An Enhanced Input-Delay Approach to Sampled-Data Stabilization for Nonlinear Stochastic Singular Systems Based on T-S Fuzzy Models

Abstract

The sampled-data stabilization problem of nonlinear stochastic singular systems on the basis of the Takagi–Sugeno fuzzy models under variable samplings is discussed in this article. A new piecewise Lyapunov–Krasovskii functional is constructed, which can capture the actual sampling mode’s available features more fully, and an enhanced input-delay method is presented. By using the proper augmented scheme based on the auxiliary vector function, the new mean square admissibility criteria are derived by making good use of the convex combination techniques and the free weighting matrix approach. It is shown that the obtained results in this article contain less conservatism when compared with the existing ones. The superiority and correctness of our results are verified by an application example of a truck–trailer model.