Recursive linear optimal filter for Markovian jump linear systems with multi-step correlated noises and multiplicative random parameters

This paper presents the state estimation problem for discrete-time Markovian jump linear systems with multi-step correlated additive noises and multiplicative random parameters (termed as MCNMP). A recursive linear optimal filter for the considered MCNMP (which is abbreviated as RLMMF) is derived based on state augmentation between the original state and mode uncertainty, with the help of estimating the multi-step correlated additive noises online simultaneously. A maneuvering target tracking example under one-step and two-step correlated additive noises scenarios with different cases (i.e. Gaussian/Gaussian mixture distribution and no multiplicative noises) is simulated to validate the designed filter.     

On almost sure stability of continuous-time Markov jump linear systems

In this paper, we study the almost sure stability of continuous-time jump linear systems with a finite-state Markov form process. A sufficient condition for almost sure stability is derived that refers to the statistics of the transition matrix over m switches. It is shown that, if the system is exponentially almost sure stable, there exists a finite m such that the criterion is satisfied. In order to evaluate the expected value appearing in the condition, an efficient Monte Carlo algorithm is worked out.     

Quantized filtering for Markovian jump LPV systems with intermittent measurements

This paper investigates the problem of quantized filtering for a class of discrete‐time linear parameter‐varying systems with Markovian switching under data missing. The measured output of the plant is quantized by a logarithmic mode‐independent quantizer. The data missing phenomenon is modeled by a stochastic variable. The purpose of the problem addressed is to design a full‐order filter such that the filtering error dynamics is stochastically stable and the prescribed noise attenuation level in the sense can be achieved. Sufficient conditions are derived for the existence of such filters in terms of parameterized linear matrix inequalities. Then the corresponding filter synthesis problem is transformed into a convex optimization problem that can be efficiently solved by using standard software packages. A simulation example is utilized to demonstrate the usefulness of the developed theoretical results. Copyright © 2011 John Wiley & Sons, Ltd.     

Quantized robust H-two filtering for Markovian jump linear systems over networks with nonaccessible mode information

This paper is concerned with the problems of H-two filtering for discrete-time Markovian jump linear systems subject to logarithmic quantization. We assume that only the output of the system is available, and therefore the mode information is nonaccessible. In this paper, a mode-independent quantized H-two filter is designed such that filter error system is stochastically stable. To this end, sufficient conditions for the existence of an upper bound of H-two norm are presented in terms of linear matrix inequalities. Considering uncertainty of system matrices, a robust H-two filter is designed. The proposed method is also applicable to cover the case where the transition probability matrix is not exactly known but belongs to a given polytope. Finally, numerical examples are provided to demonstrate the effectiveness of the proposed approach.     

Resilient linear filtering of uncertain systems

The problem of resilient linear filtering for a class of linear continuous-time systems with norm-bounded uncertainties is investigated. We have considered additive filter gain variations to reflect the imprecision in filter implementation. The design problem of resilient linear filter is formulated as a convex optimization problem over linear matrix inequalities. As a limiting procedure, the case of resilient Kalman filter is derived. All the developed results are conveniently extended to the case of multiplicative filter gain variations. Simulation studies are carried out to support the theoretical findings.     

Reduced-order filtering for linear systems with Markovian jump parameters

This paper addresses the reduced-order H_∞ filtering problem for continuous-time Makovian jump linear systems, where the jump parameters are modelled by a discrete-time Markov process. Sufficient conditions for the existence of the reduced-order H_∞ filter are proposed in terms of linear matrix inequalities (LMIs) and a coupling non-convex matrix rank constraint. In particular, the sufficient conditions for the existence of the zero-order H_∞ filter can be expressed in terms of a set of strict LMIs. The explicit parameterization of the desired filter is also given. Finally, a numerical example is given to illustrate the proposed approach.     

Optimal filtering for networked systems with Markovian communication delays

This paper is concerned with optimal filter problems for networked systems with random transmission delays, while the delay process is modeled as a multi-state Markov chain. By defining a delay-free observation sequence, the optimal filter problems are transformed into ones of the Markov jumping parameter system. We first present an optimal Kalman filter, which is with time-varying, path-dependent filter gains, and the number of the paths grows exponentially in time delay. Thus an alternative optimal Markov jump linear filter is presented, in which the filter gains just depend on the present value of the Markov chain. Further, an optimal filter with constant-gains is developed, the existence condition for the stabilizing solutions to the filter is given, and it can be shown that the proposed Markov jump linear filter converges to the constant-gain filter under appropriate assumptions.     

Network-based event-triggered filtering for Markovian jump systems

The problem of event-triggered H_∞ filtering for networked Markovian jump system is studied in this paper. A dynamic discrete event-triggered scheme is designed to choose the transmitted data for different Markovian jumping modes. The time-delay modelling method is employed to describe the event-triggered scheme and the network-related behaviour, such as transmission delay, data package dropout and disorder, into a networked Markovian time-delay jump system. Furthermore, a sufficient condition is derived to guarantee that the resulting filtering error system is stochastically stable with a prescribed performance index. A co-design method for the H_∞ filter and the event-triggered scheme is then proposed. The effectiveness and potential of the theoretic results obtained are illustrated by a simulation example.     

Robust ℋ︁∞ filtering for uncertain Markovian jump linear systems

This paper investigates the problem of ??_∞ filtering for a class of uncertain Markovian jump linear systems. The uncertainty is assumed to be norm‐bounded and appears in all the matrices of the system state‐space model, including the coefficient matrices of the noise signals. It is also assumed that the jumping parameter is available. We develop a methodology for designing a Markovian jump linear filter that ensures a prescribed bound on the ??₂‐induced gain from the noise signals to the estimation error, irrespective of the uncertainty. The proposed design is given in terms of linear matrix inequalities. Copyright © 2002 John Wiley & Sons, Ltd.     

