A journal of IEEE and CAA , publishes high-quality papers in English on original theoretical/experimental research and development in all areas of automation

IEEE/CAA Journal of Automatica Sinica

  • JCR Impact Factor: 15.3, Top 1 (SCI Q1)
    CiteScore: 23.5, Top 2% (Q1)
    Google Scholar h5-index: 77, TOP 5
Turn off MathJax
Article Contents
W. Qian, Y. Wu, and B. Shen, “Novel adaptive memory event-triggered-based fuzzy robust control for nonlinear networked systems via the differential evolution algorithm,” IEEE/CAA J. Autom. Sinica, vol. 11, no. 8, pp. 1836–1848, Aug. 2024. doi: 10.1109/JAS.2024.124419
Citation: W. Qian, Y. Wu, and B. Shen, “Novel adaptive memory event-triggered-based fuzzy robust control for nonlinear networked systems via the differential evolution algorithm,” IEEE/CAA J. Autom. Sinica, vol. 11, no. 8, pp. 1836–1848, Aug. 2024. doi: 10.1109/JAS.2024.124419

Novel Adaptive Memory Event-Triggered-Based Fuzzy Robust Control for Nonlinear Networked Systems via the Differential Evolution Algorithm

doi: 10.1109/JAS.2024.124419
Funds:  This work was partially supported by the National Natural Science Foundation of China (61973105, 62373137)
More Information
  • This article mainly investigates the fuzzy optimization robust control issue for nonlinear networked systems characterized by the interval type-2 (IT2) fuzzy technique under a differential evolution algorithm. To provide a more reasonable utilization of the constrained communication channel, a novel adaptive memory event-triggered (AMET) mechanism is developed, where two event-triggered thresholds can be dynamically adjusted in the light of the current system information and the transmitted historical data. Sufficient conditions with less conservative design of the fuzzy imperfect premise matching (IPM) controller are presented by introducing the Wirtinger-based integral inequality, the information of membership functions (MFs) and slack matrices. Subsequently, under the IPM policy, a new MFs intelligent optimization technique that takes advantage of the differential evolution algorithm is first provided for IT2 Takagi-Sugeno (T-S) fuzzy systems to update the fuzzy controller MFs in real-time and achieve a better system control effect. Finally, simulation results demonstrate that the proposed control scheme can obtain better system performance in the case of using fewer communication resources.

     

