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

共 50 条

[1] Data-Driven High-Order Point-to-Point ILC With Higher Computational Efficiency
Zhang, Xueming
Hou, Mengxue
Hou, Zhongsheng
[J]. IEEE TRANSACTIONS ON AUTOMATION SCIENCE AND ENGINEERING, 2023, : 1 - 16
[2] Data-driven Norm Optimal Iterative Learning Control for Point-to-Point Tasks
Jiang, Zheng
Chen, Bin
Chu, Bing
[J]. IFAC PAPERSONLINE, 2023, 56 (02): : 1051 - 1056
[3] An Improved Data-Driven Point-to-Point ILC Using Additional On-Line Control Inputs With Experimental Verification
Chi, Ronghu
Hou, Zhongsheng
Jin, Shangtai
Huang, Biao
[J]. IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS, 2019, 49 (04): : 687 - 696
[4] Data-driven predictive point-to-point iterative learning control
Zhang, Xueming
Hou, Zhongsheng
[J]. NEUROCOMPUTING, 2023, 518 : 431 - 439
[5] Objective-Driven ILC for Point-to-Point Movement Tasks
Freeman, Chris T.
Cai, Zhonglun
Lewin, Paul L.
Rogers, Eric
[J]. 2009 AMERICAN CONTROL CONFERENCE, VOLS 1-9, 2009, : 252 - 257
[6] Point-to-point ILC with Accelerated Convergence
Chu, Bing
Owens, David H.
Freeman, Chris T.
Liu, Yanhong
[J]. 2017 6TH DATA DRIVEN CONTROL AND LEARNING SYSTEMS (DDCLS), 2017, : 530 - 535
[7] A Data-Driven Design of Optimal ILC for Nonlinear Systems
Chi Ronghu
Hou Zhongsheng
Jin Shangtai
Wang Danwei
[J]. PROCEEDINGS OF THE 31ST CHINESE CONTROL CONFERENCE, 2012, : 7076 - 7079
[8] A Novel Design Framework for Point-to-Point ILC Using Successive Projection
Chu, Bing
Freeman, Chris T.
Owens, David H.
[J]. IEEE TRANSACTIONS ON CONTROL SYSTEMS TECHNOLOGY, 2015, 23 (03) : 1156 - 1163
[9] A coordinate descent approach to optimal tracking time allocation in point-to-point ILC
Chen, Yiyang
Chu, Bing
Freeman, Christopher T.
[J]. MECHATRONICS, 2019, 59 : 25 - 34
[10] Computationally Inexpensive Robust Data Driven Optimal Point-To-Point Tracking ILC for City Subway Trains subject to Iteration-Dependent Disturbances
Liu, Genfeng
Hou, Zhongsheng
[J]. PROCEEDINGS OF 2018 IEEE 7TH DATA DRIVEN CONTROL AND LEARNING SYSTEMS CONFERENCE (DDCLS), 2018, : 770 - 776

← 1 2 3 4 5 →