A Data-driven Optimal Design of Point-to-Point ILC

被引：0

作者：

Chi Ronghu ^{[1
]}

Hou Zhongsheng ^{[2
]}

Jin Shangtai ^{[2
]}

Wang Danwei ^{[3
]}

机构：

[1] Qingdao Univ Sci & Technol, Sch Automat & Elect Engn, Qingdao 266042, Peoples R China

[2] Beijing Jiaotong Univ, Sch Elect & Informat Engn, Adv Control Syst Lab, Beijing 100044, Peoples R China

[3] Nanyang Technol Univ, Sch Elect & Elect Engn, EXQUISITUS, Ctr E City, Singapore 639798, Singapore

来源：

2013 32ND CHINESE CONTROL CONFERENCE (CCC) | 2013年

基金：

美国国家科学基金会;

关键词：

Data-driven control; optimal ILC; Point-to-point tracking tasks; Nonlinear discrete-time systems; ITERATIVE LEARNING CONTROL; RESIDUAL VIBRATION SUPPRESSION; CONVERGENCE; SYSTEMS; MODELS; ROBOTS;

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：

This paper presents a new data-driven optimal design framework of point-to-point iterative learning control (PTP-ILC), where the control signal is directly updated from the errors of given multiple intermediate pass points. A major contribution is that the presented optimal PTP-ILC mechanism only uses the real-time measured I/O data without any model information of the plant for the controller design, convergence analysis, and conduction, from which the distinctive 'data-driven' feature of the presented approaches is obvious and intuitional. Rigorous mathematical analysis is developed to illustrate the effecience of the proposed approach.

引用

页码：2934 / 2938

页数：5

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