  • loading
  • [1]
    W. Qian, W. Xing, and S. Fei, “H state estimation for neural networks with general activation function and mixed time-varying delays,” IEEE Trans. Neural Networks and Learning Systems, vol. 32, no. 9, pp. 3909–3918, 2021. doi: 10.1109/TNNLS.2020.3016120
    [2]
    H. Geng, Z. Wang, Y. Chen, X. Yi, and Y. Cheng, “Variance-constrained filtering fusion for nonlinear cyber-physical systems with the denialof-service attacks and stochastic communication protocol,” IEEE/CAA J. Autom. Sinica, vol. 9, no. 6, pp. 978–989, 2022. doi: 10.1109/JAS.2022.105623
    [3]
    J. Zhang, C. Peng, X. Xie, and D. Yue, “Output feedback stabilization of networked control systems under a stochastic scheduling protocol,” IEEE Trans. Cybern., vol. 50, no. 6, pp. 2851–2860, 2020. doi: 10.1109/TCYB.2019.2894294
    [4]
    W. Qian, D. Lu, S. Guo, and Y. Zhao, “Distributed state estimation for mixed delays system over sensor networks with multichannel random attacks and Markov switching topology,” IEEE Trans. Neural Networks and Learning Systems, vol. 35, no. 6, pp. 8623–8637, 2024. doi: 10.1109/TNNLS.2022.3230978
    [5]
    C. Wu, W. Pan, R. Staa, J. Liu, G. Sun, and L. Wu, “Deep reinforcement learning control approach to mitigating actuator attacks,” Automatica, vol. 152, p. 110999, 2023. doi: 10.1016/j.automatica.2023.110999
    [6]
    P. Du, W. Zhong, X. Peng, L. Li, and Z. Li, “Self-healing control for wastewater treatment process based on variable-gain state observer,” IEEE Trans. Industrial Informatics, vol. 19, p. 10, 2023.
    [7]
    X. Wan, C. Zhang, F. Wei, C.-K. Zhang, and M. Wu, “Hybrid dynamic variables-dependent event-triggered fuzzy model predictive control,” IEEE/CAA J. Autom. Sinica, vol. 11, no. 3, pp. 723–733, 2024. doi: 10.1109/JAS.2023.123957
    [8]
    H. Ren, H. Ma, H. Li, and Z. Wang, “Adaptive fixed-time control of nonlinear MASs with actuator faults,” IEEE/CAA J. Autom. Sinica, vol. 10, no. 5, pp. 1252–1262, 2023. doi: 10.1109/JAS.2023.123558
    [9]
    L. Liu, C. Zhu, Y. Liu, R. Wang, and S. Tong, “Performance improvement of active suspension constrained system via neural network identification,” IEEE Trans. Neural Networks and Learning Systems, vol. 34, no. 10, pp. 7089–7098, 2023. doi: 10.1109/TNNLS.2021.3137883
    [10]
    H. Wang, K. Xu, and H. Zhang, “Adaptive finite-time tracking control of nonlinear systems with dynamics uncertainties,” IEEE Trans. Autom. Control, vol. 68, no. 9, pp. 5737–5744, 2023. doi: 10.1109/TAC.2022.3226703
    [11]
    P. Du, W. Zhong, X. Peng, L. Li, and Z. Li, “Data-driven fault compensation tracking control for coupled wastewater treatment process,” IEEE/CAA J. Autom. Sinica, vol. 10, no. 1, pp. 294–297, 2023. doi: 10.1109/JAS.2023.123054
    [12]
    Z. Zhang and J. Dong, “A new optimization control policy for fuzzy vehicle suspension systems under membership functions online learning,” IEEE Trans. Systems, Man, and Cybern.: Systems, vol. 53, no. 5, pp. 3255–3266, 2023. doi: 10.1109/TSMC.2022.3224739
    [13]
    W. Li, Z. Xie, Y. Cao, P. K. Wong, and J. Zhao, “Sampled-data asynchronous fuzzy output feedback control for active suspension systems in restricted frequency domain,” IEEE/CAA J. Autom. Sinica, vol. 8, no. 5, pp. 1052–1066, 2021. doi: 10.1109/JAS.2020.1003306
    [14]
    J. Yang, Q. Zhong, K. Shi, Y. Yu, and S. Zhong, “Stability and stabilization for T-S fuzzy load frequency control power system with energy storage system,” IEEE Trans. Fuzzy Systems, vol. 32, no. 3, pp. 893–905, 2024. doi: 10.1109/TFUZZ.2023.3311925
    [15]
    Y. Wang, B. Jiang, Z.-G. Wu, S. Xie, and Y. Peng, “Adaptive sliding mode fault-tolerant fuzzy tracking control with application to unmanned marine vehicles,” IEEE Trans. Systems, Man, and Cybern.: Systems, vol. 51, no. 11, pp. 6691–6700, 2021. doi: 10.1109/TSMC.2020.2964808
