Exponential passivity of discrete-time switched neural networks with transmission delays via an event-triggered sliding mode control

2021 ◽  
Author(s):  
Jinling Wang ◽  
Haijun Jiang ◽  
Cheng Hu ◽  
Tianlong Ma
Keyword(s):  
Neural Networks ◽  
Sliding Mode Control ◽  
Discrete Time ◽  
Sliding Mode ◽  
Transmission Delays ◽  
Mode Control ◽  
Switched Neural Networks ◽  
Event Triggered

scholarly journals Event-triggered Discrete-time Sliding Mode Control for High-order Systems via Reduced-order Model Approach

2020 ◽  
Vol 53 (2) ◽  
pp. 6207-6212
Author(s):  
Kiran Kumari ◽  
Bijnan Bandyopadhyay ◽  
Johann Reger ◽  
Abhisek K. Behera
Keyword(s):  
Sliding Mode Control ◽  
Discrete Time ◽  
Sliding Mode ◽  
High Order ◽  
Reduced Order Model ◽  
Order Model ◽  
Reduced Order ◽  
Mode Control ◽  
Event Triggered ◽  
Model Approach

scholarly journals Finite-time Projective Synchronization for Semi-markovian Neural Networks Systems via Event-triggered Sliding Mode Control

2021 ◽  
Author(s):  
Junchao Ren ◽  
xuejiao Li
Keyword(s):  
Neural Networks ◽  
Sliding Mode Control ◽  
Finite Time ◽  
Sliding Mode ◽  
Projective Synchronization ◽  
Linear Matrix ◽  
Markov Jump ◽  
Mode Control ◽  
Markov Jump System ◽  
Event Triggered

Abstract This paper investigates the projection synchronization problem of stochastic neural networked systems based on event-triggered sliding mode control (SMC) covering a finite-time period. For improve transmission efficiency and save network resources, a related event-triggered scheme is proposed for the error system, which can identify whether the measurement error should be transmitted to the controller. For finite-time projective synchronization under given event-triggered mechanism, a semi-Markov jump system model is proposed. Secondly, by creating Lyapunov Krasovsky functional and using linear matrix inequality (LMI) technology, as well as considering a proper sliding surface, a sliding mode controller is designed to implement finite-time projection synchronization of different neural networks. Finally, numerical simulations are exploited to illustrate the effectiveness of the main results.

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