Adaptive event‐triggered H∞ state estimation of semi‐Markovian jump neural networks with randomly occurred sensor nonlinearity

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.

Event‐triggered nonfragile state estimation for delayed neural networks with additive and multiplicative gain variations

“…9,10,18,21,22,27 Although some initial research has been focused on event-triggered state estimation for various types of networks, most of the existing results implicitly assume that the estimator can be executed with precision. 10,22,27 There are very few results that designed the estimator even if some imprecision occurred in the estimator (i.e., nonfragile estimator). 9,18,21 Unfortunately, in the existing literature, 9,18,21 the estimator gain variations of the non-fragile state estimator for delayed NNs under ETM is assumed to satisfy only the additive norm-bounded conditions.…”

“…To address this, in recent years the researchers have been focusing on introducing ETM into the state estimation problem for NNs. 9,10,18,21,22,27 Although some initial research has been focused on event-triggered state estimation for various types of networks, most of the existing results implicitly assume that the estimator can be executed with precision. 10,22,27 There are very few results that designed the estimator even if some imprecision occurred in the estimator (i.e., nonfragile estimator).…”

“…Consequently, implementing a large‐scale NN requires a significant allocation of processing and storage resources. To address this, in recent years the researchers have been focusing on introducing ETM into the state estimation problem for NNs 9,10,18,21,22,27 . Although some initial research has been focused on event‐triggered state estimation for various types of networks, most of the existing results implicitly assume that the estimator can be executed with precision 10,22,27 .…”

Event‐triggered annular finite‐time H∞$$ {H}_{\infty } $$ filtering for discrete‐time mean‐field stochastic systems

“…By introducing the T‐S fuzzy technique and ETM, Li and Xiong 10 solved the $$$ {H}_{\infty } $$$ filtering issue for discrete‐time nonlinear networked systems. Under an adaptive ETM, the $$$ {H}_{\infty } $$$ state estimation of semi‐Markovian jump neural networks was discussed in Reference 11. More achievements can be found in References 12 and 13 and the references therein.…”

Event‐triggered control of complex‐valued networked control systems under randomly occurring deception attacks

“…This mechanism prevents continual communication signals from passing through the network, thereby reducing the transmission load on the communication network and enhancing network channel utilization. Therefore, this type of triggered mechanism has therefore been discussed by numerous researchers 14‐16 . In Reference 17, a novel ETM was proposed, which contributes to the improvement of network channel utilization.…”