Addressed is the problem of state estimation for dynamic Markovian jump systems (MJS) with unknown transitional probability matrix (TPM) of the embedded Markov chain governing the system jumps. Based on recent authors’ results, proposed is a new TPM-estimation algorithm that utilizes stochastic simulation methods (viz. Bayesian sampling) for finite mixtures’ estimation. Monte Carlo simulation r...

The problem of delay-dependent stochastic stability for neutral Markovian jump systems with mixed delays is investigated in this paper. Combined the new constructed Lyapunov functional with the introduced free matrices, and using the analysis technique of matrix inequalities, the delay-dependent stability conditions are obtained. The obtained results are formulated in terms of LMIs, which can b...

This paper deals with a class of continuous-time uncertain singular linear systems with Markovian jump parameters and time delays. Sufficient conditions on stochastic stability and stochastic stabilizability are developed. A design algorithm for a state-feedback controller which guarantees that the closed-loop dynamic will be regular, impulse free and robustly stochastically stable is proposed ...

Abstract: This paper is considered with the problem of H∞ model reduction for a class of discrete-time 2D Markovian jump systems with state delays described by the Roesser model. Since these obtained conditions are not expressed as strict LMIs, the cone complementarily linearization (CCL) method is exploited to cast them into nonlinear minimization problems subject to LMI constraints. A numeric...

This paper considers the guaranteed cost control of singular Markovian jump systems (SMJSs) with time delay whose mode signal is inaccessible. The main contribution is to develop an approach to mode-independent guaranteed control, where the switching probability rate is also designed. New sufficient conditions of such controller are proposed in terms of linear matrix inequalities with some equa...

We present a novel approach to inference in conditionally Gaussian continuous time stochastic processes, where the latent process is a Markovian jump process. We first consider the case of jump-diffusion processes, where the drift of a linear stochastic differential equation can jump at arbitrary time points. We derive partial differential equations for exact inference and present a very effici...

We showed several years ago that the density operator of Markovian open systems can be diagonalized continuously in time. The resulting pure state jump processes correspond to quantum trajectories proposed in recent quantum optics calculations or, at fundamental level, to exact consistent histories. The quantum state of an open quantum system — actually a subsystem of a closed one — cannot be d...