Self-triggered filter design for a class of nonlinear stochastic systems with Markovian jumping parameters. (May 2021)

• Record Type: Journal Article
• Authors: Yang, Hua; Wang, Zidong; Shen, Yuxuan; Alsaadi, Fuad E.
• Is Part Of: Nonlinear analysis. Volume 40(2021)
• Publication Date: 2021-05
• Subjects: Nonlinear stochastic systems -- Event-triggered mechanism -- Self-triggered mechanism -- Markovian jumping parameters -- Boundedness in probability; Nonlinear functional analysis -- Periodicals; Analyse fonctionnelle non linéaire -- Périodiques; Nonlinear functional analysis; Periodicals; 515.7248
• DOI: 10.1016/j.nahs.2021.101022
• Languages: English
• ISSNs: 1751-570X

Abstract

Abstract: This paper is concerned with the self-triggered filtering problem for a class of Markovian jumping nonlinear stochastic systems. The event-triggered mechanism (ETM) is employed between the sensor and the filter to reduce unnecessary measurement transmission. Governed by the ETM, the measurement is transmitted to the filter as long as a predefined condition is satisfied. The purpose of the addressed problem is to synthesize a filter such that the dynamics of the filtering error is bounded in probability (BIP). A sufficient condition is first given to ensure the boundedness in probability of the filtering error dynamics, and the characterization of the desired filter gains is then realized by means of the feasibility of certain matrix inequalities. Furthermore, a self-triggered mechanism is designed to guarantee the filtering error dynamics to be BSP with excluded Zeno phenomenon. In the end, numerical simulation is carried out to illustrate the usefulness of the proposed self-triggered filtering algorithm.