ISSN 0253-2778

CN 34-1054/N

open
Free to Publish. Free to Read.
Open AccessOpen Access JUSTC Original Paper

Effect of output noise in inverse-model-based iterative learning control

  • Electric Power Research Institute, SMEPC, Shanghai 200437, China

Cite this:
https://doi.org/10.3969/j.issn.0253-2778.2014.01.010
More Information
  • Author Bio:

    LIU Shaojie, male, born in 1980, PhD. Research field: Automation of electricpower systems.

  • Received Date: 29 September 2013
  • Accepted Date: 27 December 2013
  • Rev Recd Date: 27 December 2013
  • Publish Date: 30 January 2014
    Abstract

  • Abstract

    Inverse-model-based iterative learning control (ILC) for linear-time invariant, single-input single output (SISO) systems subject to output noise is proposed with the intent of predicting expectation of the underlying “noise-free” mean square error (Euclidean norm) on each iteration. Frequency domain formulae are derived to provide an insight into links between plant characteristics, noise spectra and inverse-model-based ILC parameters. Simulations are used to illustrate the theoretical findings.

    Abstract

    Inverse-model-based iterative learning control (ILC) for linear-time invariant, single-input single output (SISO) systems subject to output noise is proposed with the intent of predicting expectation of the underlying “noise-free” mean square error (Euclidean norm) on each iteration. Frequency domain formulae are derived to provide an insight into links between plant characteristics, noise spectra and inverse-model-based ILC parameters. Simulations are used to illustrate the theoretical findings.
    • FullText(HTML)
  • loading
    • References(19)
  • [1]
    Arimoto S, Kawamura S, Miyazaki F. Bettering operations of robots by learning[J]. Journal of Robotic Systems, 1984, 1(2): 123-140.
    [2]
    Moore K L. Iterative learning control for deterministic systems [M]. Berlin, Germany: Springer-Verlag, 1992.
    [3]
    Bien Z, Xu J X. Iterative Learning Control: Analysis, Design, Integration and Applications [M]. Boston, USA: Kluwer, 1998.
    [4]
    Chen Y Q, Wen C Y. Iterative Learning Control: Convergence, Robustness and Applications [M]. London, UK: Springer-Verlag, 1999.
    [5]
    Ahn H S, Moore K L, Chen Y Q. Iterative learning control: robustness and monotonic convergence for interval systems [M]. London: Springer-Verlag, 2007.
    [6]
    Bristow D A, Tharayil M, Alleyne A G. A survey of iterative learning control [J]. IEEE Control Systems Magazine, 2006, 26(3): 96-114.
    [7]
    Ahn H, S Chen Y Q, Moore K L. Iterative learning control: brief survey and categorization [J]. IEEE Transactions on Systems, Man and Cybernetics: Part C, 2007, 37(6): 1 109-1 121.
    [8]
    Amann N, Owens D H, Rogers E. Iterative learning control using optimal feedback and feedforward actions [J]. International Journal of Control, 1996, 65(2): 277-293.
    [9]
    Owens D H, Freeman C T, van Dinh T. Norm-optimal iterative learning control with intermediate point weighting: Theory, algorithms, and experimental evaluation [J]. IEEE Transactions on Control Systems Technology, 2013, 21(3): 999-1 007.
    [10]
    Owens D H, Htnen J. Iterative learning control — An optimization paradigm [J]. Annual Reviews in Control, 2005, 29(1): 57-70.
    [11]
    Owens D H, Htnen J, Daley S. Robust monotone gradient-based discrete-time iterative learning control [J]. International Journal of Robust and Non-linear Control, 2008, 19(6): 634-661.
    [12]
    Owens D H, Feng K. Parameter optimization in iterative learning control [J]. International Journal of Control, 2003, 76(11): 1 059-1 069.
    [13]
    Owens D H, Freeman C T, Chu B. Multivariable norm optimal iterative learning control with auxiliary optimisation [J]. International Journal of Control, 2013, 86(6): 1 026-1 045.
    [14]
    Owens D H, Liu S. Iterative learning control: Quantifying the effect of output noise [J]. Control Theory and Applications, 2011, 5(2): 379-388.
    [15]
    Daley S, Owens D H, Htnen J. Application of optimal iterative learning control to the dynamic testing of structures [J]. Special Issue of the Proceedings of the Institution of Mechanical Engineers, Part I, 2007, 221(1): 211-222.
    [16]
    Chen W, Chen Y Q, Yeh C P. Robust iterative learning control via continuous sliding-mode technique with validation on an SRV02 rotary plant [J]. Mechatronics, 2012, 22(5): 588-593.
    [17]
    Bouakrif F, Boukhetala D, Boudjema F. Velocity observer-based iterative learning control for robot manipulators [J]. International Journal of Systems Science, 2013, 44(2): 214-222.
    [18]
    Butcher M, Kamiri A, Longchamp R. A statistical analysis of certain iterative learning control algorithms [J]. International Journal of Control, 2008, 81(1): 156-166.
    [19]
    Harte T J, Htnen J, Owens D H. Discrete-time inverse model-based iterative learning control: Stability, monotonicity and robustness [J]. International Journal of Control, 2005, 78(8): 577-586.
    • Supplements(0)
    • Related Articles
    • Track Citations
  • 加载中

