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

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

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.

Suggested Citation

  • Mao, Jun & Li, Qiang & Wang, Ronghao & Zou, Wencheng & Xiang, Zhengrong, 2025. "Adaptive neural fixed-time sampled-data output-feedback stabilization for a class of nonlinear systems," Applied Mathematics and Computation, Elsevier, vol. 507(C).
    • Handle: RePEc:eee:apmaco:v:507:y:2025:i:c:s0096300325002760
    DOI: 10.1016/j.amc.2025.129550
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