    [16]
    Y. Pan, Q. Li, H. Liang, and H.-K. Lam, “A novel mixed control approach for fuzzy systems via membership functions online learning policy,” IEEE Trans. Fuzzy Systems, vol. 30, no. 9, pp. 3812–3822, 2022. doi: 10.1109/TFUZZ.2021.3130201
    [17]
    Z. Zhang and J. Dong, “Robust output-feedback ${{H}}_{\infty} $ online optimization control for T-S fuzzy systems via differential evolution algorithm,” IEEE Trans. Fuzzy Systems, vol. 31, no. 11, pp. 4109–4120, 2023. doi: 10.1109/TFUZZ.2023.3267405
    [18]
    Z. Du, Y. Kao, H. R. Karimi, and X. Zhao, “Interval type-2 fuzzy sampled-data ${{H}}_{\infty} $ control for nonlinear unreliable networked control systems,” IEEE Trans. Fuzzy Systems, vol. 28, no. 7, pp. 1434–1448, 2020. doi: 10.1109/TFUZZ.2019.2911490
    [19]
    Z. Zhang and J. Dong, “Fault-tolerant containment control for IT2 fuzzy networked multiagent systems against denial-of-service attacks and actuator faults,” IEEE Trans. Systems, Man, and Cybern.: Systems, vol. 52, no. 4, pp. 2213–2224, 2022. doi: 10.1109/TSMC.2020.3048999
    [20]
    K. Zhu, Z. Wang, Y. Chen, and G. Wei, “Event-triggered cost-guaranteed control for linear repetitive processes under probabilistic constraints,” IEEE Trans. Autom. Control, vol. 68, no. 1, pp. 424–431, 2023. doi: 10.1109/TAC.2022.3140384
    [21]
    Y. Pan, Y. Wu, and H.-K. Lam, “Security-based fuzzy control for nonlinear networked control systems with DoS attacks via a resilient event-triggered scheme,” IEEE Trans. Fuzzy Systems, vol. 30, no. 10, pp. 4359–4368, 2022. doi: 10.1109/TFUZZ.2022.3148875
    [22]
    Y. Ju, D. Ding, X. He, Q.-L. Han, and G. Wei, “Consensus control of multi-agent systems using fault-estimation-in-the-loop: Dynamic eventtriggered case,” IEEE/CAA J. Autom. Sinica, vol. 9, no. 8, pp. 1440–1451, 2022. doi: 10.1109/JAS.2021.1004386
    [23]
    G. Lin, H. Li, C. K. Ahn, and D. Yao, “Event-based finite-time neural control for human-in-the-loop UAV attitude systems,” IEEE Trans. Neural Networks and Learning Systems, vol. 34, p. 12, 2023.
    [24]
    X.-L. Wang and G.-H. Yang, “Event-triggered H control for T-S fuzzy systems via new asynchronous premise reconstruction approach,” IEEE Trans. Cybern., vol. 51, no. 6, pp. 3062–3070, 2021. doi: 10.1109/TCYB.2019.2956736
    [25]
    B. Jiang, H. R. Karimi, Y. Kao, and C. Gao, “Takagi-Sugeno model based event-triggered fuzzy sliding-mode control of networked control systems with semi-Markovian switchings,” IEEE Trans. Fuzzy Systems, vol. 28, no. 4, pp. 673–683, 2020. doi: 10.1109/TFUZZ.2019.2914005
    [26]
    H. Ren, Z. Cheng, J. Qin, and R. Lu, “Deception attacks on eventtriggered distributed consensus estimation for nonlinear systems,” Automatica, vol. 154, p. 111100, 2023. doi: 10.1016/j.automatica.2023.111100
    [27]
    W. Qian, Y. Wu, and J. Yang, “Event-driven reduced-order fault detection filter design for nonlinear systems with complex communication channel,” IEEE Trans. Fuzzy Systems, vol. 32, no. 1, pp. 281–293, 2024. doi: 10.1109/TFUZZ.2023.3297718
    [28]
    Y. Tan, M. Xiong, B. Zhang, and S. Fei, “Adaptive event-triggered nonfragile state estimation for fractional-order complex networked systems with cyber attacks,” IEEE Trans. Systems, Man, and Cybern.: Systems, vol. 52, no. 4, pp. 2121–2133, 2022. doi: 10.1109/TSMC.2021.3049231
    [29]
    H. Li, Z. Zhang, H. Yan, and X. Xie, “Adaptive event-triggered fuzzy control for uncertain active suspension systems,” IEEE Trans. Cybern., vol. 49, no. 12, pp. 4388–4397, 2019. doi: 10.1109/TCYB.2018.2864776
    [30]
