Open AccessIterative Learning Control for a Class of Partial State Measurable Systems
Author(s) -
Xidan Wang,
Kaina Xu,
Qingdong Yan,
Jianping Cai,
Zhi Yang,
Zhiyan Bao
Publication year - 2020
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1631/1/012027
Subject(s) - iterative learning control , control theory (sociology) , parametric statistics , computer science , bounded function , trajectory , scheme (mathematics) , iterative method , state (computer science) , adaptive control , mathematical optimization , class (philosophy) , nonparametric statistics , tracking (education) , control (management) , mathematics , artificial intelligence , algorithm , psychology , mathematical analysis , pedagogy , statistics , physics , astronomy
In this paper, the tracking problem for a class of nonparametric systems is discussed, in which only partial system state can be measurable. After implementing reasonable coordinate transformations, an adaptive iterative learning control scheme is developed with the partial measurable system state information used. Iterative learning control and Robust control are together used to deal with non-parametric uncertainties under alignment condition. As the iteration number increases, the system output can follow the desired trajectory over the full period, and all signal are guaranteed to be bounded. A simulation example is given to verify the effectiveness of the proposed iterative learning control scheme.