This paper is aimed at the distributed fault estimation issue associated with the potential loss of actuator efficiency for a type of discrete-time nonlinear systems with sensor saturation.For the distributed estimati...This paper is aimed at the distributed fault estimation issue associated with the potential loss of actuator efficiency for a type of discrete-time nonlinear systems with sensor saturation.For the distributed estimation structure under consideration,an estimation center is not necessary,and the estimator derives its information from itself and neighboring nodes,which fuses the state vector and the measurement vector.In an effort to cut down data conflicts in communication networks,the stochastic communication protocol(SCP)is employed so that the output signals from sensors can be selected.Additionally,a recursive security estimator scheme is created since attackers randomly inject malicious signals into the selected data.On this basis,sufficient conditions for a fault estimator with less conservatism are presented which ensure an upper bound of the estimation error covariance and the mean-square exponential boundedness of the estimating error.Finally,a numerical example is used to show the reliability and effectiveness of the considered distributed estimation algorithm.展开更多
This paper addresses the issue of nonfragile state estimation for memristive recurrent neural networks with proportional delay and sensor saturations. In practical engineering, numerous unnecessary signals are transmi...This paper addresses the issue of nonfragile state estimation for memristive recurrent neural networks with proportional delay and sensor saturations. In practical engineering, numerous unnecessary signals are transmitted to the estimator through the networks, which increases the burden of communication bandwidth. A dynamic event-triggered mechanism,instead of a static event-triggered mechanism, is employed to select useful data. By constructing a meaningful Lyapunov–Krasovskii functional, a delay-dependent criterion is derived in terms of linear matrix inequalities for ensuring the global asymptotic stability of the augmented system. In the end, two numerical simulations are employed to illustrate the feasibility and validity of the proposed theoretical results.展开更多
This article presents an event-based framework for nonfragile state estimation in memristor-driven competitive neural networks,which are particularly vulnerable to stochastic cyber attacks and sensor saturation.Often,...This article presents an event-based framework for nonfragile state estimation in memristor-driven competitive neural networks,which are particularly vulnerable to stochastic cyber attacks and sensor saturation.Often,unnecessary signals burden the communication bandwidth when transmitted to the estimator.To address this,the article proposes a dynamic event-triggered mechanism that carefully transmits relevant data.Unlike a static event-trigger,this approach optimizes data transmission.By constructing proper Lyapunov-Krasovskii functional the authors establish delay-dependent criteria expressed as linear matrix inequalities to ensure asymptotic stability of the system.To showcase the advantages of the proposed criteria and confirm their practical relevance,two well-established benchmark problems including a quadruple-tank control system are examined.展开更多
基金supported in part by the National Natural Science Foundation of China(62073189,62173207)the Taishan Scholar Project of Shandong Province(tsqn202211129)。
文摘This paper is aimed at the distributed fault estimation issue associated with the potential loss of actuator efficiency for a type of discrete-time nonlinear systems with sensor saturation.For the distributed estimation structure under consideration,an estimation center is not necessary,and the estimator derives its information from itself and neighboring nodes,which fuses the state vector and the measurement vector.In an effort to cut down data conflicts in communication networks,the stochastic communication protocol(SCP)is employed so that the output signals from sensors can be selected.Additionally,a recursive security estimator scheme is created since attackers randomly inject malicious signals into the selected data.On this basis,sufficient conditions for a fault estimator with less conservatism are presented which ensure an upper bound of the estimation error covariance and the mean-square exponential boundedness of the estimating error.Finally,a numerical example is used to show the reliability and effectiveness of the considered distributed estimation algorithm.
文摘This paper addresses the issue of nonfragile state estimation for memristive recurrent neural networks with proportional delay and sensor saturations. In practical engineering, numerous unnecessary signals are transmitted to the estimator through the networks, which increases the burden of communication bandwidth. A dynamic event-triggered mechanism,instead of a static event-triggered mechanism, is employed to select useful data. By constructing a meaningful Lyapunov–Krasovskii functional, a delay-dependent criterion is derived in terms of linear matrix inequalities for ensuring the global asymptotic stability of the augmented system. In the end, two numerical simulations are employed to illustrate the feasibility and validity of the proposed theoretical results.
基金supported by the National Natural Science Foundation of China(Grant No.62573122)the Fundamental Research Funds for the Central Universities(Grant No.2242025K30025)+2 种基金the Open Research Project of State Key Laboratory of Industrial Control Technology,China(Grant No.ICT2025B37)the Open Research Project of the Key Laboratory of Numerical Simulation for Large Scale Complex Systems,Ministry of Education,China(Grant No.NSLSCS202502)the Jiangsu Provincial Scientific Research Center of Applied Mathematics(Grant No.BK20233002)。
文摘This article presents an event-based framework for nonfragile state estimation in memristor-driven competitive neural networks,which are particularly vulnerable to stochastic cyber attacks and sensor saturation.Often,unnecessary signals burden the communication bandwidth when transmitted to the estimator.To address this,the article proposes a dynamic event-triggered mechanism that carefully transmits relevant data.Unlike a static event-trigger,this approach optimizes data transmission.By constructing proper Lyapunov-Krasovskii functional the authors establish delay-dependent criteria expressed as linear matrix inequalities to ensure asymptotic stability of the system.To showcase the advantages of the proposed criteria and confirm their practical relevance,two well-established benchmark problems including a quadruple-tank control system are examined.
