Adaptive practical stabilization of a class of uncertain nonlinear systems via sampled-data control

In this paper, an adaptive practical stabilization problem is investigated for a class of nonlinear systems via sampled-data control. The systems under study possess uncertain dynamics and unknown gain functions. During sampled-data controller design procedure, a dynamic signal is introduced to dominate the unmeasured states existed in the external disturbances, and neural networks are adopted to approximate the unknown nonlinear functions. By choosing appropriate sampling period, the designed sampled-data controller can render all states of the resulting closed-loop system to be semi-globally uniformly ultimately bounded. Two examples are given to demonstrate feasibility and efficacy of the proposed methods.