State Estimation of Switched Time-Delay Complex Networks With Strict Decreasing LKF

IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS(2023)

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摘要
State estimation issue is investigated for a switched complex network (CN) with time delay and external disturbances. The considered model is general with a one-sided Lipschitz (OSL) nonlinear term, which is less conservative than Lipschitz one and has wide applications. Adaptive mode-dependent nonidentical event-triggered control (ETC) mechanisms for only partial nodes are proposed for state estimators, which are not only more practical and flexible but also reduce the conservatism of the results. By using dwell-time (DT) segmentation and convex combination methods, a novel discretized Lyapunov-Krasovskii functional (LKF) is developed such that the value of LKF at switching instants is strict monotone decreasing, which makes it easy for nonweighted L-2-gain analysis without additional conservative transformation. The main results are given in the form of linear matrix inequalities (LMIs), by which the control gains of the state estimator are designed. A numerical example is given to illustrate the advantages of the novel analytical method.
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关键词
Complex networks,discretized Lyapunov-Krasovskii functional (LKF),nonweighted L-2-gain,one-sided Lipschitz (OSL),state estimation,time delay
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