H ∞ filtering for Markovian jump linear systems

Fixed Interval Optimal Estimation for Linear Discrete-Time Markovian Jump Systems by Maximum Likelihood Approach

“…The optimal and H ∞ control theory has presented for each of these systems respectively ( [7,9,15,25]). With regard to the estimation for the Markovian jump systems, the LMMSE filtering theory ( [6,7,11]), the approximately optimal smoothing theory ( [4,14]) and the H ∞ filtering theory ( [26,27]) in terms of LMIs have been presented.…”

“…Markovian jump systems ( [4,5,6,7,8,9,10,11,14,15,17,18,25,26,27]) have abrupt random mode changes in their dynamics. The mode changes follow Markov processes.…”

“…Such systems may be found in the area of power systems, manufacturing systems, communications, aerospace systems, financial engineering and so on. Such systems are classified into continuoustime ( [5,8,10,11,15,17,25,26]) and discrete-time ( [4,6,7,9,14,18,27]) systems. The optimal and H ∞ control theory has presented for each of these systems respectively ( [7,9,15,25]).…”

Stability Analysis of Markovian Jump Systems with Multiple Delay Components and Polytopic Uncertainties

“…This class of systems is often used to model systems whose structures are subject to abrupt changes and their extensive applications have been applied to many physical systems such as manufacturing systems, aircraft control, target tracking, robotics, solar receiver control, power systems, and so on [2,18]. Considerable attention has recently been devoted to the study of Markovian jump linear systems such as controllability [16,17], stability [5,10,37], H ∞ control [2,7,19,23,28], H 2 control [9], H ∞ filtering [11,22,26,36,39], guaranteed cost control [8], and model reduction [30,42].…”

Optimal State Estimation for Discrete-Time Markov Jump Systems with Missing Observations

“…∞ filtering algorithm [26][27][28] has been developed for discrete systems with random packet losses in [29,30]. In [31], a robust filtering algorithm was developed for state estimation of MJLSs with random missing observation by applying basic IMM approach and ∞ technique.…”