ℋ∞ filtering for discrete‐time hidden singular Markov jump systems subject to partially known probability information under DoS attacks. (2nd January 2023)

• Record Type: Journal Article
• Authors: Wang, Guanqi; Shen, Hao; Li, Feng; Wang, Jing
• Is Part Of: International journal of robust and nonlinear control. Volume 33:Number 5(2023)
• Page Start: 3210
• Page End: 3226
• Publication Date: 2023-01-02
• Subjects: DoS attacks -- hidden Markov model -- partially known information -- singular Markov jump systems; Automatic control -- Periodicals; Control theory -- Periodicals; Nonlinear systems -- Periodicals; 629.836
• DOI: 10.1002/rnc.6566
• Languages: English
• ISSNs: 1049-8923

Abstract

Summary: This paper concentrates on the problem of asynchronous ℋ ∞ $$ {\mathscr{H}}_{\infty } $$ filtering for singular Markov jump systems under DoS attacks, in which the unknown probability information cases are considered. To better describe cyber security problem, DoS attacks are considered as encountered randomly and modeled by a set of stochastic variables obeying Bernoulli distribution. Besides, hidden Markov model with partially known probability information is proposed to handle the cases that the Markov state information of the system is restrictedly accessed, and the transition probability information and observation probability information can not be directly obtained. A set of criteria are derived to analyze the regularity, causality, and stochastic stability for the filtering error system with an ℋ ∞ $$ {\mathscr{H}}_{\infty } $$ performance index. Furthermore, an asynchronous filter design method is proposed to against DoS attacks. Finally, a numerical example and a practical example modeled by a DC motor are given to demonstrate the effectiveness of the proposed method.