Robust data-driven control for set-point tracking of linear discrete-time systems with time delays

Chengyan Wang , Xingren Chen , Shi Li

Electronic Research Archive ›› 2026, Vol. 34 ›› Issue (9) : 6432 -6447.

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Electronic Research Archive ›› 2026, Vol. 34 ›› Issue (9) :6432 -6447. DOI: 10.3934/era.2026281
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Robust data-driven control for set-point tracking of linear discrete-time systems with time delays
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Abstract

This paper focused on the direct data-driven set-point tracking control problem of linear discrete-time systems with input time-delay. First, a tracking error was introduced by constructing an extended state vector through state reconstruction to generate a data-driven model. Second, a data-based tracking controller was designed with the objective of minimizing the L2 gain from disturbances to the performance output through H∞ control, thereby reducing the impact of input delay and disturbance and ensuring asymptotic tracking stability. Finally, to validate the effectiveness and practicality of the proposed method, this paper took an autonomous direct current (DC) micro-grid with two distributed generation units as the research object. Through simulation results, the effectiveness of the data-driven method in addressing set-point tracking control issues for time-delay systems was demonstrated.

Keywords

data-driven control / set-point tracking / input time-delay / H∞ control

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Chengyan Wang, Xingren Chen, Shi Li. Robust data-driven control for set-point tracking of linear discrete-time systems with time delays. Electronic Research Archive, 2026, 34 (9) : 6432-6447 DOI:10.3934/era.2026281

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References

[1]

Z. S. Hou, Z. Wang, From model-based control to data-driven control: Survey, classification and perspective, Inf. Sci., 235 (2013), 3-35. https://doi.org/10.1016/j.ins.2012.07.014

[2]

Z. S. Hou, S. T. Jin, Data-driven model-free adaptive control for a class of MIMO nonlinear discrete-time systems, IEEE Trans. Neural Networks, 22 (2011), 2173-2188. https://doi.org/10.1109/TNN.2011.2176141

[3]

Y. Wang, K. Zhao, J. L. G. Guirao, K. Pan, H. Chen, Online intelligent maneuvering penetration methods of missile with respect to unknown intercepting strategies based on reinforcement learning, Electron. Res. Arch., 30 (2022), 4366-4381. https://doi.org/10.3934/era.2022221

[4]

L. Zhang, Vibration analysis and multi-state feedback control of maglev vehicle-guideway coupling system, Electron. Res. Arch., 30 (2022), 3887-3901. https://doi.org/10.3934/era.2022198

[5]

J. Berberich, J. Köhler, M. A. Müller, F. Allgöwer, Data-driven model predictive control with stability and robustness guarantees, IEEE Trans. Autom. Control, 66 (2021), 1702-1717. https://doi.org/10.1109/TAC.2020.3000182

[6]

P. Yu, S. Tan, J. Guo, Y. Song, Data-driven optimal controller design for sub-satellite deployment of tethered satellite system, Electron. Res. Arch., 32 (2024), 505-522. https://doi.org/10.3934/era.2024025

[7]

L. Huang, J. Zhen, J. Lygeros, F. Dörfler, Robust data-enabled predictive control: Tractable formulations and performance guarantees, IEEE Trans. Autom. Control, 68 (2023), 3163-3170. https://doi.org/10.1109/TAC.2023.3241282

[8]

M. Wang, J. Qiu, H. Yan, Y. Tian, Z. Li, Data-driven control for discrete-time piecewise affine systems, Automatica, 155 (2023), 111168. https://doi.org/10.1016/j.automatica.2023.111168

[9]

J. G. Rueda-Escobedo, E. Fridman, J. Schiffer, Data-driven control for linear discrete-time delay systems, IEEE Trans. Autom. Control, 67 (2022), 3321-3336. https://doi.org/10.1109/TAC.2021.3096896

[10]

X. Y. Wang, G. D. Wang, S. H. Li, Distributed finite-time optimization for disturbed second-order multiagent systems, IEEE Trans. Cybern., 51 (2021), 4634-4647. https://doi.org/10.1109/TCYB.2020.2988490

[11]

