A Data-driven Iterative Learning Control for I/O Constrained Nonlinear Systems

被引：0
|
作者：
Chi, Ronghu [1 ]
Liu, Xiaohe [1 ]
Lin, Na [1 ]
Zhang, Ruikun [1 ]
机构：
[1] Qingdao Univ Sci & Technol, Sch Automat & Elect Engn, Qingdao, Peoples R China
来源：
2016 14TH INTERNATIONAL CONFERENCE ON CONTROL, AUTOMATION, ROBOTICS AND VISION (ICARCV) | 2016年
基金：
美国国家科学基金会;
关键词：
iterative learning control; data-driven control; I/O constraints; nonlinear discrete systems; DYNAMIC-SYSTEMS; DESIGN;
D O I：
暂无
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
In this paper, a new data-driven method is proposed for I/O constrained nonlinear systems. An iterative dynamic linearization is introduced for the controlled nonlinear systems. All of the constraints on the system inputs and outputs are reformulated with a linear matrix inequality. The learning control law is then developed by minimizing a predesigned cost function subjected to the linear matrix inequality constraint. Simulation results illustrate the effectiveness of the proposed approach.
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页数：4
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