State Estimation for Networked Systems With Markov Driven Transmission and Buffer Constraint
Yong Xu, Lixin Yang, Zhuo Wang, Hongxia Rao, Renquan Lu
Abstract
This article investigates the problem of state estimation for discrete-time systems with a Markov driven transmission strategy. A buffer with limited capacity is used to store the latest measurements, and they are transmitted simultaneously once the system accesses to the shared channel. A buffer-dependent smart estimator is then proposed to process the received measurements. A convex sufficient condition concerning the exponential mean-square stability and the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$l_{2}-l_{\infty }$ </tex-math></inline-formula> performance is established for the estimation error system to design the estimator gains. Finally, two examples are presented to illustrate the effectiveness of the derived result under different conditions.