Reliable asynchronous sampled-data filtering of T-S fuzzy uncertain delayed neural networks with stochastic switched topologies

By Kaibo Shi, Jun Wang, Yuanyan Tang, Shouming Zhong. Fuzzy Sets Syst., 2020

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Abstract This paper investigates the issue of the reliable asynchronous sampled-data filtering of Takagi–Sugeno (T–S) fuzzy delayed neural networks with stochastic intermittent faults, randomly occurring time-varying parameters uncertainties and controller gain fluctuation. The asynchronous phenomenon occurs between the system modes and controller modes. First, in order to reduce the utilization rate of communication bandwidth, a novel alterable sampled-data terminal method is considered via variable sampling rates. Second, based on the fuzzy-model-based control approach, improved reciprocally convex inequality and new parameter-time-dependent discontinuous Lyapunov approach, several relaxed conditions are derived and compared with the existing work. Third, the intermittent fault-tolerance scheme is also taken into fully account in designing a reliable asynchronous sampled-data controller, which ensures such that the resultant neural networks is asymptotically stable. Finally, two numerical examples are presented to illustrate the effectiveness and advantages of the theoretical results.

Subfields: Artificial Intelligence, Computer Networks and Communications, Control and Systems Engineering

View Paper: https://www.semanticscholar.org/paper/5512be062252555c65220276ba25979488ed1ed2