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Volume 11 Issue 1
Jan.  2024

IEEE/CAA Journal of Automatica Sinica

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Z. Zhang, H. Jiang, D. Shen, and S. Saab, “Data-driven learning control algorithms for unachievable tracking problems,” IEEE/CAA J. Autom. Sinica, vol. 11, no. 1, pp. 205–218, Jan. 2024. doi: 10.1109/JAS.2023.123756
Citation: Z. Zhang, H. Jiang, D. Shen, and S. Saab, “Data-driven learning control algorithms for unachievable tracking problems,” IEEE/CAA J. Autom. Sinica, vol. 11, no. 1, pp. 205–218, Jan. 2024. doi: 10.1109/JAS.2023.123756

Data-Driven Learning Control Algorithms for Unachievable Tracking Problems

doi: 10.1109/JAS.2023.123756
Funds:  This work was supported by the National Natural Science Foundation of China (62173333, 12271522), Beijing Natural Science Foundation (Z210002), and the Research Fund of Renmin University of China (2021030187)
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  • For unachievable tracking problems, where the system output cannot precisely track a given reference, achieving the best possible approximation for the reference trajectory becomes the objective. This study aims to investigate solutions using the P-type learning control scheme. Initially, we demonstrate the necessity of gradient information for achieving the best approximation. Subsequently, we propose an input-output-driven learning gain design to handle the imprecise gradients of a class of uncertain systems. However, it is discovered that the desired performance may not be attainable when faced with incomplete information. To address this issue, an extended iterative learning control scheme is introduced. In this scheme, the tracking errors are modified through output data sampling, which incorporates low-memory footprints and offers flexibility in learning gain design. The input sequence is shown to converge towards the desired input, resulting in an output that is closest to the given reference in the least square sense. Numerical simulations are provided to validate the theoretical findings.

     

