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Volume 13 Issue 9
Sep.  2026

IEEE/CAA Journal of Automatica Sinica

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Article Navigation >  IEEE/CAA Journal of Automatica Sinica  > 2026  >  13(9): 2099-2108
Z. Li and F. Hao, “Necessary and/or sufficient conditions of controllability of networked sampled-data systems with nonequidistant sampling,” IEEE/CAA J. Autom. Sinica, vol. 13, no. 9, pp. 2099–2108, Sep. 2026. doi: 10.1109/JAS.2026.125906
Citation: Z. Li and F. Hao, “Necessary and/or sufficient conditions of controllability of networked sampled-data systems with nonequidistant sampling,” IEEE/CAA J. Autom. Sinica, vol. 13, no. 9, pp. 2099–2108, Sep. 2026. doi: 10.1109/JAS.2026.125906
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Necessary and/or Sufficient Conditions of Controllability of Networked Sampled-Data Systems With Nonequidistant Sampling

doi: 10.1109/JAS.2026.125906
Funds:  This work was supported by the National Natural Science Foundation of China (62473019, 62227810)
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    Abstract
  • The assumption of equidistant sampling is usually not valid in many networked systems. Controllability is the prerequisite for analyzing networked systems. Then, in this article, the controllability of networked systems with nonequidistant sampling is investigated. Some necessary and/or sufficient conditions for verifying the controllability of networked systems with non-uniform sampling are presented. It is found that the network structure, inner-coupling, and node system collectively determine the controllability of the whole networked sampled-data systems. The networked systems with special structures are more easily controlled after sampling. Besides, if the nodes are one-dimensional, it is demonstrated that the controllability can be preserved after nonequidistant sampling in this paper. The feasibility and simplicity of these criteria are verified by some numerical examples.

     

