Improved data-driven optimal TILC using time-varying input signals

被引：27

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：

JOURNAL OF PROCESS CONTROL | 2014年 / 24卷 / 12期

基金：

中国国家自然科学基金;

关键词：

Data-driven control; Optimal terminal ILC; Time-varying input signals; Multiple time-varying partial derivatives; Nonlinear discrete-time systems; ITERATIVE LEARNING CONTROL; OPTIMIZATION PROBLEMS; BATCH PROCESSES; TRACKING; SYSTEMS; POINTS; MODELS; ROBOTS;

D O I：

10.1016/j.jprocont.2014.07.007

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

A new improved data-driven optimal TILC (DDOTILC) is proposed for a class of nonlinear discrete-time systems by using time-varying control input signals to enhance control, performance. An equivalent dynamical linear presentation is developed in the iteration domain for the repeatable nonlinear system, where the time-varying partial derivatives of the system with respect to the time-varying control inputs reflect the dynamical characters of the plant. Both the time-varying input signals and time-varying partial derivatives over the entire finite time interval are updated in batches, respectively. The proposed approach is a data-driven control scheme and only the boundedness of the partial derivatives is needed for control system design and analysis. Both rigorous mathematical analysis and the simulation results are provide to verify the applicability and effectiveness of the proposed approach further. (C) 2014 Elsevier Ltd. All rights reserved.

引用

页码：78 / 85

页数：8

共 23 条

[11] From model-based control to data-driven control: Survey, classification and perspective [J].

Hou, Zhong-Sheng ;

Wang, Zhuo .

INFORMATION SCIENCES, 2013, 235 :3-35

[12] Terminal iterative learning control based station stop control of a train [J].

Hou, Zhongsheng ;

Wang, Yi ;

Yin, Chenkun ;

Tang, Tao .

INTERNATIONAL JOURNAL OF CONTROL, 2011, 84 (07) :1263-1274

[13] Newton method based iterative learning control for discrete non-linear systems [J].

Lin, T. ;

Owens, D. H. ;

Haetoenen, J. .

INTERNATIONAL JOURNAL OF CONTROL, 2006, 79 (10) :1263-1276

[14] ACCURATE SOLUTION OF DIFFERENTIAL ALGEBRAIC OPTIMIZATION PROBLEMS [J].

LOGSDON, JS ;

BIEGLER, LT .

INDUSTRIAL & ENGINEERING CHEMISTRY RESEARCH, 1989, 28 (11) :1628-1639

[15] DECOMPOSITION STRATEGIES FOR LARGE-SCALE DYNAMIC OPTIMIZATION PROBLEMS [J].

LOGSDON, JS ;

BIEGLER, LT .

CHEMICAL ENGINEERING SCIENCE, 1992, 47 (04) :851-864

[16]

Ray W.H., 1981, ADV PROCESS CONTROL

[17] Single-cycle and multi-cycle generalized 2D model predictive iterative learning control (2D-GPILC) schemes for batch processes [J].

Shi, Jia ;

Gao, Furong ;

Wu, Tie-Jun .

JOURNAL OF PROCESS CONTROL, 2007, 17 (09) :715-727

[18] Iterative learning control in optimal tracking problems with specified data points [J].

Son, Tong Duy ;

Ahn, Hyo-Sung ;

Moore, Kevin L. .

AUTOMATICA, 2013, 49 (05) :1465-1472

[19]

Son TD, 2011, 2011 IEEE INTERNATIONAL SYMPOSIUM ON INTELLIGENT CONTROL (ISIC), P1528, DOI 10.1109/ISIC.2011.6045393

[20]

Son TD, 2011, P AMER CONTR CONF, P3651

← 1 2 3 →