非线性系统
控制理论(社会学)
数学
理论(学习稳定性)
李普希茨连续性
趋同(经济学)
随机变量的收敛性
应用数学
计算机科学
随机变量
控制(管理)
数学分析
统计
人工智能
量子力学
经济增长
机器学习
物理
经济
作者
Wangli He,Feng Qian,Qing‐Long Han,Guanrong Chen
出处
期刊:IEEE Transactions on Automatic Control
[Institute of Electrical and Electronics Engineers]
日期:2020-09-01
卷期号:65 (9): 3879-3886
被引量:83
标识
DOI:10.1109/tac.2020.2972220
摘要
This article is concerned with the stability problem for a class of Lipschitz-type nonlinear systems in networked environments, which are suffered from random and impulsive deception attacks. The attack is modeled as a randomly destabilizing impulsive sequence, whose impulsive instants and impulsive gains are both random with only the expectations available. Almost sure stability is ensured based on Doob's Martingale Convergence Theorem. Sufficient conditions are derived for the solution of the nonlinear system to be almost surely stable. An example is given to verify the effectiveness of the theoretical results. It is shown that the random attack will be able to destroy the stability, therefore, a large feedback gain may be necessary.
科研通智能强力驱动
Strongly Powered by AbleSci AI