Event-triggered \(H_\infty\) synchronization for switched discrete time delayed recurrent neural networks with actuator constraints and nonlinear perturbations

DOI10.1016/j.jfranklin.2020.01.016zbMath1437.93110OpenAlexW3011609574MaRDI QIDQ2181328

M. Syed Ali, R. Vadivel, Young Hoon Joo

Publication date: 19 May 2020

Published in: Journal of the Franklin Institute (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.jfranklin.2020.01.016

zbMATH Keywords

exponential stabilityevent-triggered \(H_\infty\) synchronizationrobust discrete time delayed recurrent neural networks

Mathematics Subject Classification ID

Discrete-time control/observation systems (93C55) (H^infty)-control (93B36) Discrete event control/observation systems (93C65) Control/observation systems governed by functional relations other than differential equations (such as hybrid and switching systems) (93C30) Exponential stability (93D23) Networked control (93B70)

Related Items (10)

\(H \infty\) synchronization of semi-Markovian jump neural networks with random sensor nonlinearities via adaptive event-triggered output feedback control ⋮ Asynchronous fault-tolerant control for stochastic jumping singularly perturbed systems: an \(H_\infty\) sliding mode control scheme ⋮ Event-triggered distributed self-learning robust tracking control for uncertain nonlinear interconnected systems ⋮ Resilient fixed-time synchronization of neural networks under DoS attacks ⋮ Event-triggered delayed impulsive control for functional differential systems on networks ⋮ Global exponential synchronization of switching neural networks with leakage time-varying delays ⋮ Event-triggered gain-scheduling dissipative synchronization control for switched neural networks under state-dependent switching ⋮ Dynamic event‐triggered control for delayed switched neural networks: A merging signal scheme ⋮ Stability analysis and \(L_1\)-gain characterization for uncertain Markovian hybrid switching positive systems ⋮ Quantisation compensated data-driven iterative learning control for nonlinear systems

Uses Software

• LMI toolbox

Cites Work

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