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Iterative learning control for nonlinear differential inclusion systems
Author(s) -
Liu Shengda,
Wang JinRong,
Shen Dong,
Fečkan Michal
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.4920
Subject(s) - iterative learning control , differential inclusion , lipschitz continuity , convex hull , convergence (economics) , nonlinear system , computer science , trajectory , mathematical optimization , set (abstract data type) , control theory (sociology) , iterative method , regular polygon , mathematics , control (management) , artificial intelligence , geometry , astronomy , economics , programming language , economic growth , mathematical analysis , physics , quantum mechanics
Summary In this paper, we propose an iterative learning control strategy to track a desired trajectory for a class of uncertain systems governed by nonlinear differential inclusions. By imposing Lipschitz continuous condition on a set‐valued mapping described by a closure of the convex hull of a set and using D ‐type and PD ‐type updating laws with initial iterative learning, we establish the iterative learning process and give a new convergence analysis with the help of Steiner‐type selector. Finally, numerical examples are provided to verify the effectiveness of the proposed method with suitable selection of set‐valued mappings. An application to the speed control of robotic fish is also given.