Adaptive event‐triggered H∞ state estimation of semi‐Markovian jump neural networks with randomly occurred sensor nonlinearity
Hongqian Lu, Yao Xu, Xingxing Song, Wuneng Zhou
Abstract
Abstract This article mainly discusses the problem for adaptive event‐triggered H state estimation of semi‐Markovian jump neural networks (s‐MJNNs) subject to random sensor nonlinearity. To reduce the communication load, adaptive event‐triggered scheme (AETS) is introduced to decide whether to transmit sampled data or not. Also, considering the possible sensor nonlinearity, a new estimation error model is established under the framework of AETS. An appropriate Lyapunov–Krasovskii functional (LKF) containing the proposed adaptive event trigger condition is constructed, and sufficient conditions are obtained to guarantee the asymptotic stability of the estimation error system. Then, through a set of feasible linear matrix inequalities (LMIs), the co‐design method of estimator and AETS is proposed. Finally, the feasibility of this paper is proved by three numerical examples.