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    • References(41)
  • [1]
    R. E. Kalman, “On the general theory of control systems,” IFAC Proc. Volumes, vol. 1, no. 1, pp. 491−502, Aug. 1960.
    [2]
    R. E. Kalman, “Contributions to the theory of optimal control,” Bol. Soc. Mat. Mex., vol. 5, no. 2, pp. 102−119, 1960.
    [3]
    R. E. Kalman, “Mathematical description of linear dynamical systems,” J. Soc. Ind. Appl. Math. Ser. A Control, vol. 1, no. 2, pp. 152−192, 1963. doi: 10.1137/0301010
    [4]
    M. L. J. Hautus, “Controllability and observability conditions of linear autonomous systems,” Indagationes Math., vol. 72, no. 5, pp. 443−448, 1969.
    [5]
    Z. Lu, Z. Ji, and Z. Zhang, “Sampled-data based structural controllability of multi-agent systems with switching topology,” J. Franklin Inst., vol. 357, no. 15, pp. 10886−10899, Oct. 2020.
    [6]
    B. Zhao, “Data-sampling based controllability analysis and optimal control of linear multi-agent systems,” J. Syst. Sci. Complex., vol. 37, no. 6, pp. 2614−2632, Dec. 2024. doi: 10.1007/s11424-024-3295-9
    [7]
    B. Gao, Z. Ji, Y. Liu, and C. Lin, “Sampling controllability of multi-agent systems based on structural decomposition,” Syst. Control Lett., vol. 191, Art. no. 105882, Sep. 2024.
    [8]
    G. Kreisselmeier, “On sampling without loss of observability/controllability,” IEEE Trans. Autom. Control, vol. 44, no. 5, pp. 1021−1025, May 1999.
    [9]
    G. Guo, “Systems with nonequidistant sampling: Controllable? Observable? Stable?” Asian J. Control, vol. 7, no. 4, pp. 455−461, Dec. 2005.
    [10]
    F. Ding, L. Qiu, and T. Chen, “Reconstruction of continuous-time systems from their non-uniformly sampled discrete-time systems,” Automatica, vol. 45, no. 2, pp. 324−332, Feb. 2009.
    [11]
    L. Wang, Z. Li, G. Zhao, G. Guo, and Z. Kong, “Input structure design for structural controllability of complex networks,” IEEE/CAA J. Autom. Sinica, vol. 10, no. 7, pp. 1571−1581, Jul. 2023.
    [12]
    D. Yang, Y. Sun, Q. Wei, H. Zhang, and T. Li, “Topology prediction and structural controllability analysis of complex networks without connection information,” IEEE Trans. Syst. Man Cybern. Syst., vol. 52, no. 9, pp. 6015−6027, Sep. 2022.
    [13]
    L. Wang, Z. Li, L. Cao, G. Guo, and Z. Kong, “Controllability of multi-relational networks with heterogeneous dynamical nodes,” IEEE/CAA J. Autom. Sinica, vol. 11, no. 12, pp. 2476−2486, Dec. 2024.
    [14]
    Q. Cai, S. Alam, M. Pratama, and J. Liu, “Robustness evaluation of multipartite complex networks based on percolation theory,” IEEE Trans. Syst. Man Cybern. Syst., vol. 51, no. 10, pp. 6244−6257, Oct. 2021.
    [15]
    S. Zhu, J. Zhou, J. Lü, and J. A. Lu, “Adaptive synchronization of complex dynamical networks: Dealing with uncertain impulses,” IEEE Trans. Autom. Control, vol. 69, no. 6, pp. 3997−4004, Jun. 2024.
    [16]
    X. Du, L. Zou, and M. Zhong, “Set-membership filtering approach to dynamic event-triggered fault estimation for a class of nonlinear time-varying complex networks,” IEEE/CAA J. Autom. Sinica, vol. 11, no. 3, pp. 638−648, Mar. 2024.
    [17]
    Y. Y. Liu, J. J. Slotine, and A. L. Barabási, “Controllability of complex networks,” Nature, vol. 473, no. 7346, pp. 167−173, May 2011.
    [18]
    C. Commault and J. van der Woude, “A classification of nodes for structural controllability,” IEEE Trans. Autom. Control, vol. 64, no. 9, pp. 3877−3882, Sep. 2019.
    [19]
    S. S. Mousavi, M. Haeri, and M. Mesbahi, “On the structural and strong structural controllability of undirected networks,” IEEE Trans. Autom. Control, vol. 63, no. 7, pp. 2234−2241, Jul. 2018.
    [20]
    S. S. Mousavi, M. Haeri, and M. Mesbahi, “Strong structural controllability of networks under time-invariant and time-varying topological perturbations,” IEEE Trans. Autom. Control, vol. 66, no. 3, pp. 1375−1382, Mar. 2021.
    [21]
    T. Bai, S. Li, Y. Zou, and X. Yin, “Block-based minimum input design for the structural controllability of complex networks,” Automatica, vol. 107, pp. 68−76, Sep. 2019.
    [22]
    L. Wang, G. Chen, X. Wang, and W. K. S. Tang, “Controllability of networked MIMO systems,” Automatica, vol. 69, pp. 405−409, Jul. 2016.
    [23]
    Y. Hao, Z. Duan, G. Chen, and F. Wu, “New controllability conditions for networked, identical LTI systems,” IEEE Trans. Autom. Control, vol. 64, no. 10, pp. 4223−4228, Oct. 2019.
    [24]
    L. Xiang, P. Wang, F. Chen, and G. Chen, “Controllability of directed networked MIMO systems with heterogeneous dynamics,” IEEE Trans. Control Network Syst., vol. 7, no. 2, pp. 807−817, Jun. 2020.
    [25]
    Z. Kong, L. Cao, L. Wang, and G. Guo, “Controllability of heterogeneous networked systems with nonidentical inner-coupling matrices,” IEEE Trans. Control Network Syst., vol. 9, no. 2, pp. 867−878, Jun. 2022.
    [26]
    Z. Li and F. Hao, “Necessary and sufficient conditions of controllability of networked SISO systems with nonidentical dimensional nodes,” J. Franklin Inst., vol. 362, no. 9, Art. no. 107715, Jun. 2025.
    [27]
    J.-N. Wu, X. Li, and G. Chen, “Controllability of deep-coupling dynamical networks,” IEEE Trans. Circuits Syst. I. Reg. Papers, vol. 67, no. 12, pp. 5211−5222, Dec. 2020.
    [28]
    J.-N. Wu, X. Li, and G. Chen, “Controllability of multilayer snapback networks,” IEEE Trans. Control Network Syst., vol. 10, no. 1, pp. 15−25, Mar. 2023.
    [29]
    A. Chapman, M. Nabi-Abdolyousefi, and M. Mesbahi, “Controllability and observability of network-of-networks via Cartesian products,” IEEE Trans. Autom. Control, vol. 59, no. 10, pp. 2668−2679, Oct. 2014.
    [30]
    Y. Hao, Q. Wang, Z. Duan, and G. Chen, “Controllability of Kronecker product networks,” Automatica, vol. 110, Art. no. 108597, Dec. 2019.
    [31]
    Z. Yang, X. Wang, and L. Wang, “Controllability of networked sampled-data systems,” IEEE Trans. Autom. Control, vol. 69, no. 8, pp. 5081−5093, Aug. 2024.
    [32]
    Z. Yang, X. Wang, and L. Wang, “Controllability of multilayer networked sampled-data systems,” IEEE Trans. Cybern., vol. 54, no. 8, pp. 4416−4428, Aug. 2024.
    [33]
    Z. Yang, L. Wang, X. Wang, and G. Chen, “Controllability of heterogeneous networked sampled-data systems,” Sci. China Inf. Sci., vol. 68, no. 3, Art. no. 132207, Feb. 2025.
    [34]
    G. Mustafa and T. Chen, “Stabilisation of non-uniformly sampled systems via dynamic output-feedback control,” IET Control Theory Appl., vol. 7, no. 2, pp. 228−233, Jan. 2013.
    [35]
    Muhammad, G. Mustafa, A. Q. Khan, and M. Abid, “On the observability of non-uniformly sampled systems,” in Proc. 12th Int. Conf. Frontiers of Information Technology, Islamabad, Pakistan, 2014, pp. 87−90.
    [36]
    T. Chen and B. A. Francis, Optimal Sampled-Data Control Systems, London, United Kingdom: Springer, 1995.
    [37]
    S. Roman, Advanced Linear Algebra, 2nd ed., New York: Springer, 2005.
    [38]
    J. P. Hespanha, Linear Systems Theory, 2nd ed., Princeton: Princeton Univ. Press, 2018.
    [39]
    J. Wang, D. Wang, H. Yan, and H. Shen, “Composite antidisturbance H∞ control for hidden Markov jump systems with multi-sensor against replay attacks,” IEEE Trans. Autom. Control, vol. 69, no. 3, pp. 1760−1766, Mar. 2024.
    [40]
    H. Shen, M. Xing, H. Yan, and J. Cao, “Observer-based l2−l∞ control for singularly perturbed semi-Markov jump systems with an improved weighted TOD protocol,” Sci. China Inf. Sci., vol. 65, no. 9, Art. no. 199204, Jun. 2022.
    [41]
    H. Shen, Y. Wang, J. Wu, J. H. Park, and J. Wang, “Secure control for Markov jump cyber-physical systems subject to malicious attacks: A resilient hybrid learning scheme,” IEEE Trans. Cybern., vol. 54, no. 11, pp. 7068−7079, Nov. 2024.
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