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  • [1]
    D. A. Bristow, M. Tharayil, and A. G. Alleyne, “A survey of iterative learning control,” IEEE Control Systems, vol. 26, no. 3, pp. 96–114, 2006. doi: 10.1109/MCS.2006.1636313
    [2]
    S. R. Nekoo, J. Á. Acosta, G. Heredia, and A. Ollero, “A PD-type state-dependent riccati equation with iterative learning augmentation for mechanical systems,” IEEE/CAA J. Autom. Sinica, vol. 9, no. 8, pp. 1499–1511, 2022. doi: 10.1109/JAS.2022.105533
    [3]
    D. Shen and C. Zhang, “Zero-error tracking control under unified quantized iterative learning framework via encodingdecoding method,” IEEE Trans. Cybernetics, vol. 52, no. 4, pp. 1979–1991, 2022. doi: 10.1109/TCYB.2020.3004187
    [4]
    D. Shen, N. Huo, and S. S. Saab, “A probabilistically quantized learning control framework for networked linear systems,” IEEE Trans. Neural Networks and Learning Systems, vol. 33, no. 12, pp. 7559–7573, 2022. doi: 10.1109/TNNLS.2021.3085559
    [5]
    D. Shen and Y. Xu, “Iterative learning control for discrete-time stochastic systems with quantized information,” IEEE/CAA J. Autom. Sinica, vol. 3, no. 1, pp. 59–67, 2016. doi: 10.1109/JAS.2016.7373763
    [6]
    S. S. Saab, “Stochastic P-type/D-type iterative learning control algorithms,” Int. Journal of Control, vol. 76, no. 2, pp. 139–148, 2003. doi: 10.1080/0020717031000077717
    [7]
    X. Dai, S. Tian, Y. Peng, and W. 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, 2014. doi: 10.1109/JAS.2014.7004684
    [8]
    D. Meng and K. L. Moore, “Robust iterative learning control for nonrepetitive uncertain systems,” IEEE Trans. Automatic Control, vol. 62, no. 2, pp. 907–913, 2017. doi: 10.1109/TAC.2016.2560961
    [9]
    C. Liu and X. Ruan, “Input-output-driven gain-adaptive iterative learning control for linear discrete-time-invariant systems,” Int. J. Robust and Nonlinear Control, vol. 31, no. 17, pp. 8551–8568, 2021. doi: 10.1002/rnc.5753
    [10]
    R. Chi, H. Zhang, B. Huang, and Z. Hou, “Quantitative data-driven adaptive iterative learning control: From trajectory tracking to point-to-point tracking,” IEEE Trans. Cybernetics, vol. 52, no. 6, pp. 4859–4873, 2022. doi: 10.1109/TCYB.2020.3015233
    [11]
    S. He, W. Chen, D. Li, Y. Xi, Y. Xu, and P. Zheng, “Iterative learning control with data-driven-based compensation,” IEEE Trans. Cybernetics, vol. 52, no. 8, pp. 7492–7503, 2022. doi: 10.1109/TCYB.2020.3041705
    [12]
    C. Hu, R. Zhou, Z. Wang, Y. Zhu, and M. Tomizuka, “Real-time iterative compensation framework for precision mechatronic motion control systems,” IEEE/CAA J. Autom. Sinica, vol. 9, no. 7, pp. 1218–1232, 2022. doi: 10.1109/JAS.2022.105689
    [13]
    X. Bu, Z. Hou, S. Jin, and R. Chi, “An iterative learning control design approach for networked control systems with data dropouts,” Int. J. Robust and Nonlinear Control, vol. 26, no. 1, pp. 91–109, 2016. doi: 10.1002/rnc.3300
    [14]
    J. Chen, C. Hua, and X. Guan, “Iterative learning model-free control for networked systems with dual-direction data dropouts and actuator faults,” IEEE Trans. Neural Networks and Learning Systems, vol. 32, no. 11, pp. 5232–5240, 2021. doi: 10.1109/TNNLS.2020.3027651
    [15]
    H.-S. Ahn, K. L. Moore, and Y. Chen, “Stability of discrete-time iterative learning control with random data dropouts and delayed controlled signals in networked control systems,” in Proc. 10th Int. Conf. Control, Automation, Robotics and Vision, 2008, pp. 757–762.
    [16]
    J. Liu and X. Ruan, “Networked iterative learning control design for discrete-time systems with stochastic communication delay in input and output channels,” Int. J. Systems Science, vol. 48, no. 9, pp. 1844–1855, 2017. doi: 10.1080/00207721.2017.1289567
    [17]
    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
    [18]
    D. Shen and S. S. Saab, “Noisy-output-based direct learning tracking control with Markov nonuniform trial lengths using adaptive gains,” IEEE Trans. Automatic Control, vol. 67, no. 8, pp. 4123–4130, 2022. doi: 10.1109/TAC.2021.3106860
    [19]
    X. Li, K. Wang, and D. Liu, “An improved result of multiple model iterative learning control,” IEEE/CAA J. Autom. Sinica, vol. 1, no. 3, pp. 315–322, 2014. doi: 10.1109/JAS.2014.7004689
