Adaptive neural network-based security asynchronous control for uncertain Markov jump power systems with dead zone under DoS attack
type: Item
instance of: scholarly article
title: Adaptive neural network-based security asynchronous control for uncertain Markov jump power systems with dead zone under DoS attack
zbMATH Open document ID: 1545.93267
author: Shanling Dong
author: Enjun Liu
author: Bo Wang
author: Meiqin Liu
author: Guanrong Chen
published in: International Journal of Robust and Nonlinear Control
publication date: 2024-08-21T00:00:00Z
Mathematics Subject Classification ID: 93C40
Mathematics Subject Classification ID: 93E15
Mathematics Subject Classification ID: 93B70
Mathematics Subject Classification ID: 93B52
zbMATH DE Number: 7900082
zbMATH Keywords: adaptive neural network control
zbMATH Keywords: denial-of-service attack
zbMATH Keywords: hidden Markov model
zbMATH Keywords: input dead zone
zbMATH Keywords: Markov jump
MaRDI profile type: Publication
cites work: Stability Analysis of Power Systems: A Network Synchronization Perspective
cites work: Decentralized control of power systems via robust control of uncertain Markov jump parameter systems
cites work: Asynchronous H∞ filtering of continuous‐time Markov jump systems
cites work: Asynchronous output feedback control for Markov jump systems under dynamic event‐triggered strategy
cites work: Asynchronous observer‐based sliding mode control for 2‐D Markov jump Roesser systems
cites work: Model-Predictive Control for Markovian Jump Systems Under Asynchronous Scenario: An Optimizing Prediction Dynamics Approach
DOI: 10.1002/RNC.7420
description: scientific article; zbMATH DE number 7900082