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Exponential Synchronization of Delayed Neural Networks with Actuator Failure Using Stochastic Sampled-data Control

Ganlei Zhang, Jiayong Zhang*, Wei Li*, Chao Ge*, and Yajuan Liu
International Journal of Control, Automation, and Systems, vol. 20, no. 2, pp.691-701, 2022

Abstract : This paper investigates the exponential synchronization issue for delayed neural networks with stochastic sampling. The variable sampling period of controller is assumed to switch stochastically between different values with given probability. In addition, the actuator failure phenomenon may occur in many actual systems which is taken into account. By using input delay method, the sampling system is converted to the continuous system. Then, a neoteric time-delay Lyapunov-Krasovskii functional (LKF) that contains delay bounds information is constructed and by using reciprocally convex approach, the sufficient conditions are derived to guarantee the exponentially mean-square stable of the delayed neural networks. The corresponding sampled-data controller can be obtained in terms of the solution to linear matrix inequalities (LMIs). Finally, one numerical example is used to illustrate the effectiveness of proposed method.

Keyword : Actuator failure, delayed neural networks, exponential synchronization, stochastic sampling.

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