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Memory-based event-triggered asynchronous control for semi-Markov switching systems

Author

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  • Xie, Lifei
  • Cheng, Jun
  • Wang, Hailing
  • Wang, Jiange
  • Hu, Mengjie
  • Zhou, Zhidong

Abstract

In this paper, the asynchronous control problem is addressed for semi-Markov switching systems with a memory-based event-triggered mechanism. In light of the asynchronous phenomenon between the resulting dynamic modes and the memory controller modes, a more general hidden semi-Markov model is expected. Notably, aiming at decrease the triggering intervals while improving the dynamic performance, a novel mode-dependent memory-based event-triggered mechanism is proposed, whose triggering condition varies with some historic released data. By virtue of the hidden semi-Markov model and historic released data, a memory asynchronous control strategy is skillfully synthesized. By resorting to Lyapunov theory, some criteria are formulated to guarantee the stochastically stable of the resulting dynamic. Eventually, the feasibility of the presented approach is verified by a practical example.

Suggested Citation

  • Xie, Lifei & Cheng, Jun & Wang, Hailing & Wang, Jiange & Hu, Mengjie & Zhou, Zhidong, 2022. "Memory-based event-triggered asynchronous control for semi-Markov switching systems," Applied Mathematics and Computation, Elsevier, vol. 415(C).
    • Handle: RePEc:eee:apmaco:v:415:y:2022:i:c:s0096300321007785
    DOI: 10.1016/j.amc.2021.126694
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    References listed on IDEAS

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    1. Wu, Yuyan & Cheng, Jun & Zhou, Xia & Cao, Jinde & Luo, Mengzhuo, 2021. "Asynchronous filtering for nonhomogeneous Markov jumping systems with deception attacks," Applied Mathematics and Computation, Elsevier, vol. 394(C).
    2. Dong, Zeyu & Wang, Xin & Zhang, Xian, 2020. "A nonsingular M-matrix-based global exponential stability analysis of higher-order delayed discrete-time Cohen–Grossberg neural networks," Applied Mathematics and Computation, Elsevier, vol. 385(C).
    3. Li, Feng & Song, Shuai & Zhao, Jianrong & Xu, Shengyuan & Zhang, Zhengqiang, 2019. "Synchronization control for Markov jump neural networks subject to HMM observation and partially known detection probabilities," Applied Mathematics and Computation, Elsevier, vol. 360(C), pages 1-13.
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    Cited by:

    1. Liu, Yiqun & Zhuang, Guangming & Zhao, Junsheng & Lu, Junwei & Wang, Zekun, 2023. "H∞.. admissibilization for time-varying delayed nonlinear singular impulsive jump systems based on memory state-feedback control," Applied Mathematics and Computation, Elsevier, vol. 447(C).
    2. Lin, An & Cheng, Jun & Cao, Jinde & Wang, Hailing & Alsaedi, Ahmed, 2022. "Fault detection filtering for MNNs with dynamic quantization and improved protocol," Applied Mathematics and Computation, Elsevier, vol. 434(C).

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