A journal of IEEE and CAA , publishes high-quality papers in English on original theoretical/experimental research and development in all areas of automation
Volume 6 Issue 5
Sep.  2019

IEEE/CAA Journal of Automatica Sinica

  • JCR Impact Factor: 11.8, Top 4% (SCI Q1)
    CiteScore: 17.6, Top 3% (Q1)
    Google Scholar h5-index: 77, TOP 5
Turn off MathJax
Article Contents
Ganggui Qu and Dong Shen, "Stochastic Iterative Learning Control With Faded Signals," IEEE/CAA J. Autom. Sinica, vol. 6, no. 5, pp. 1196-1208, Sept. 2019. doi: 10.1109/JAS.2019.1911696
Citation: Ganggui Qu and Dong Shen, "Stochastic Iterative Learning Control With Faded Signals," IEEE/CAA J. Autom. Sinica, vol. 6, no. 5, pp. 1196-1208, Sept. 2019. doi: 10.1109/JAS.2019.1911696

Stochastic Iterative Learning Control With Faded Signals

doi: 10.1109/JAS.2019.1911696
Funds:

the National Natural Science Foundation of China 61673045

the Fundamental Research Funds for the Central Universities XK1802-4

More Information
  • Stochastic iterative learning control (ILC) is designed for solving the tracking problem of stochastic linear systems through fading channels. Consequently, the signals used in learning control algorithms are faded in the sense that a random variable is multiplied by the original signal. To achieve the tracking objective, a two-dimensional Kalman filtering method is used in this study to derive a learning gain matrix varying along both time and iteration axes. The learning gain matrix minimizes the trace of input error covariance. The asymptotic convergence of the generated input sequence to the desired input value is strictly proved in the mean-square sense. Both output and input fading are accounted for separately in turn, followed by a general formulation that both input and output fading coexists. Illustrative examples are provided to verify the effectiveness of the proposed schemes.

     

