Adaptive Sampled‐Data Stabilization for Nonlinear Systems With Quantized Input and Input Unmodeled Dynamics

This article focuses on an unresolved sampled‐data stabilizing control problem pertaining to a concerned nonlinear system with logarithmic quantized input and input unmodeled dynamics. The M‐filters are employed to handle the input unmodeled dynamics, while the radial basis function neural networks (RBFNNs) realize the approximation for existing nonlinearities in the backstepping design procedure. Under the appropriate choice of Lyapunov function candidates (LFCs), the analysis demonstrates that the resulting sampled‐data stabilizer enables the formulated closed‐loop system to have practically asymptotic stability through selecting suitable parameters and an allowable sampling period. Simulation results, which are exported from a power system, reflect the effectiveness of the developed control scheme.