Norm-Optimal Iterative Learning Control With Intermediate Point Weighting: Theory, Algorithms, and Experimental Evaluation

被引：67
|
作者：
Owens, David H. [1 ,2 ]
Freeman, Christopher T. [2 ]
Thanh Van Dinh [2 ]
机构：
[1] Univ Sheffield, Dept Automat Control & Syst Engn, Sheffield S1 3JD, S Yorkshire, England
[2] Univ Southampton, Sch Elect & Comp Sci, Southampton SO17 1BJ, Hants, England
来源：
IEEE TRANSACTIONS ON CONTROL SYSTEMS TECHNOLOGY | 2013年 / 21卷 / 03期
关键词：
Iterative learning control (ILC); iterative methods; learning control systems; linear systems; motion control; non-minimum phase systems; optimization methods; test facilities; ILC; DESIGN;
D O I：
10.1109/TCST.2012.2196281
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 brief considers the iterative learning control (ILC) problem when tracking is only required at a subset of isolated time points along the trial duration. It presents a norm-optimal ILC solution to the problem with well-defined convergence properties, design guidelines, and supporting experimental results using an electromechanical test facility.
引用
收藏
页码：999 / 1007
页数：9
相关论文
共 50 条
  • [41] Experimental comparison of stochastic iterative learning control algorithms
    Cai, Zhonglun
    Freeman, Chris T.
    Lewin, Paul L.
    Rogers, Eric
    2008 AMERICAN CONTROL CONFERENCE, VOLS 1-12, 2008, : 4548 - 4553
  • [42] Robustness of iterative learning control - Algorithms with experimental benchmarking
    School of Electronics and Computer Science, University of Southampton, Southampton SO17 1BJ, United Kingdom
    Bull. Pol. Acad. Sci. Tech. Sci., 2008, 3 (205-215):
  • [43] Experimental evaluation of iterative learning control algorithms for non-minimum phase plants
    Freeman, CT
    Lewin, PL
    Rogers, E
    INTERNATIONAL JOURNAL OF CONTROL, 2005, 78 (11) : 826 - 846
  • [44] A Frequency Domain Approach for Designing Filters for Norm-Optimal Iterative Learning Control and Its Fundamental Tradeoff Between Robustness, Convergence Speed and Steady State Error
    Ge, Xinyi
    Stein, Jeffrey L.
    Ersal, Tulga
    2016 AMERICAN CONTROL CONFERENCE (ACC), 2016, : 384 - 391
  • [45] Constrained point-to-point iterative learning control with experimental verification
    Freeman, Chris T.
    CONTROL ENGINEERING PRACTICE, 2012, 20 (05) : 489 - 498
  • [46] Iterative Learning Control for Optimal Multiple-Point Tracking
    Son, Tong Duy
    Dinh Hoa Nguyen
    Ahn, Hyo-Sung
    2011 50TH IEEE CONFERENCE ON DECISION AND CONTROL AND EUROPEAN CONTROL CONFERENCE (CDC-ECC), 2011, : 6025 - 6030
  • [47] Broiler Growth Optimization using Norm Optimal Terminal Iterative Learning Control
    Johansen, Simon V.
    Jensen, Martin R.
    Chu, Bing
    Bendtsen, Jan D.
    Mogensen, Jesper
    Rogers, Eric
    2018 IEEE CONFERENCE ON CONTROL TECHNOLOGY AND APPLICATIONS (CCTA), 2018, : 1258 - 1264
  • [48] A computationally efficient norm optimal iterative learning control approach for LTV systems
    Sun, Heqing
    Alleyne, Andrew G.
    AUTOMATICA, 2014, 50 (01) : 141 - 148
  • [49] Norm Optimal Iterative Learning Control for General Spatial Path Following Problem
    Chen, Yiyang
    Chu, Bing
    Freeman, Christopher T.
    2018 IEEE CONFERENCE ON DECISION AND CONTROL (CDC), 2018, : 4933 - 4938
  • [50] Distributed Norm Optimal Iterative Learning Control for Formation of Networked Dynamical Systems
    Chen, Bin
    Chu, Bing
    2019 IEEE 58TH CONFERENCE ON DECISION AND CONTROL (CDC), 2019, : 5574 - 5579
12345