Event-triggered distributed filtering for Markov jump systems over sensor networks

Abstract

This article deals with the distributed filtering problem for a class of discrete-time Markov jump systems over sensor networks. First, in the distributed filtering network, each local filter simultaneously fuses the estimation and measurement from itself and neighboring nodes to achieve the system state estimation. And each sensor intelligent node is embedded with an event-triggered sampling mechanism, which can reduce the computation load or saving limited network bandwidth. Then, we use Bernoulli stochastic variables to describe whether the filtering network can successfully receive the system jump modes. Next, based on the Lyapunov stability theory, we design a distributed filter dependent on partial system modes, which has the exponential stability in mean square and [Formula: see text] performance. Finally, all desired estimator parameters can be obtained by solving a set of linear matrix inequalities. Moreover, two numerical examples are given to illustrate the effectiveness of the distributed [Formula: see text] filtering design approach.

Asynchronous control for Markov jump systems subject to actuator saturation and partial mode information

“…Peng et al 24 let a set Z & R n be the domain of attraction in mean square sense of systems equation ( 1) and x k (x(0), r(0)) denote the trajectory of the state x(k) starting from initial state (x(0), r(0)). Systems equations ( 1)- (2) [with u(k)[0 satisfy stochastic stability at the origin if, for every initial state x 0 2 Z, there exist the inequality…”

“…Due mainly to the jumping characteristic from Markov process, MJSs are effective in modeling complex systems whose structures and parameters are susceptible to abrupt variations. 1,2 Over the past decade, more attention has focused on the control problems of MJSs, and a string of related achievements have been made. Cao et al 3 considered the hybrid sliding mode control problem for the uncertain Markovian jump systems.…”

Set‐membership state estimation for delayed switched systems with fading measurement and interval uncertainty

“…For different noise types, scholars have proposed different state estimation algorithms, such as the Kalman filter, 13,14 $$$ {H}_{\infty } $$$ state estimation, 15,16 recursive filter, 17–19 and so forth. The Kalman filter and $$$ {H}_{\infty } $$$ state estimation are applied to random systems with known noise statistical properties and systems with limited noise energy, respectively.…”

Event-Triggered Fault Estimation and Fault Tolerance for Cyber-Physical Systems with False Data Injection Attacks

“…The core concept in event-triggered data transmission revolves around determining if the information ought to be transmitted via the intended communication channel. Control and estimation theory is one of the first fields to explore the use of event-triggered transmission schemes [6][7][8][9].…”