H. Modares, F. L. Lewis, Z. P. Jiang, H∞ tracking control of completely unknown continuous-time systems via off-policy reinforcement learning , IEEE Trans. Neural Networks Learn. Syst., 26 (2015), 2550-2562. https://doi.org/10.1109/TNNLS.2015.2441749

[12]

M. Rotulo, C. De Persis, P. Tesi, Online learning of data-driven controllers for unknown switched linear systems, Automatica, 145 (2022), 110519. https://doi.org/10.1016/j.automatica.2022.110519

[13]

T. Hu, X. Xu, T. M. Nguyen, F. Liu, S. Yuan, L. Xie, Tire wear aware trajectory tracking control for multi-axle swerve-drive autonomous mobile robots, J. Autom. Intell., 4 (2025), 243-253. https://doi.org/10.1016/j.jai.2025.05.003

[14]

J. X. Zhang, T. Chai, Fault-tolerant control of wheeled mobile robots with prescribed trajectory tracking performance, J. Autom. Intell., 4 (2025), 73-81. https://doi.org/10.1016/j.jai.2025.03.003

[15]

A. S. Bazanella, L. Campestrini, D. Eckhard, The data-driven approach to classical control theory, Annu. Rev. Control, 56 (2023), 100906. https://doi.org/10.1016/j.arcontrol.2023.100906

[16]

K. J. Åström, P. R. Kumar, Control: A perspective, Automatica, 50 (2014), 3-43. https://doi.org/10.1016/j.automatica.2013.10.012

[17]

Z. Bubnicki, Modern Control Theory, Springer Science & Business Media, Berlin, 2005. https://doi.org/10.1007/3-540-28087-1

[18]

P. J. Antsaklis, A. N. Michel, Linear Systems, Birkhäuser, Boston, 2006. https://doi.org/10.1007/0-8176-4435-0

[19]

Y. Xu, L. Zhou, J. Zhao, L. Ma, C. Yang, Direct data-driven optimal set-point tracking control of linear discrete-time systems, IEEE Trans. Circuits Syst. II Express Briefs, 71 (2024), 3795-3799. https://doi.org/10.1109/TCSII.2024.3368271

[20]

F. Yang, J. He, D. Wang, New stability criteria of delayed load frequency control systems via infinite-series-based inequality, IEEE Trans. Ind. Inf., 14 (2018), 231-240. https://doi.org/10.1109/TII.2017.2751510

[21]

Z. Jia, X. Li, Q. Cui, L. Li, Asymptotical stability of stochastic delayed time-varying systems with stochastic delayed impulses, Int. J. Syst. Sci., 56 (2025), 1662-1674. https://doi.org/10.1080/00207721.2024.2429024

[22]

Y. Liu, H. Zhang, R. Yu, Q. Qu, Data-driven optimal tracking control for discrete-time systems with delays using adaptive dynamic programming, J. Franklin Inst., 355 (2018), 5649-5666. https://doi.org/10.1016/j.jfranklin.2018.06.013

[23]

L. Cui, B. Pang, M. Krstić, Z. Jiang, Learning-based adaptive optimal control of linear time-delay systems: A value iteration approach, Automatica, 171 (2025), 111944. https://doi.org/10.1016/j.automatica.2024.111944

[24]

K. Gu, V. L. Kharitonov, J. Chen, Introduction to Time-Delay Systems: Analysis and Control, Springer, Boston, 2014.

[25]

C. De Persis, P. Tesi, Formulas for data-driven control: Stabilization, optimality, and robustness, IEEE Trans. Automat. Control, 65 (2020), 909-924. https://doi.org/10.1109/TAC.2019.2959924

[26]

S. Derakhshan, M. Shafiee-Rad, Q. Shafiee, M. R. Jahed-Motlagh, S. Sahoo, F. Blaabjerg, Decentralized voltage control of autonomous dc microgrids with robust performance approach, IEEE J. Emerging Sel. Top. Power Electron., 9 (2021), 5508-5520. https://doi.org/10.1109/JESTPE.2021.3054723

[27]

V. Venkatasubramanian, H. Schattler, J. Zaborszky, Fast time-varying phasor analysis in the balanced three-phase large electric power system, IEEE Trans. Autom. Control, 40 (1995), 1975-1982. https://doi.org/10.1109/9.471228

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