Adaptive Event-Triggered and Double-Quantized Consensus of Leader-Follower Multiagent Systems With Semi-Markovian Jump Parameters

2020 ◽  
pp. 1-13
Author(s):  
Cheng Gong ◽  
Guopu Zhu ◽  
Peng Shi
Keyword(s):  
Multiagent Systems ◽  
Markovian Jump ◽  
Markovian Jump Parameters ◽  
Quantized Consensus ◽  
Event Triggered

Event-triggered sliding mode control of networked control systems with Markovian jump parameters

Automatica ◽  
2021 ◽  
Vol 125 ◽  
pp. 109405
Author(s):  
Xiaojie Su ◽  
Chunlian Wang ◽  
Hongbin Chang ◽  
Yue Yang ◽  
Wudhichai Assawinchaichote
Keyword(s):  
Sliding Mode Control ◽  
Control Systems ◽  
Networked Control Systems ◽  
Sliding Mode ◽  
Networked Control ◽  
Markovian Jump ◽  
Markovian Jump Parameters ◽  
Mode Control ◽  
Event Triggered

Dissipativity analysis of delayed stochastic generalized neural networks with Markovian jump parameters

2021 ◽  
Vol 0 (0) ◽  
Author(s):  
Grienggrai Rajchakit ◽  
Ramalingam Sriraman ◽  
Rajendran Samidurai
Keyword(s):  
Linear Matrix ◽  
Time Varying ◽  
Matrix Inequalities ◽  
Markovian Jump ◽  
Stochastic Perturbations ◽  
Practical Applications ◽  
Markovian Jump Parameters ◽  
Generalized Neural Networks ◽  
System Information ◽  
Dissipativity Analysis

Abstract This article discusses the dissipativity analysis of stochastic generalized neural network (NN) models with Markovian jump parameters and time-varying delays. In practical applications, most of the systems are subject to stochastic perturbations. As such, this study takes a class of stochastic NN models into account. To undertake this problem, we first construct an appropriate Lyapunov–Krasovskii functional with more system information. Then, by employing effective integral inequalities, we derive several dissipativity and stability criteria in the form of linear matrix inequalities that can be checked by the MATLAB LMI toolbox. Finally, we also present numerical examples to validate the usefulness of the results.

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