Novel Mismatch Parameter-Dependent Stabilization Approach Based on Sampled-Data Fuzzy Lyapunov Function

This article concentrates on the sampled-data control problem by utilizing a novel mismatch parameter-dependent stabilization method for the Takagi-Sugeno (T-S) fuzzy system. First, taking the information on sampled-parameter and fuzzy weighted function into account, a novel sampled-parameter-dependent fuzzy Lyapunov function is proposed. Furthermore, an affine matched sampled-data controller is designed to contain an affine transformed membership function, thereby achieving larger stabilizable regions. Based on a novel fuzzy Lyapunov function and parameterized matrices bounding technique, a sufficient condition concerning the asymptotic stability of the closed-loop fuzzy system is formulated in the form of linear matrix inequality. In comparison with the prior works, the derived condition has less conservative and the largest sampling interval. Numerical experiments on Rosser's system confirm the advantages and benefits of the novel mismatch parameter-dependent stabilization approach.