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Iterative learning identification for a class of parabolic distributed parameter systems

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  • Xingyu Zhou
  • Haoping Wang
  • Xisheng Dai
  • Senping Tian

Abstract

This paper presents an iterative learning identification scheme for a class of parabolic distributed parameter systems with unknown curved surfaces. The identification design method is proposed on the basis of the iterative learning concept. Initially, a new nonlinear learning identification law based on vector-plot analysis is developed to estimate the curved surface with spatial-temporal varying iteratively. Subsequently, through theoretical analysis, the sufficient convergence conditions for identification error in the sense of $\mathbf {L}_2 $L2 norm is manifested. Furthermore, a high-order P-type learning law is applied to identifying the curved surface in order to compare the convergent rate with the aforesaid identification law. Finally, simulation results on a specific numerical example and the temperature profile of a catalytic rod confirm that the proposed learning identification laws is effective.

Suggested Citation

  • Xingyu Zhou & Haoping Wang & Xisheng Dai & Senping Tian, 2019. "Iterative learning identification for a class of parabolic distributed parameter systems," International Journal of Systems Science, Taylor & Francis Journals, vol. 50(16), pages 2918-2934, December.
    • Handle: RePEc:taf:tsysxx:v:50:y:2019:i:16:p:2918-2934
    DOI: 10.1080/00207721.2019.1691281
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    Cited by:

    1. Yan, Fei & Qiu, Jiangchen & Tian, Jianyan, 2022. "An iterative learning identification strategy for nonlinear macroscopic traffic flow model," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 604(C).

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