Event-Triggered Dissipative Filter Design for Semi-Markovian Jump Systems with Time-Varying Delays

Abstract

This paper deals with the event-triggered dissipative filtering problem for semi-Markovian jump systems with time-varying delays. The purpose is to design a filter which guarantees that the filtering error system is not only stochastically stable but also satisfies dissipativity. First, based on Lyapunov–Krasovskii theory and matrix integral inequality, a sufficient condition is established for the existence of such a filter. Then, the codesign method of event-triggered matrices and the desired filter parameters is proposed. Finally, two numerical examples are given to illustrate the advantages and validity of the scheme developed in this paper.

Finite-time dissipative filter design for discrete-time stochastic interval systems with time-varying delays

“…Noted that the interval matrix method not only overcomes the influence of countless subsystems switching casually in the system (1), but also greatly reduces computational complexity and conservativeness, which makes the results different from the existing ones. [35][36][37][38][39] Denote…”

Finite‐time dissipative control for time‐delay Markov jump systems with conic‐type non‐linearities under guaranteed cost controller and quantiser

“…In [21], the fault detection of conic non-linear systems was investigated. The dissipative theory [22], since its proposal by Willems, has received much attention and has been widely used, for example, circuit analysis [23][24][25][26], neural networks [27][28][29][30], filtre design [31][32][33][34]. In [35], the authors achieved the stochastic stability and extended dissipativity of fuzzy switched systems by designing an asynchronous controller.…”

H 2 Control of Markovian Jump Systems with Input Saturation and Incomplete Knowledge of Transition Probabilities

“…Over the last few decades, Markovian jump systems (MJSs) have been recognized as one of the most effective models for the representation of dynamic systems subjected to random and abrupt variations. us, numerous studies have been conducted to analyze and synthesize MJSs [1][2][3][4][5][6][7]. e findings of these studies have been applied in various practical systems, such as networked control systems [8], manufacturing systems [8], economic systems [9], power systems [10], and actuator saturation [11].…”