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Adaptive iterative learning control for discrete‐time nonlinear systems with multiple iteration‐varying high‐order internal models
Author(s) -
Yu Miao,
Chai Sheng
Publication year - 2021
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.5690
Subject(s) - iterative learning control , convergence (economics) , nonlinear system , internal model , iterative method , discrete time and continuous time , parametric statistics , control theory (sociology) , mathematics , power iteration , mathematical optimization , fixed point iteration , computer science , fixed point , control (management) , artificial intelligence , statistics , physics , quantum mechanics , economics , economic growth , mathematical analysis
In this work, an adaptive iterative learning control (AILC) method is designed for a class of parametric discrete‐time nonlinear systems with random initial condition, unknown time‐varying input gain and multiple time‐iteration‐varying factors including multiple unknown time‐iteration‐varying parameters and unknown time‐iteration‐varying external disturbance. The iteration‐varying factors can be generated by virtue of multiple iteration‐varying high‐order internal models, respectively, where iteration‐varying high‐order internal model means it has iteration‐varying order or coefficients. Moreover, the parameter updating law is designed based on the recursive least squares algorithm. Using the designed AILC based on iteration‐varying high‐order internal model, the learning convergence in the iteration domain is guaranteed through rigorous theoretical analysis under Lyapunov theory. Finally, two simulation examples are given to demonstrate that the proposed scheme is effective.