State estimate for fuzzy neural networks with random uncertainties based on sampled-data control - Ge, Chao; Shi, YanPeng; Park, Ju H.; Hua, Changchun (Journal of the Franklin Institute, 2020)
The state estimation problem for a class of T–S fuzzy neural networks (FNNs) is concerned in this paper, where random uncertainties and variable sampling intervals are taken into account. In order to make full use of the characteristic about real sampling pattern, a novel piecewise Lyapunov–Krasovskii functional (LKF) in which some free-weighting-matrices are not necessarily positive definite is proposed. The cross terms can be deal with by the Free-Matrix-Based (FMB) inequality technique. By use of an appropriate scheme, new sufficient criteria are derived to guarantee the stability of estimation error system with the maximal allowable upper bound (MAUB) of sampling intervals. Then, the sampled-data controller gain matrix can be synthesized by calculating a set of linear matrix inequalities (LMIs). Compared with the existing results, the obtained criteria are less conservative. Finally, the numerical examples are considered and analyzed by the proposed scheme so as to illustrate the benefit and superiority.