A journal of IEEE and CAA , publishes high-quality papers in English on original theoretical/experimental research and development in all areas of automation
Advanced Search
Volume 1 Issue 3
Jul.  2014

IEEE/CAA Journal of Automatica Sinica

  • JCR Impact Factor: 15.3, Top 1 (SCI Q1)
    CiteScore: 23.5, Top 2% (Q1)
    Google Scholar h5-index: 77， TOP 5
Turn off MathJax
Article Contents
Article Navigation >  IEEE/CAA Journal of Automatica Sinica  > 2014  >  1(3): 315-322
Xiaoli Li, Kang Wang and Dexin Liu, "An Improved Result of Multiple Model Iterative Learning Control," IEEE/CAA J. of Autom. Sinica, vol. 1, no. 3, pp. 315-322, 2014.
Citation: Xiaoli Li, Kang Wang and Dexin Liu, "An Improved Result of Multiple Model Iterative Learning Control," IEEE/CAA J. of Autom. Sinica, vol. 1, no. 3, pp. 315-322, 2014.
shu

An Improved Result of Multiple Model Iterative Learning Control

  • School of Automation and Electrical Engineering, University of Science and Technology Beijing, Beijing 100083, China

Funds:

This work was supported by National Natural Science Foundation of China (61074055, 61473034), Fundamental Research Funds for the Central Universities (FRFTP-12-005B), Program for New Century Excellent Talents in Universities (NCET-11-0578) and Specialized Research Fund for the Doctoral Program of Higher Education (SRFDP) (20130006110008).

    Abstract
  • For system operating repetitively, iterative learning control (ILC) has been tested as an effective method even with estimated models. However, the control performance may deteriorate due to sudden system failure or the adoption of imprecise model. The multiple model iterative learning control (MMILC) method shows great potential to improve the transient response and control performance. However, in existed MMILC, the stability can be guaranteed only by finite switching or very strict conditions about coefficient matrix, which make the application of MMILC a little difficult. In this paper, an improved MMILC method is presented. Control procedure is simplified and the ceasing condition is relaxed. Even with infinite times of model switching, system output is proved convergent to the desired trajectory. Simulation studies are carried out to show the effectiveness of the proposed method.

     

