Time-delay neural network observer-based adaptive finite-time prescribed performance control for nonlinear systems with unknown time-delay

Yuzhuo Zhao and Dan Ma

Chaos, Solitons & Fractals, 2025, vol. 191, issue C

Abstract: An adaptive finite-time prescribed performance control (FTPPC) strategy is considered based on the time-delay neural network (NN) observer for the uncertain nonlinear system with unknown time-delay. Unlike previous works, a time-delay NN state observer based on the existing NN state observer is proposed, which not only solves the problem of the linear observer being unable to accurately observe the system states, but also extends the NN state observer without the time-delay to the time-delay NN state observer for the nonlinear system with state time-delay. What is more, instead of traditional Krasovskii functionals, the finite covering lemma and the RBF NN are combined to approximate unknown nonlinear time-delay functions. In addition, an adaptive FTPPC method is proposed by using the finite-time performance function (FTPF), which ensures the dynamic performance of the system while ensures the steady-state performance of the system in finite time. Among them, the stability time can be arbitrarily given, which means it does not rely on any parameter value. Finally, the electromechanical system is utilized to verify the effectiveness of the proposed strategy.

Keywords: Adaptive control; Finite-time prescribed performance; Nonlinear time-delay systems; Time-delay neural network state observer (search for similar items in EconPapers)
Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:eee:chsofr:v:191:y:2025:i:c:s0960077924014437

DOI: 10.1016/j.chaos.2024.115891

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