    L. Zhang, Y. Sun, H.-K. Lam, H. Li, J. Wang, and D. Hou, “Guaranteed cost control for interval type-2 fuzzy semi-Markov switching systems within a finite-time interval,” IEEE Trans. Fuzzy Systems, vol. 30, no. 7, pp. 2583–2594, 2022. doi: 10.1109/TFUZZ.2021.3089248
    [31]
    N. Zhao, P. Shi, W. Xing, and C. P. Lim, “Resilient adaptive eventtriggered fuzzy tracking control and filtering for nonlinear networked systems under denial-of-service attacks,” IEEE Trans. Fuzzy Systems, vol. 30, no. 8, pp. 3191–3201, 2022. doi: 10.1109/TFUZZ.2021.3106674
    [32]
    J. Liu, L. Zha, E. Tian, et al., “Interval type-2 fuzzy-modelbased filtering for nonlinear systems with event-triggering weighted tryonce-discard protocol and cyber-attacks,” IEEE Trans. Fuzzy Systems, , vol. 32, no. 3, pp. 721–732, 2024. doi: 10.1109/TFUZZ.2023.3305088
    [33]
    Z. Lian, P. Shi, and C. C. Lim, “Dynamic hybrid-triggered-based fuzzy control for nonlinear networks under multiple cyberattacks,” IEEE Trans. Fuzzy Systems, vol. 30, no. 9, pp. 3940–3951, 2022. doi: 10.1109/TFUZZ.2021.3134745
    [34]
    J. Liu, T. Yin, J. Cao, D. Yue, and H. R. Karimi, “Security control for T-S fuzzy systems with adaptive event-triggered mechanism and multiple cyber-attacks,” IEEE Trans. Systems, Man, and Cybern.: Systems, vol. 51, no. 10, pp. 6544–6554, 2021. doi: 10.1109/TSMC.2019.2963143
    [35]
    Y. Tan, Y. Yuan, X. Xie, E. Tian, and J. Liu, “Observer-based eventtriggered control for interval type-2 fuzzy networked system with network attacks,” IEEE Trans. Fuzzy Systems, vol. 31, no. 8, pp. 2788–2798, 2023. doi: 10.1109/TFUZZ.2023.3237846
    [36]
    Z. Zhang and J. Dong, “A novel H control for T-S fuzzy systems with membership functions online optimization learning,” IEEE Trans. Fuzzy Systems, vol. 30, no. 4, pp. 1129–1138, 2022. doi: 10.1109/TFUZZ.2021.3053315
    [37]
    X. Li and D. Ye, “Asynchronous event-triggered control for networked interval type-2 fuzzy systems against DoS attacks,” IEEE Trans. Fuzzy Systems, vol. 29, no. 2, pp. 262–274, 2021. doi: 10.1109/TFUZZ.2020.2975495
    [38]
    Y. Pan and G.-H. Yang, “Event-triggered fault detection filter design for nonlinear networked systems,” IEEE Trans. Systems, Man, and Cybern.: Systems, vol. 48, no. 11, pp. 1851–1862, 2018. doi: 10.1109/TSMC.2017.2719629
    [39]
    X.-L. Wang, G.-H. Yang, and D. Zhang, “Event-triggered fault detection observer design for T-S fuzzy systems,” IEEE Trans. Fuzzy Systems, vol. 29, no. 9, pp. 2532–2542, 2021. doi: 10.1109/TFUZZ.2020.3002393
    [40]
    J. Liu, Y. Gu, X. Xie, D. Yue, and J. H. Park, “Hybrid-driven-based H control for networked cascade control systems with actuator saturations and stochastic cyber attacks,” IEEE Trans. Systems, Man, and Cybern.: Systems, vol. 49, no. 12, pp. 2452–2463, 2019. doi: 10.1109/TSMC.2018.2875484
    [41]
    L. Zha, R. Liao, J. Liu, X. Xie, E. Tian, and J. Cao, “Dynamic eventtriggered output feedback control for networked systems subject to multiple cyber attacks,” IEEE Trans. Cybern., vol. 52, p. 12, 2022.
    [42]
    H. Li, C. Wu, X. Jing, and L. Wu, “Fuzzy tracking control for nonlinear networked systems,” IEEE Trans. Cybern., vol. 47, no. 8, pp. 2020–2031, 2017. doi: 10.1109/TCYB.2016.2594046
    [43]
    G. Ran, C. Li, R. Sakthivel, C. Han, B. Wang, and J. Liu, “Adaptive event-triggered asynchronous control for interval type-2 fuzzy Markov jump systems with cyberattacks,” IEEE Trans. Control of Network Systems, vol. 9, no. 1, pp. 88–99, 2022. doi: 10.1109/TCNS.2022.3141025

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(16)  / Tables(1)

    Article Metrics

    Article views (51) PDF downloads(23) Cited by()

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return
    Return