  • loading
  • [1]
    S. Arimoto, S. Kawamura, and F. Miyazaki, "Bettering operation of robots by learning, " J. Robot. Syst., vol. 1, no. 2, pp. 123-140, 1984. doi: 10.1002/rob.4620010203
    [2]
    D. A. Bristow, M. Tharayil, and A. G. Alleyne, "A survey of iterative learning control: a learning-based method for high performance tracking control, " IEEE Control Syst. Mag., vol. 26, no. 3, pp. 96-114, 2006. doi: 10.1109/MCS.2006.1636313
    [3]
    D. Shen and Y. Wang, "Survey on stochastic iterative learning control, " J. Process Control, vol. 24, no. 12, pp. 64-77, 2014. doi: 10.1016/j.jprocont.2014.04.013
    [4]
    D. Shen, "Iterative learning control with incomplete information: A survey, " IEEE/CAA J. Autom. Sinica, vol. 5, no. 5, pp. 885-901, 2018. doi: 10.1109/JAS.2018.7511123
    [5]
    D. Shen, "A technical overview of recent progresses on stochastic iterative learning control, " Unmanned Systems, vol. 6, no. 3, pp. 147-164, 2018. doi: 10.1142/S2301385018400058
    [6]
    T. Seel, T. Schauer, and J. Raisch, "Monotonic convergence of iterative learning control systems with variable pass length, " Int. J. Control, vol. 90, no. 3, pp. 393-406, 2017.
    [7]
    X. Li, J.-X. Xu, and D. Huang, "An iterative learning control approach for linear systems with randomly varying trial lengths, " IEEE Trans. Automatic Control, vol. 59, no. 7, pp. 1954-1960, 2014. doi: 10.1109/TAC.2013.2294827
    [8]
    D. Shen and J.-X. Xu, "Adaptive learning control for nonlinear systems with randomly varying iteration lengths, " IEEE Trans. Neural Networks and Learning Systems, vol. 30, no. 4, pp. 1119-1132, 2019. doi: 10.1109/TNNLS.2018.2861216
    [9]
    D. Shen, W. Zhang, Y. Wang, and C.-J. Chien, "On almost sure and mean square convergence of P-type ILC under randomly varying iteration lengths, " Automatica, vol. 63, no. 1, pp. 359-365, 2016. http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=c17d17ccbef3d79620401ddc49f28f27
    [10]
    D. Shen, "Data-driven learning control for stochastic nonlinear systems: Multiple communication constraints and limited storage, " IEEE Trans. Neural Networks and Learning Systems, vol. 29, no. 6, pp. 2429-2440, 2018. doi: 10.1109/TNNLS.2017.2696040
    [11]
    D. Shen and J.-X. Xu, "A novel Markov chain based ILC analysis for linear stochastic systems under general data dropouts environments, " IEEE Trans. Automatic Control, vol. 62, no. 11, pp. 5850-5857, 2017. doi: 10.1109/TAC.2016.2638044
    [12]
    D. Shen and J.-X. Xu, "A framework of iterative learning control under random data dropouts: mean square and almost sure convergence, " Int. J. Adaptive Control and Signal Processing, vol. 31, no. 12, pp. 1825- 1852, 2017. doi: 10.1002/acs.2802
    [13]
    D. Meng, Y. Jia, and J. Du, "Consensus seeking via iterative learning for multi-agent systems with switching topologies and communication time-delays, " Int. J. Robust and Nonlinear Control, vol. 26, no. 12, pp. 3772-3790, 2016 http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=39da95248fc940200702925f5af0ba17
    [14]
    D. Shen and J.-X. Xu, "Distributed learning consensus for heterogenous high-order nonlinear multi-agent systems with output constraints, " Automatica, vol. 97, pp. 64-72, 2018. doi: 10.1016/j.automatica.2018.07.030
    [15]
    X. Bu, Z. Hou, L. Cui, and J. Yang, "Stability analysis of quantized iterative learning control systems using lifting representation, " Int. J. Adaptive Control and Signal Processing, vol. 31, no. 9, pp. 1327-1336, 2017. doi: 10.1002/acs.2767
    [16]
    W. Xiong, X. Yu, R. Patel, and W. Yu, "Iterative learning control for discrete-time systems with event-triggered transmission strategy and quantization, " Automatica, vol. 72, pp. 84-91, 2016. doi: 10.1016/j.automatica.2016.05.031
    [17]
    C. Zhang and D. Shen, "Zero-error convergence of iterative learning control based on uniform quantisation with encoding and decoding mechanism, " IET Control Theory & Applications, vol. 12, no. 14, pp. 1907-1915, 2018. http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=38b1b18aaa6cd2a2f1302fdf61a399b0
    [18]
    N. Elia, "Remote stabilization over fading channels, " Systems & Control Letters, vol. 54, no. 3, pp. 237-249, 2005. http://d.old.wanfangdata.com.cn/NSTLQK/NSTL_QKJJ025511761/
    [19]
    N. Xiao, L. Xie, and L. Qiu, "Feedback stabilization of discretetime networked systems over fading channels, " IEEE Trans. Automatic Control, vol. 57, no. 9, pp. 2176-2189, 2012. doi: 10.1109/TAC.2012.2183450
    [20]
    L. Xu, Y. Mo, L. Xie, and N. Xiao, "Mean square stabilization of linear discrete-time systems over power-constrained fading channels, " IEEE Trans. Automatic Control, vol. 62, no. 12, pp. 6505-6512, 2017. doi: 10.1109/TAC.2017.2656065
    [21]
    S. Dey, A. Leong, and J. Evans, "Kalman filtering with faded measurements, " Automatica, vol. 45, no. 10, pp. 2223-2233, 2009. doi: 10.1016/j.automatica.2009.06.025
    [22]
    H. Geng, Z. Wang, Y. Liang, Y. Cheng, and F. E. Alsaadi, "Tobit Kalman filter with fading measurments, " Signal Processing, vol. 140, pp. 60-68, 2017.
    [23]
    L. Su and G. Chesi, "Robust stability analysis and synthesis for uncertain discrete-time networked control systems over fading channels, " IEEE Trans. Automatic Control, vol. 62, no. 4, pp. 1966-1971, 2017. doi: 10.1109/TAC.2016.2585124
    [24]
    S. S. Saab, "A discrete-time stochastic learning control algorithm, " IEEE Trans. Automatic Control, vol. 46, no. 6, pp. 877-887, 2001. doi: 10.1109/9.928588
    [25]
    H. S. Ahn, Y. Q. Chen, and K. L. Moore, "Intermittent iterative learning control, " IEEE Int. Symposium on Intelligent Control, vol. 6, no. 1, pp. 832-837, 2006. http://d.old.wanfangdata.com.cn/NSTLHY/NSTL_HYCC0214793771/
    [26]
    H. S. Ahn, K. L. Moore, and Y. Q. Chen, "Discrete-time intermittent iterative learning controller with independent data dropouts, " IFAC Proceedings Volumes, vol. 41, no. 2, pp. 12442-12447, 2008. doi: 10.3182/20080706-5-KR-1001.02106
    [27]
    D. Wang, "Intelligent critic control with robustness guarantee of disturbed nonlinear plants, " IEEE Trans. Cybernetics, 2019, DOI: 10.1109/ TCYB.2019.2903117.
    [28]
    D. Wang and D. Liu, "Learning and guaranteed cost control with event-based adaptive critic implementation, " IEEE Trans. Neural Networks and Learning Systems, vol. 29, no. 12, pp. 6004-6014, Dec. 2018.
    [29]
    S. N. Huang, K. K. Tan, and T. H. Lee, "Necessary and sufficient condition for convergence of iterative learning algorithm, " Automatica, vol. 38, no. 7, pp. 1257-1260, 2002. doi: 10.1016/S0005-1098(02)00014-6
    [30]
    L. Li, Y. Liu, Z. Yang, X. Yang, and K. Li, "Mean-square error constrained approach to robust stochastic iterative learning control, " IET Control Theory & Applications, vol. 12, no. 1, pp. 38-44, 2018. http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=edb0c9c9f874a9377fb819ff01e37480
    [31]
    W. Zhou, M. Yu, and D. Huang, "A high-order internal model based iterative learning control scheme for discrete linear time-varying systems, " Int. J. Autom. Comput., vol. 12, no. 3, pp. 330-336, 2015. doi: 10.1007/s11633-015-0886-x

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

    Figures(6)  / Tables(2)

    Article Metrics

    Article views (1380) PDF downloads(62) Cited by()

    Highlights

    • The paper provides a Kalman filtering-based approach to solve iterative learning control problem through random fading channels
    • Fading channels at both output and input sides are taken into account, modeled by a general multiplicative randomness form.
    • The learning gain matrix is recursively computed by minimizing the trace of input error covariance, which varies in both time and iteration domains.
    • The asymptotic convergence of the input sequence to the desired value is strictly proved in the mean-square sense.

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return
    Return