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High‐order internal model‐based iterative learning control design for nonlinear distributed parameter systems
Author(s) -
Gu Panpan,
Tian Senping
Publication year - 2020
Publication title -
international journal of robust and nonlinear control
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.361
H-Index - 106
eISSN - 1099-1239
pISSN - 1049-8923
DOI - 10.1002/rnc.5052
Subject(s) - iterative learning control , nonlinear system , trajectory , convergence (economics) , internal model , control theory (sociology) , norm (philosophy) , iterative method , mathematical optimization , computer science , mathematics , domain (mathematical analysis) , control (management) , artificial intelligence , mathematical analysis , physics , quantum mechanics , astronomy , political science , law , economics , economic growth
Summary This article deals with the problem of iterative learning control algorithm for a class of nonlinear parabolic distributed parameter systems (DPSs) with iteration‐varying desired trajectories. Here, the variation of the desired trajectories in the iteration domain is described by a high‐order internal model. According to the characteristics of the systems, the high‐order internal model‐based P‐type learning algorithm is constructed for such nonlinear DPSs, and furthermore, the corresponding convergence theorem of the presented algorithm is established. It is shown that the output trajectory can converge to the desired trajectory in the sense of ( L 2 , λ ) ‐norm along the iteration axis within arbitrarily small error. Finally, a simulation example is given to illustrate the effectiveness of the proposed method.