Iterative learning identification for a class of parabolic distributed parameter systems. Issue 16 (10th December 2019) - Zhou, Xingyu
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 L 2 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.