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

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.