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Command-filter-based neural networks predefined time control for switched nonlinear systems with event-triggering mechanism

Author

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  • Yang, Yu
  • Bi, Wenshan
  • Sui, Shuai
  • Chen, C.L. Philip

Abstract

The article proposes a dynamic event-triggered adaptive predefined time output feedback control technique for uncertain switching multi-input multi-output (MIMO) nonlinear systems with strict feedback forms. In contrast to previous event-triggered output feedback control, the control technique proposed in this study not only enables the system to reach steady state within a predefined time, but also further saves communication resources. Subsequently, the unpredictable states of the system are modeled using a neural network (NN) state observer. In the framework of backstepping control, an output feedback control strategy based on command filtering is proposed. Finally, the stability for a switched nonlinear system has been demonstrated using predefined time stability theory and average dwell time (ADT). The results concern this semi-global practically predefined time stabilization (SGPPTS) of all signals in the closed-loop system. Simulations and comparisons are utilized to verify the predefined time control characteristics.

Suggested Citation

  • Yang, Yu & Bi, Wenshan & Sui, Shuai & Chen, C.L. Philip, 2025. "Command-filter-based neural networks predefined time control for switched nonlinear systems with event-triggering mechanism," Applied Mathematics and Computation, Elsevier, vol. 491(C).
    • Handle: RePEc:eee:apmaco:v:491:y:2025:i:c:s0096300324006660
    DOI: 10.1016/j.amc.2024.129205
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