Robust linear filtering for discrete-time hybrid Markov linear systems

In this paper we consider the robust linear filtering of hybrid discrete-time Markovian jump linear systems. We assume that only an output of the system is available, and therefore the values of the jump parameter are not known. It is desired to design a dynamic linear filter such that the closed loop system is mean square stable and minimizes the stationary expected value of the square error. We consider uncertainties on the parameters of the possible modes of operation of the system. A linear matrix inequalities (LMI) formulation is proposed to solve the problem. For the case in which there are no uncertainties on the modes of operation of the system, we show that the LMI formulation provides a filter with the same stationary mean square error as the one obtained from the Riccati equation approach.     

Nonfragile filtering for discrete-time linear systems in finite-frequency domain

This article investigates the problem of nonfragile filter design for discrete-time linear systems subject to noises with known frequency ranges. Additive interval uncertainty reflecting imprecision in filter implementation is considered. By the aid of generalised KYP lemma, both deterministic and randomised filtering algorithms are proposed to deal with noises in low-, middle- and high-frequency domain, respectively. The proposed nonfragile finite-frequency filters can get a better noise attenuation performance when frequency ranges of noises are known beforehand. An example about F-18 aircraft model is given to illustrate the effectiveness of the proposed algorithms.     

Optimal guaranteed cost filtering for uncertain discrete-time linear systems

This paper presents a result on the design of a steady-state robust state estimator for a class of uncertain discrete-time linear systems with normal bounded uncertainty. This result extends the steady state Kalman filter to the case in which the underlying system is uncertain. A procedure is given for the construction of a state estimator which minimizes a bound on the state error covariance. It is shown that this leads to a state estimator which is optimal with respect to a notion of quadratic guaranteed cost state estimation.     

H-infinity filtering for discrete-time switched linear systems under arbitrary switching

This paper is concerned with the problem of H-infinity filtering for discrete-time switched linear systems under arbitrary switching laws.New sufficient conditions for the solvability of the problem are given via switched quadratic Lyapunov functions.Based on Finsler’s lemma,two sets of slack variables with special structure are introduced to provide extra degrees of freedom in optimizing the guaranteed H-infinity performance.Compared to the existing methods,the proposed one has better performances and less conservatism.An example is given to illustrate its effectiveness.     

Nonlinear regulation in constrained input discrete-time linear systems

The use of composite linear and non-linear feedback laws for the control of constrained input discrete-time linear systems is re-examined. By making use of the delta operator formulation of a discrete-time system, an apparent restriction on the magnitude of the non-linear control law is removed, and the similarities between the continuous and discrete-time solutions to the problem are elucidated. In order to develop the results, unconstrained systems are treated initially, but it is shown that, locally, the sufficient conditions for the stabilization of such systems are actually equivalent to those for the stabilization of the corresponding constrained systems.     

Robust filtering for 2-D discrete-time linear systems with convex-bounded parameter uncertainty

This paper is concerned with the problems of robust H_∞ and H₂ filtering for 2-dimensional (2-D) discrete-time linear systems described by a Fornasini-Marchesini second model with matrices that depend affinely on convex-bounded uncertain parameters. By a suitable transformation, the system is represented by an equivalent difference-algebraic representation. A parameter-dependent Lyapunov function approach is then proposed for the design of 2-D stationary discrete-time linear filters that ensure either a prescribed H_∞ performance or H₂ performance for all admissible uncertain parameters. The filter designs are given in terms of linear matrix inequalities. Numerical examples illustrate the effectiveness of the proposed filter design methods.     

Distributed filtering for discrete-time linear systems with fading measurements and time-correlated noise

This paper studies the problem of distributed filtering for discrete-time linear systems with fading measurements and time-correlated noise over a sensor network. To address the problem of the time-correlated measurement noise, the measurement differencing approach is adopted to define a new measurement such that the noise in the new measurement is not time-correlated any longer. Based on the new measurement, the innovation-based and the consensus-based distributed filters are proposed for each sensor by using its neighboring information. By resorting to the graph properties, the filter gain matrices are designed for each sensor to develop optimal distributed filters in the sense of minimum variance. Moreover, suboptimal distributed filters are proposed to reduce the computational cost and the communication cost. The performance of the distributed filters is analyzed with respect to the fading factor. Simulation results are provided to show the effectiveness of the proposed filters.     

广义离散随机线性系统降阶Wiener滤波、平滑和预报器

应用现代时间序列方法 ,基于自回归滑动平均 (ARMA)新息模型、白噪声估值器和观测预报器 ,对于广义离散随机线性系统 ,提出了降阶Wiener状态估值器 ,可统一处理滤波、平滑和预报问题 ,并且能减少计算负担 .仿真例子说明了算法的有效性     

Networked H∞ filtering for linear discrete-time systems

This paper studies the problem of networked H_∞ filtering for linear discrete-time systems. A new model is proposed as the filtering error system to simultaneously capture the communication constraint, random packet dropout and quantization effects in the networked systems. A sufficient condition is presented for the filtering error system to be mean square exponentially stable with a prescribed H_∞ performance by employing the multiple Lyapunov function method. The obtained condition depends on some parameters of the networked systems, such as the access sequence of nodes, packet dropout rate and quantization density. With these parameters fixed, a design procedure for the desired H_∞ filter is also presented based on the derived condition. Finally, an illustrative example is utilized to show the effectiveness of the proposed method.     

