Stabilization of sampled-data fuzzy systems under variable sampling

This paper investigates the problem of stabilization for sampled-data fuzzy systems under variable sampling. The Jensen's integral inequality method is employed to reduce the computational complexity, and a reciprocally convex approach is utilized to deal with nonlinear time-varying coefficients derived from the Jensen's integral inequality. Combining with capturing the characteristic of the discussed systems with a novel piecewise Lyapunov-Krasovskii functional (LKF), a stabilization criterion with less complexity and less conservatism is formulated as linear matrix inequalities (LMIs), which can be easily checked by using standard numerical software. An illustrative example is also given to show the effectiveness of the proposed method.