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Distributed dynamic event-based finite-time dissipative synchronization control for semi-Markov switched fuzzy cyber-physical systems against random packet losses

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Chinese Physics B 2023年第10期32卷 328-342页

作者：伍锡如张煜翀张畑畑张斌磊 School of Electronic Engineering and Automation Guilin University of Electronic TechnologyGuilin 541004China

This paper is concerned with the finite-time dissipative synchronization control problem of semi-Markov switched cyber-physical systems in the presence of packet losses, which is constructed by the Takagi–Sugeno fuzzy model. To save the network communication burden, a distributed dynamic event-triggered mechanism is developed to restrain the information update. Besides, random packet dropouts following the Bernoulli distribution are assumed to occur in sensor to controller channels, where the triggered control input is analyzed via an equivalent method containing a new stochastic variable. By establishing the mode-dependent Lyapunov–Krasovskii functional with augmented terms, the finite-time boundness of the error system limited to strict dissipativity is studied. As a result of the help of an extended reciprocally convex matrix inequality technique, less conservative criteria in terms of linear matrix inequalities are deduced to calculate the desired control gains. Finally, two examples in regard to practical systems are provided to display the effectiveness of the proposed theory.

关键词： cyber-physical systems finite-time synchronization distributed dynamic event-triggered mechanism random packet losses

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Iterative Learning Control for Networked Stochastic Systems with random Measurement losses

Iterative Learning Control for Networked Stochastic Systems ...

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第26届中国控制与决策会议

作者： Dong Shen Youqing Wang College of Information Science and Technology Beijing University of Chemical Technology

The iterative learning control（ILC） is constructed for the discrete-time stochastic systems with random measurement losses modeled by a stochastic sequence. A simple P-type update law is used and the almost sure convergence is strictly proved for both linear case and nonlinear case based on stochastic approximation. Illustrative examples show the effectiveness of the proposed approach.

关键词： Iterative Learning Control Networked Stochastic System random packet losses Stochastic Approximation

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