Set-membership filtering for polytopic uncertain discrete-time systems

In this paper, a set-membership filtering problem is considered for systems with polytopic uncertainty. A recursive algorithm for calculating an ellipsoid which always contains the state is developed. In the prediction step, a predicted state ellipsoid is determined; in the update step, a state estimation ellipsoid is computed by combining the predicted state ellipsoid and the set of states compatible with the measurement equation. A smallest possible estimate set is calculated recursively by solving the semi-definite programming problems. Hence, the proposed set-membership filter relies on a two-step prediction–correction structure, which is similar to the Kalman filter. Simulation results are provided to demonstrate the effectiveness of the proposed method.     

How non-zero initial conditions affect the minimality of linear discrete-time systems

From the state-space approach to linear systems, promoted by Kalman, we learned that minimality is equivalent with reachability together with observability. Our past research on optimal reduced-order LQG controller synthesis revealed that if the initial conditions are non-zero, minimality is no longer equivalent with reachability together with observability. In the behavioural approach to linear systems promoted by Willems, that consider systems as exclusion laws, minimality is equivalent with observability. This article describes and explains in detail these apparently fundamental differences. Out of the discussion, the system properties weak reachability or excitability, and the dual property weak observability emerge. Weak reachability is weaker than reachability and becomes identical only if the initial conditions are empty or zero. Weak reachability together with observability is equivalent with minimality. Taking the behavioural systems point of view, minimality becomes equivalent with observability when the linear system is time invariant. This article also reveals the precise influence of a possibly stochastic initial state on the dimension of a minimal realisation. The issues raised in this article become especially apparent if linear time-varying systems (controllers) with time-varying dimensions are considered. Systems with time-varying dimensions play a major role in the realisation theory of computer algorithms. Moreover, they provide minimal realisations with smaller dimensions. Therefore, the results of this article are of practical importance for the minimal realisation of discrete-time (digital) controllers and computer algorithms with non-zero initial conditions. Theoretically, the results of this article generalise the minimality property to linear systems with time-varying dimensions and non-zero initial conditions.     

Receding-horizon estimation for discrete-time linear systems

The problem of estimating the state of a discrete-time linear system can be addressed by minimizing an estimation cost function dependent on a batch of recent measure and input vectors. This problem has been solved by introducing a receding-horizon objective function that includes also a weighted penalty term related to the prediction of the state. For such an estimator, convergence results and unbiasedness properties have been proved. The issues concerning the design of this filter are discussed in terms of the choice of the free parameters in the cost function. The performance of the proposed receding-horizon filter is evaluated and compared with other techniques by means of a numerical example.     

Reduced-order observers for linear discrete-time systems

Luenberger's observer is considered as an alternate to the Kalman filter for obtaining state estimates in linear discrete-time stochastic systems. An interesting new solution to the problem of constructing optimal and suboptimal reduced-order observers is presented. The solution contains as special cases both Kalman's optimal filter and the optimal minimal-order observer of Leondes and Novak. Also, the Tse and Athans observer is obtained as a special case of the reduced-order observer solution.     

Optimal quadratic filtering of linear discrete-time non-Gaussiansystems

In this work, the state estimation problem for linear discrete-time systems with non-Gaussian state and output noises is dealt with. By following a geometric approach, an optimal recursive second-order polynomial estimate is proposed, which actually improves the widely used optimal linear one     

Implicit linear discrete-time systems

This paper studies various properties of implicit linear discrete-time systems given by a linear difference equationEx _k+1 =Fx _k +Gu _k . The topics considered include a basic characterization of these subspaces which describe acceptance of all input sequences, the uniqueness property and regularity, and the notion of controllability. This work was performed under the auspices of Fund RP.I.02: Teoria sterowania i optymalizacji ciągłych układów dynamicznych i procesów dyskretnych.     

Quasi-equality constrained risk-sensitive filtering for nonlinear discrete-time systems

More and more data fusion models contain state constraints with valuable information in the filtering process.In this study,an optimal filter of risk-sensitive with quasi-equality constraints is formul...     

Risk-sensitive filtering for discrete-time systems with time-varying delay

In this paper, the risk-sensitive filtering problem with time-varying delay is investigated. The problem is transformed into Krein space as an equivalent optimisation problem. The observations with time-varying delays are restructured as ones with multiple constant delays by defining a binary variable model with respect to the arrival process of observations, containing the same state information as the original. Finally, the reorganised innovation analysis approach in Krein space allows the solution to the proposed risk-sensitive filtering in terms of the solutions to Riccati and matrix difference equations.     

Model order-reduction for discrete-time switched linear systems

This article describes the problem of model order-reduction for a class of hybrid discrete-time switched linear systems composed of linear discrete-time invariant subsystems with a switching rule. This article investigates two novel approaches to model order-reduction. The first approach consists in evaluating the error approximation performance; the problems are solved using the robust stability results of the switched systems. The second approach presents the reachability and observability Gramians of the switched systems, which allows a balanced truncation model reduction procedure. A numerical example shows the effectiveness of the proposed approaches.