    [20]
    D. Shen, G. Qu, and X. Yu, “Averaging techniques for balancing learning and tracking abilities over fading channels,” IEEE Trans. Automatic Control, vol. 66, no. 6, pp. 2636–2651, 2021. doi: 10.1109/TAC.2020.3011329
    [21]
    S. Zhu, X. Wang, and H. Liu, “Observer-based iterative and repetitive learning control for a class of nonlinear systems,” IEEE/CAA J. Autom. Sinica, vol. 5, no. 5, pp. 990–998, 2018. doi: 10.1109/JAS.2017.7510463
    [22]
    G. Qu and D. Shen, “Stochastic iterative learning control with faded signals,” IEEE/CAA J. Autom. Sinica, vol. 6, no. 5, pp. 1196–1208, 2019. doi: 10.1109/JAS.2019.1911696
    [23]
    D. Shen and X. Yu, “Learning tracking over unknown fading channels based on iterative estimation,” IEEE Trans. Neural Networks and Learning Systems, vol. 33, no. 1, pp. 48–60, 2022. doi: 10.1109/TNNLS.2020.3027475
    [24]
    Y. Chen, C. Wen, Z. Gong, and M. Sun, “An iterative learning controller with initial state learning,” IEEE Trans. Automatic Control, vol. 44, no. 2, pp. 371–376, 1999. doi: 10.1109/9.746269
    [25]
    Y. Hui, R. Chi, B. Huang, and Z. Hou, “Extended state observer-based data-driven iterative learning control for permanent magnet linear motor with initial shifts and disturbances,” IEEE Trans. Systems,Man,and Cybernetics: Systems, vol. 51, no. 3, pp. 1881–1891, 2021. doi: 10.1109/TSMC.2019.2907379
    [26]
    X. He, Z. Sun, Z. Geng, and A. Robertsson, “Exponential set-point stabilization of underactuated vehicles moving in three-dimensional space,” IEEE/CAA J. Autom. Sinica, vol. 9, no. 2, pp. 270–282, 2022. doi: 10.1109/JAS.2021.1004323
    [27]
    D. Shen, C. Liu, L. Wang, and X. Yu, “Iterative learning tracking for multisensor systems: A weighted optimization approach,” IEEE Trans. Cybernetics, vol. 51, no. 3, pp. 1286–1299, 2021. doi: 10.1109/TCYB.2019.2942105
    [28]
    Z. Zhang, H. Jiang, and D. Shen, “Extended iterative learning control for inconsistent tracking problems with random dropouts,” in Proc. IEEE 11th Data Driven Control and Learning Systems Conf., 2022, pp. 935–940.
    [29]
    R. Chi, Z. Hou, B. Huang, and S. Jin, “A unified data-driven design framework of optimality-based generalized iterative learning control,” Computers &Chemical Engineering, vol. 77, pp. 10–23, 2015.
    [30]
    L. Ma, X. Liu, X. Kong, and K. Y. Lee, “Iterative learning model predictive control based on iterative data-driven modeling,” IEEE Trans. Neural Networks and Learning Systems, vol. 32, no. 8, pp. 3377–3390, 2021. doi: 10.1109/TNNLS.2020.3016295
    [31]
    S. S. Saab, D. Shen, M. Orabi, D. Kors, and R. H. Jaafar, “Iterative learning control: Practical implementation and automation,” IEEE Trans. Industrial Electronics, vol. 69, no. 2, pp. 1858–1866, 2022. doi: 10.1109/TIE.2021.3063866
    [32]
    R. A. Horn and C. R. Johnson, Matrix Analysis. Cambridge University Press, 1985.
    [33]
    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
    [34]
    H.-S. Ahn, K. Moore, and Y. Chen, “Monotonic convergent iterative learning controller design based on interval model conversion,” IEEE Trans. Automatic Control, vol. 51, no. 2, pp. 366–371, 2006. doi: 10.1109/TAC.2005.863498
    [35]
    H.-S. Ahn, K. L. Moore, and Y. Chen, “Stability analysis of discrete-time iterative learning control systems with interval uncertainty,” Automatica, vol. 43, no. 5, pp. 892–902, 2007. doi: 10.1016/j.automatica.2006.11.020
    [36]
    J. H. Lee, K. S. Lee, and W. C. Kim, “Model-based iterative learning control with a quadratic criterion for time-varying linear systems,” Automatica, vol. 36, no. 5, pp. 641–657, 2000. doi: 10.1016/S0005-1098(99)00194-6

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    Highlights

    • characterize the concept of the unachievable tracking problem
    • demonstrate the necessity of gradient information in achieving the control objective
    • Introduce a design for an input-output-driven learning gain matrix
    • Identify the gradient drift problem arising from incomplete data
    • Propose an extended ILC scheme incorporating an error compensation mechanism

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