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Volume 7 Issue 3
Apr.  2020

IEEE/CAA Journal of Automatica Sinica

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Jianxiang Zhang, Baotong Cui, Xisheng Dai and Zhengxian Jiang, "Iterative Learning Control for Distributed Parameter Systems Based on Non-Collocated Sensors and Actuators," IEEE/CAA J. Autom. Sinica, vol. 7, no. 3, pp. 865-871, May 2020. doi: 10.1109/JAS.2019.1911663
Citation: Jianxiang Zhang, Baotong Cui, Xisheng Dai and Zhengxian Jiang, "Iterative Learning Control for Distributed Parameter Systems Based on Non-Collocated Sensors and Actuators," IEEE/CAA J. Autom. Sinica, vol. 7, no. 3, pp. 865-871, May 2020. doi: 10.1109/JAS.2019.1911663

Iterative Learning Control for Distributed Parameter Systems Based on Non-Collocated Sensors and Actuators

doi: 10.1109/JAS.2019.1911663
Funds:  This work was supported by National Natural Science Foundation of China (61807016) and Postgraduate Research and Practice Innovation Program of Jiangsu Province (KYCX18-1859)
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  • In this paper, an open-loop PD-type iterative learning control (ILC) scheme is first proposed for two kinds of distributed parameter systems (DPSs) which are described by parabolic partial differential equations using non-collocated sensors and actuators. Then, a closed-loop PD-type ILC algorithm is extended to a class of distributed parameter systems with a non-collocated single sensor and m actuators when the initial states of the system exist some errors. Under some given assumptions, the convergence conditions of output errors for the systems can be obtained. Finally, one numerical example for a distributed parameter system with a single sensor and two actuators is presented to illustrate the effectiveness of the proposed ILC schemes.

     

