Adaptive neural fixed-time sampled-data output-feedback stabilization for a class of nonlinear systems

Jun Mao, Qiang Li, Ronghao Wang, Wencheng Zou and Zhengrong Xiang

Applied Mathematics and Computation, 2025, vol. 507, issue C

Abstract: This article is engaged in addressing a neural-network-based fixed-time stabilization problem for a controlled nonlinear system by basing on its sampled output detection. For observing unavailable states, an observer, which is established by depending on system's sampled output and sampled-data input, is applied. By following the backstepping technique, an adaptive fixed-time sampled-data output-feedback stabilizer (AFSOS), which is established by depending on the strong approximation ability of neural networks (NNs), is developed. Moreover, singularity-free derivation for developed virtual control laws (VCLs) can be realized by the benefit of VCLs' special switching structures. In the light of fixed-time stability criterion and also by selecting suitable Lyapunov function candidates (LFCs), sufficient conditions for ensuring practically fixed-time stable (PFS) of the formulating closed-loop system can be exported. Lastly, a simulation is carried out to reflect the availability of the developed scheme.

Keywords: Fixed-time control; Sampled-data control; Adaptive output-feedback control; Neural networks (search for similar items in EconPapers)
Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:eee:apmaco:v:507:y:2025:i:c:s0096300325002760

DOI: 10.1016/j.amc.2025.129550

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