Delay-dependent stability analysis for Markovian jump systems with interval time-varying-delays

This paper proposes improved stochastic stability conditions for Markovian jump systems with interval time-varying delays. In terms of linear matrix inequalities (LMIs), less conservative delay-range-dependent stability conditions for Markovian jump systems are proposed by constructing a different Lyapunov-Krasovskii function. The resulting criteria have advantages over some previous ones in that they involve fewer matrix variables but have less conservatism. Numerical examples are provided to demonstrate the efficiency and reduced conservatism of the results in this paper.     

H∞ filtering for singular Markovian jump systems with time delay

The problem of H_∞ filtering is considered for singular Markovian jump systems with time delay. In terms of linear matrix inequality (LMI) approach, a delay‐dependent bounded real lemma (BRL) is proposed for the considered system to be stochastically admissible while achieving the prescribed H_∞ performance condition. Based on the BRL and under partial knowledge of the jump rates of the Markov process, both delay‐dependent and delay‐independent sufficient conditions that guarantee the existence of the desired filter are presented. The explicit expression of the desired filter gains is also characterized by solving a set of strict LMIs. Some numerical examples are given to demonstrate the effectiveness of the proposed methods. Copyright © 2009 John Wiley & Sons, Ltd.     

Mean square stability for Kalman filtering with Markovian packet losses

This paper studies the stability of Kalman filtering over a network subject to random packet losses, which are modeled by a time-homogeneous ergodic Markov process. For second-order systems, necessary and sufficient conditions for stability of the mean estimation error covariance matrices are derived by taking into account the system structure. While for certain classes of higher-order systems, necessary and sufficient conditions are also provided to ensure stability of the mean estimation error covariance matrices. All stability criteria are expressed by simple inequalities in terms of the largest eigenvalue of the open loop matrix and transition probabilities of the Markov process. Their implications and relationships with related results in the literature are discussed.     

Robust sampled-data control for Markovian jump linear systems

In this paper, we consider the problem of robust control for uncertain sampled-data systems that possess random jumping parameters which is described by a finite-state Markov process. The conditions for the existence of a stabilizing control and optimal control for the underlying systems are obtained. The desired controllers are designed which are in terms of matrix inequalities. Finally, a numerical example is given to show the potential of the proposed techniques.     

A state predictor for continuous-time stochastic systems

This work investigates the state prediction problem for nonlinear stochastic differential systems, affected by multiplicative state noise. This problem is relevant in many state-estimation frameworks such as filtering of continuous-discrete systems (i.e. stochastic differential systems with discrete measurements) and time-delay systems. A very common heuristic to achieve the state prediction exploits the numerical integration of the deterministic nonlinear equation associated to the noise-free system. Unfortunately these methods provide the exact solution only for linear systems. Instead here we provide the exact state prediction for nonlinear system in terms of the series expansion of the expected value of the state conditioned to the value in a previous time instant, obtained according to the Carleman embedding technique. The truncation of the infinite series allows to compute the prediction at future times with an arbitrary approximation. Simulations support the effectiveness of the proposed state-prediction algorithm in comparison to the aforementioned heuristic method.     

Stochastic model predictive control for constrained discrete-time Markovian switching systems

In this paper we study constrained stochastic optimal control problems for Markovian switching systems, an extension of Markovian jump linear systems (MJLS), where the subsystems are allowed to be nonlinear. We develop appropriate notions of invariance and stability for such systems and provide terminal conditions for stochastic model predictive control (SMPC) that guarantee mean-square stability and robust constraint fulfillment of the Markovian switching system in closed-loop with the SMPC law under very weak assumptions. In the special but important case of constrained MJLS we present an algorithm for computing explicitly the SMPC control law off-line, that combines dynamic programming with parametric piecewise quadratic optimization.     

Stability and stabilization of Markovian jump linear systems with partly unknown transition probabilities

In this paper, the stability and stabilization problems of a class of continuous-time and discrete-time Markovian jump linear system (MJLS) with partly unknown transition probabilities are investigated. The system under consideration is more general, which covers the systems with completely known and completely unknown transition probabilities as two special cases — the latter is hereby the switched linear systems under arbitrary switching. Moreover, in contrast with the uncertain transition probabilities studied recently, the concept of partly unknown transition probabilities proposed in this paper does not require any knowledge of the unknown elements. The sufficient conditions for stochastic stability and stabilization of the underlying systems are derived via LMIs formulation, and the relation between the stability criteria currently obtained for the usual MJLS and switched linear systems under arbitrary switching, are exposed by the proposed class of hybrid systems. Two numerical examples are given to show the validity and potential of the developed results.     

Detection and estimation for abruptly changing systems

The problem of state estimation and system structure detection for discrete stochastic dynamical systems with parameters which may switch among a finite set of values is considered. The switchings are modelled by a Markov chain with known transition probabilities. A brief survey and a unified treatment of the existing suboptimal algorithms are provided. The optimal algorithms require exponentially increasing memory and computations with time. Simulation results comparing the various suboptimal algorithms are presented.     

Positive filtering for continuous-time positive systems under L1 performance

In this paper, we address the positive filtering problem for positive continuous-time systems under the L₁-induced performance. A pair of positive filters with error-bounding feature is proposed to estimate the output of positive systems. A novel characterisation is first obtained to ensure that the filtering error system is asymptotically stable with a prescribed L₁-induced performance. Then, necessary and sufficient conditions for the existence of required filters are presented, and the obtained results are expressed in terms of linear programming problems, which can be easily checked by standard software. Finally, a numerical example is given to illustrate the effectiveness of the proposed design procedures.