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  • [1]
    R. Padhi and S. N. Balakrishnan, “Optimal dynamic inversion control design for a class of nonlinear distributed parameter systems with continuous and discrete actuators,” IET Contr. Theory Appl., vol. 1, no. 6, pp. 1662–1671, Dec. 2007. doi: 10.1049/iet-cta:20060343
    [2]
    H. X. Li and C. Qi, “Modeling of distributed parameter systems for applications-a synthesized review from time-space separation,” J. Process Control, vol. 20, no. 8, pp. 891–901, Sept. 2010. doi: 10.1016/j.jprocont.2010.06.016
    [3]
    M. Wang and H. Shi, “An adaptive neural network prediction for nonlinear parabolic distributed parameter system based on block-wise moving window technique,” Neurocomputing, vol. 133, no. 10, pp. 67–73, Jun. 2014.
    [4]
    M. A. Demetriou, “Guidance of a moving collocated actuator/sensor for improved control of distributed parameter systems,” in Proc. 47th IEEE Conf. Decis. Control, pp. 215–220, Dec. 2008.
    [5]
    M. A. Demetriou, “Gain adaptation and sensor guidance of diffusion PDEs using on-line approximation of optimal feedback kernels,” in Proc. American Control Conf, pp. 2536–2541, Jul. 2016.
    [6]
    W. Y. Mu, B. T. Cui, W. Li, and Z. X. Jiang, “Improving control and estimation for distributed parameter systems utilizing mobile actuatorsensor network,” ISA Trans., vol. 53, no. 4, pp. 1087–1095, Jul. 2014. doi: 10.1016/j.isatra.2014.05.004
    [7]
    Z. X. Jiang, B. T. Cui, W. Wu, and B. Zhuang, “Event-driven observerbased control for distributed parameter systems using mobile sensor and actuator,” Computer and Mathematics With Applications, vol. 72, no. 12, pp. 2854–2864, Dec. 2016. doi: 10.1016/j.camwa.2016.10.009
    [8]
    S. Arimoto, S. Kawamura, and F. Miyazaki, “Bettering operation of robots by learning,” J. Robotic System, vol. 1, no. 2, pp. 123–140, Jun. 1984. doi: 10.1002/(ISSN)1097-4563
    [9]
    N. Zeng and X. Ying, “Iterative learning control algorithm for nonlinear dynamical systems,” Acta Automatica Sinica, vol. 18, no. 2, pp. 168–176, Mar. 1992.
    [10]
    M. X. Sun and B. J. Huang, Iterative Learning Control, Beijing: National Defence Industrial Press, 1999.
    [11]
    F. X. Piao, Q. L. Zhang, and Z. F. Wang, “Iterative learning control for a class of singular systems,” Acta Automatica Sinica, vol. 33, no. 6, pp. 658–659, 2007.
    [12]
    R. H. Chi and Z. S. Hou, “Dual-stage optimal iterative learning control for nonlinear non-affine discrete-time system,” Acta Automatica Sinica, vol. 32, no. 10, pp. 1061–1065, Oct. 2007.
    [13]
    D. Shen, Y. Mu, and G. Xiong, “Iterative learning control for non-linear systems with deadzone input and time delay in presence of measurement noise,” IET Contr. Theory Appl., vol. 5, no. 12, pp. 1418–1425, Aug. 2011. doi: 10.1049/iet-cta.2010.0465
    [14]
    X. H. Bu, T. H. Wang, Z. S. Hou, and R. H. Chi, “Iterative learning control for discrete-time systems with quantised measurements,” IET Contr. Theory Appl., vol. 9, no. 9, pp. 1455–1460, Jul. 2015. doi: 10.1049/iet-cta.2014.1056
    [15]
    W. Paszke, E. Rogers, K. Galkowski, and Z. Cai, “Robust finite frequency range iterative learning control design and experimental verification,” Control Eng. Practice, vol. 21, no. 10, pp. 1310–1320, Oct. 2013. doi: 10.1016/j.conengprac.2013.05.011
    [16]
    C. T. Freeman, E. Rogers, A. Hughes, J. H. Burridge, and K. L. Meadmore, “Iterative learning control in health care: electrical stimulation and robotic-assisted upper-limb stroke rehabilitation,” IEEE Control Syst. Mag., vol. 32, no. 1, pp. 18–43, Feb. 2012. doi: 10.1109/MCS.2011.2173261
    [17]
    J. H. Lee and K. S. Lee, “Iterative learning control applied to batch processes: an overview,” Control Eng. Practice, vol. 15, no. 10, pp. 1306–1318, Oct. 2007. doi: 10.1016/j.conengprac.2006.11.013
    [18]
    X. E. Ruan, Z. Z. Bien, and K. H. Park, “Decentralized iterative learning control to large-scale industrial processes for nonrepetitive trajectory tracking,” IEEE Trans. Systems,Man,and Cybernetics:Systems, vol. 38, no. 1, pp. 238–252, Feb. 2008. doi: 10.1109/TSMCA.2007.909549
    [19]
    H. H. Ji, Z. S. Hou, L. L. Fan, and F. L. Lewis, “Adaptive iterative learning reliable control for a class of non-linearly parameterised systems with unknown state delays and input saturation,” IET Contr. Theory Appl., vol. 10, no. 17, pp. 2160–2174, Jun. 2016. doi: 10.1049/iet-cta.2016.0209
    [20]
    D. Q. Huang, X. F. Li, J. X. Xu, C. Xu, and W. He, “Iterative learning control of inhomogeneous distributed parameter systems-frequency domain design and analysis,” System &Control Letters, vol. 72, pp. 22–29, Oct. 2014.
    [21]
    T. F. Xiao and H. X. Li, “Eigenspectrum-based iterative learning control for a class of distributed parameter systems,” IEEE Trans. Automatic Control, vol. 62, no. 2, pp. 824–836, Jan. 2016.
    [22]
    X. S. Dai, S. P. Tian, Y. J. Peng, and W. G. Luo, “Closed-loop P-type iterative learning control of uncertain linear distributed parameter systems,” IEEE/CAA,J. Autom. Sinica, vol. 1, no. 3, pp. 267–273, Jul. 2014. doi: 10.1109/JAS.2014.7004684
    [23]
    X. S. Dai, C. Xu, and S. P. Tian, “Iterative learning control for MIMO second-order hyperbolic distributed parameter systems with uncertainties,” Adv. Differ. Equat, vol. 1, no. 94, Dec. 2016.
    [24]
    J. X. Zhang, B. T. Cui, and X. Y. Lou, “Iterative learning control for distributed parameter systems based on actuator-sensor network,” in Proc.7th Int. Conf. Info. Science and Tech, pp. 14–18, Apr. 2017.

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    Highlights

    • We first propose the open-loop PD-type ILC scheme for a class of parabolic distributed parameter system with non-collocated sensors and actuators.
    • Then, we present a closed-loop PD-type ILC algorithm for the distributed parameter system using single sensor and multiple actuators when some errors are identified in the initial states of the system.
    • This study enhances the performance of parabolic distributed parameter system using non-collocated sensors and actuators.
    • The simulation results demonstrate the effectiveness of the proposed schemes.

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