Iterative learning identification for a class of parabolic distributed parameter systems

Xingyu Zhou, Haoping Wang, Xisheng Dai and Senping Tian

International Journal of Systems Science, 2019, vol. 50, issue 16, 2918-2934

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.

Date: 2019
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DOI: 10.1080/00207721.2019.1691281

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