is presented. Control procedure is simplified and the ceasing condition is relaxed. Even with infinite times of model switching, system output is proved convergent to the desired trajectory. Simulation studies are carried out to show the effectiveness of the proposed method.

     

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