Catalog

    Get Citation
    PDF
    XML
    [1]
    Arimoto S, Kawamura S, Miyazaki F. Bettering operations of robots by learning[J]. Journal of Robotic Systems, 1984, 1(2): 123-140.
    [2]
    Moore K L. Iterative learning control for deterministic systems [M]. Berlin, Germany: Springer-Verlag, 1992.
    [3]
    Bien Z, Xu J X. Iterative Learning Control: Analysis, Design, Integration and Applications [M]. Boston, USA: Kluwer, 1998.
    [4]
    Chen Y Q, Wen C Y. Iterative Learning Control: Convergence, Robustness and Applications [M]. London, UK: Springer-Verlag, 1999.
    [5]
    Ahn H S, Moore K L, Chen Y Q. Iterative learning control: robustness and monotonic convergence for interval systems [M]. London: Springer-Verlag, 2007.
    [6]
    Bristow D A, Tharayil M, Alleyne A G. A survey of iterative learning control [J]. IEEE Control Systems Magazine, 2006, 26(3): 96-114.
    [7]
    Ahn H, S Chen Y Q, Moore K L. Iterative learning control: brief survey and categorization [J]. IEEE Transactions on Systems, Man and Cybernetics: Part C, 2007, 37(6): 1 109-1 121.
    [8]
    Amann N, Owens D H, Rogers E. Iterative learning control using optimal feedback and feedforward actions [J]. International Journal of Control, 1996, 65(2): 277-293.
    [9]
    Owens D H, Freeman C T, van Dinh T. Norm-optimal iterative learning control with intermediate point weighting: Theory, algorithms, and experimental evaluation [J]. IEEE Transactions on Control Systems Technology, 2013, 21(3): 999-1 007.
    [10]
    Owens D H, Htnen J. Iterative learning control — An optimization paradigm [J]. Annual Reviews in Control, 2005, 29(1): 57-70.
    [11]
    Owens D H, Htnen J, Daley S. Robust monotone gradient-based discrete-time iterative learning control [J]. International Journal of Robust and Non-linear Control, 2008, 19(6): 634-661.
    [12]
    Owens D H, Feng K. Parameter optimization in iterative learning control [J]. International Journal of Control, 2003, 76(11): 1 059-1 069.
    [13]
    Owens D H, Freeman C T, Chu B. Multivariable norm optimal iterative learning control with auxiliary optimisation [J]. International Journal of Control, 2013, 86(6): 1 026-1 045.
    [14]
    Owens D H, Liu S. Iterative learning control: Quantifying the effect of output noise [J]. Control Theory and Applications, 2011, 5(2): 379-388.
    [15]
    Daley S, Owens D H, Htnen J. Application of optimal iterative learning control to the dynamic testing of structures [J]. Special Issue of the Proceedings of the Institution of Mechanical Engineers, Part I, 2007, 221(1): 211-222.
    [16]
    Chen W, Chen Y Q, Yeh C P. Robust iterative learning control via continuous sliding-mode technique with validation on an SRV02 rotary plant [J]. Mechatronics, 2012, 22(5): 588-593.
    [17]
    Bouakrif F, Boukhetala D, Boudjema F. Velocity observer-based iterative learning control for robot manipulators [J]. International Journal of Systems Science, 2013, 44(2): 214-222.
    [18]
    Butcher M, Kamiri A, Longchamp R. A statistical analysis of certain iterative learning control algorithms [J]. International Journal of Control, 2008, 81(1): 156-166.
    [19]
    Harte T J, Htnen J, Owens D H. Discrete-time inverse model-based iterative learning control: Stability, monotonicity and robustness [J]. International Journal of Control, 2005, 78(8): 577-586.
    Recommended articles
    TrendMD
    Volume 44 Issue 1 page: 79-86
    Cover

    Article Metrics

    Article views (17) PDF downloads(61)
    Proportional views

    Copyright © Editorial Office of JUSTC, All Rights Reserved. 皖ICP备05002528号

    Supported by: Beijing Renhe Information Technology Co. Ltd    

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return
    Return