    • FullText(HTML)
  • loading
    • References(34)
  • [1]
    Xu J X, Bien Z Z. The frontiers of iterative learning control. Iterative Learning Control. Boston: Kluwer, 1998. 9-35
    [2]
    Bu Xu-Hui, Hou Zhong-Sheng, Yu Fa-Shan, Fu Zi-Yi. Iterative learning control for trajectory tracking of farm vehicles. Acta Automatica Sinica, 2014, 40(2): 368-372 (in Chinese)
    [3]
    Li Z X, Yin C K, Jin S T, Hou Z S. Iterative learning control based automatic train operation with iteration-varying parameter. In: Proceedings of the 10th IEEE International Conference on Control and Automation. Hangzhou, China: IEEE, 2013. 51-56
    [4]
    Hou Z S, Yan J W, Xu J X, Li Z J. Modified iterative-learning-controlbased ramp metering strategies for freeway traffic control with iterationdependent factors. IEEE Transactions on Intelligent Transportation Systems, 2012, 13(2): 606-618
    [5]
    Arimoto S, Kawamura S, Miyazaki F. Bettering operation of robots by learning. Journal of Robotic Systems, 1984, 1(2): 123-140
    [6]
    Bristow D A, Tharayil M, Alleyne A G. A survey of iterative learning control. IEEE Control Systems Magazine, 2006, 26(3): 96-114
    [7]
    Hou Z S, Zhu Y M. Controller-dynamic-linearization-based model free adaptive control for discrete-time nonlinear systems. IEEE Transactions on Industrial Informatics, 2013, 9(4): 2301-2309
    [8]
    Liu T Q, Wang D W, Chi R H, Shen Q. Neural network based terminal iterative learning control for tracking run-varying reference point. In: Proceedings of the 9th Asian Control Conference. Istanbul, Turkey: IEEE, 2013. 1-5
    [9]
    Jiang P, Unbehauen R. Iterative learning neural network control for nonlinear system trajectory tracking. Neurocomputing, 2002, 48(1-4): 141-153
    [10]
    Chi R H, Wang D W, Hou Z S, Jin S T. Data-driven optimal terminal iterative learning control. Journal of Process Control, 2012, 22(10): 2026-2037
    [11]
    Owens D H, Freeman C T, van Thanh D. Norm-optimal iterative learning control with intermediate point weighting: theory, algorithms, and experimental evaluation. IEEE Transactions on Control Systems Technology, 2013, 21(3): 999-1007
    [12]
    Lee K S, Lee J H. Convergence of constrained model-based predictive control for batch processes. IEEE Transactions on Automatic Control, 2000, 45(10): 1928-1932
    [13]
    Shi Jia, Jiang Qing-Yin, Cao Zhi-Kai, Zhou Hua, Gao Fu-Rong. Twodimensional model predictive iterative learning control scheme based on a two-dimensional performance model. Acta Automatica Sinica, 2013, 39(5): 565-573 (in Chinese)
    [14]
    Bu Xu-Hui, Yu Fa-Shan, Hou Zhong-Sheng, Wang Fu-Zhong. Iterative learning control for a class of linear discrete-time switched systems. Acta Automatica Sinica, 2013, 39(9): 1564-1569 (in Chinese)
    [15]
    Xu J X, Tan Y. A composite energy function-based learning control approach for nonlinear systems with time-varying parametric uncertainties. IEEE Transactions on Automatic Control, 2002, 47(11): 1940-1945
    [16]
    Chi R H, Hou Z S, Xu J X. Adaptive ILC for a class of discrete-time systems with iteration-varying trajectory and random initial condition. Automatica, 2008, 44(8): 2207-2213
    [17]
    Sun Ming-Xuan, Yan Qiu-Zhen. Error tracking of iterative learning control systems. Acta Automatica Sinica, 2013, 39(3): 251-262 (in Chinese)
    [18]
    Norrlof M, Gunnarsson S. Time and frequency domain convergence properties in iterative learning control. International Journal of Control, 2002, 75(14): 1114-1126
    [19]
    Paszke W, Galkowski K, Rogers E. Repetitive process based iterative learning control design using frequency domain analysis. In: Proceedings of the 2012 IEEE International Conference on Control Applications. Dubrovnik, Croatia: IEEE, 2012. 1479-1484
    [20]
    Kurek J E, Zaremba M B. Iterative learning control synthesis based on 2-D system theory. IEEE Transactions on Automatic Control, 1993, 38(1): 121-125
    [21]
    Li X D, Chow T W S, Ho J K L. 2-D system theory based iterative learning control for linear continuous systems with time delays. IEEE Transactions on Circuits and Systems, 2005, 52(7): 1421-1430
    [22]
    Xu J X. A survey on iterative learning control for nonlinear systems. International Journal of Control, 2011, 84(7): 1275-1294
    [23]
    Bristow D A, Tharayil M, Alleyne A G. A survey of iterative learning control. IEEE Control Systems Magazine, 2006, 26(3): 96-114 6
    [24]
    Ahn H S, Chen Y Q, Moore K L. Iterative learning control: brief survey and categorization. IEEE Transactions on Systems, Man, and Cybernetics, 2007, 37(6): 1099-1121
    [25]
    Hwang D H, Bien Z, Oh S R. Iterative learning control method for discrete-time dynamic systems. IEE Proceedings D: Control Theory & Applications, 1991, 138(2): 139-144
    [26]
    Sun Ming-Xuan, Huang Bao-Jian. Iterative Learning Control. Beijing: National Defence Industry Press, 1999. 20-105 (in Chinese)
    [27]
    Sun Ming-Xuan, Bi Hong-Bo, Chen Bai-Xia, He Hai-Gang. Learning identification of a class of stochastic time-varying systems with colored noise. Control Theory & Applications, 2012, 29(8): 974-984 (in Chinese)
    [28]
    Chi R H, Hou Z S, Xu J X. Adaptive ILC for a class of discrete-time systems with iteration-varying trajectory and random initial condition. Automatica, 2008, 44(8): 2207-2213
    [29]
    Yu M, Wang J S, Qi D L. Discrete-time adaptive iterative learning control for high-order nonlinear systems with unknown control directions. International Journal of Control, 2012, 10(6): 1111-1118
    [30]
    Li X D, Xiao T F, Zheng H X. Adaptive discrete-time iterative learning control for non-linear multiple input multiple output systems with iteration-varying initial error and reference trajectory. IET Control Theory & Applications, 2011, 5(9): 1131-1139
    [31]
    Narendra K S, Xiang C. Adaptive control of discrete-time systems using multiple models. IEEE Transactions on Automatic Control, 2000, 45(9): 1669-1686
    [32]
    Li X L, Zhang W. Multiple model iterative learning control. Neurocomputing, 2010, 73(13-15): 2439-2445
    [33]
    Huang Lin. Linear Algebra in System and Control Theory. Beijing: Science Press, 1984. 250-251 (in Chinese)
    [34]
    Li Xiao-Li, Wang Wei. Multiple model switching controller for discretetime system. Journal of Northeastern University (Natural Science), 1999, 20(5): 468-469 (in Chinese)
    • Related Articles
    • Supplements(0)

Catalog

    通讯作者: 陈斌, bchen63@163.com
    • 1. 

      沈阳化工大学材料科学与工程学院 沈阳 110142

    1. 本站搜索
    2. 百度学术搜索
    3. 万方数据库搜索
    4. CNKI搜索
    Get Citation
    PDF
    XML

    Article Metrics

    Article views (1248) PDF downloads(13) Cited by()

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return
    Return