Design of recursive fixed-point smoother using covariance information in linear discrete-time systems

A recursive fixed-point smoother and filter, for linear discrete-time systems, are designed using the observed value and the covariance information of signal and observation noise for white gaussian and white gaussian plus coloured observation noise.     

New design of linear discrete-time predictor using covariance information

A discrete one-stage predictor algorithm using covariance information in linear systems is derived. The algorithm is obtained for white Gaussian observation noise. The signal is a nonstationary or stationary stochastic process. The auto-covariance function of the signal is expressed using a semi-degenerate kernel of discrete-time systems. The semi-degenerate kernel can represent general covariance functions of random processes by a finite sum of nonrandom functions.     

All covariance controllers for linear discrete-time systems

The authors characterize the set of covariances that a linear discrete-time plant with a specified-order controller can have. The controllers that assign such covariances to any linear discrete-time system are given explicitly in closed form. The freedom in these covariance controllers is explicit and is parameterized by two orthogonal matrices. By appropriately choosing these free parameters, additional system objectives can be achieved without altering the state covariance, and the stability of the closed-loop system is guaranteed     

An alternative FIR filter for state estimation in discrete-time systems

To overcome the resulting problems of existing finite impulse response (FIR) structure filters, this paper proposes an alternative FIR filter for state estimation in discrete-time systems, which is derived from the well-known Kalman filter with recursive infinite impulse response (IIR) structure. The proposed FIR filter obtains a posteriori knowledge about the window initial condition from the most recent finite observations, while existing FIR filters handle this task arbitrarily or heuristically. The gain matrix for the proposed FIR filter incorporates a posteriori knowledge about the window initial condition during its design and is shown to be time-invariant. The proposed FIR filter is shown to have good inherent properties such as unbiasedness and deadbeat. Through extensive computer simulations, the proposed FIR filter can be shown to be comparable with the Kalman filter for the nominal system and better than that for the temporarily uncertain system.     

A new prediction algorithm using covariance information in linear continuous systems

This paper proposes a new type of predictor, which uses the covariance information of the signal and the observation noise, for white Gaussian plus colored observation noise in linear continuous stochastic systems. This method has the advantage that one need not assume that the state-space model is known as prior necessary information. A numerical simulation example shows that the presented prediction algorithm is feasible.     

Relation between filter using covariance information and Kalman filter

This paper presents sequential algorithms for the optimal impulse function, Kalman gain and the error variance in linear least squares filtering problems, when the autocovariance function of the signal is given in the form of a semi-degenerate kernel, and the additive observation noise in white Gaussian. A digital simulation result indicates that the algorithms presented in this paper are feasible, and that the values of Kalman gain and the error variance calculated by these algorithms approach to those obtained by the Kalman filter theory, for time sufficiently large.     

Optimal timing control of discrete-time linear switched stochastic systems

Optimal switch-time control is the study that investigates how best to switch between different modes. In this paper, we investigate the optimal switch-time control problem for discrete-time linear switched stochastic systems. In particular, under the assumption that the sequence of active subsystems is pre-specified, we focus on the problem where the objective is to minimize a cost functional defined on the states and the switching times are the only control variables. For systems with one switching time, using calculus of variations, we firstly derive the difference formulae of the cost functional with respect to the switching time, which can be directly used to find the optimal switching instant. Then, a method is presented to deal with the problem with multiple switching times case. Finally, the viability of the proposed method is illustrated through two numerical examples.     

Optimal estimation of linear discrete-time systems with stochastic parameters

This paper considers optimal linear state estimation in the general case of linear discrete-time systems with stochastic parameters which are statistically independent with respect to time. The estimator is derived by transforming the system to one with deterministic parameters and state dependent additive system and observation noise. It is shown that mean square stability of the system is a sufficient and almost necessary condition for the existence, uniqueness and stability of the time invariant estimator.     

A maximum-likelihood Kalman filter for switching discrete-time linear systems

State estimation is addressed for a class of discrete-time systems that may switch among different modes taken from a finite set. The system and measurement equations of each mode are assumed to be linear and perfectly known, but the current mode of the system is unknown. Moreover, additive, independent, normally distributed noises are assumed to affect the dynamics and the measurements. First, relying on a well-established notion of mode observability developed “ad hoc” for switching systems, an approach to system mode estimation based on a maximum-likelihood criterion is proposed. Second, such a mode estimator is embedded in a Kalman filtering framework to estimate the continuous state. Under the unique assumption of mode observability, stability properties in terms of boundedness of the mean square estimation error are proved for the resulting filter. Simulation results showing the effectiveness of the proposed filter are reported.     

Viability decision of linear discrete-time stochastic systems with probability criterion

In this paper, the optimal viability decision problem of linear discrete-time stochastic systems with probability criterion is investigated. Under the condition of sequence-reachable discrete-time dynamic systems, the existence theorem of optimal viability strategy is given and the solving procedure of the optimal strategy is provided based on dynamic programming. A numerical example shows the effectiveness of the proposed methods.     

Control of linear discrete-time stochastic dynamic systems with multiplicative disturbances

Multiplicative random disturbances frequently occur in economic modeling. The money multiplier in a simple monetary macroeconomic model is treated as a random variable in this paper. The optimal control law is derived, and some consequences of erroneous modeling of the random disturbance are exhibited by simulation.     

具有乘性噪声的线性离散时间随机控制系统综述

具有乘性噪声的随机不确定系统的控制问题有着广泛的应用背景. 本文概述了具有乘性噪声的线性离散时间随机系统的稳定性分析、均方镇定、最优控制以及最优估计问题和相关结论. 同时, 本文研究了具有状态与控制乘性噪声的线性多变量离散时间系统的均方镇定和最优控制问题, 分析了这两个问题之间的联系, 并讨论了最优状态反馈控制器的设计算法.     

Infinite horizon linear quadratic differential games for discrete-time stochastic systems

This paper deals with the infinite horizon linear quadratic(LQ)differential games for discrete-time stochastic systems with both state and control dependent noise.The Popov-Belevitch-Hautus(PBH)criteria for exact observability and exact detectability of discrete-time stochastic systems are presented.By means of them,we give the optimal strategies (Nash equilibrium strategies)and the optimal cost values for infinite horizon stochastic differential games.It indicates that the infinite horizon LQ stochastic differential games are associated with four coupled matrix-valued equations.Furthermore, an iterative algorithm is proposed to solve the four coupled equations.Finally,an example is given to demonstrate our results.     

Infinite horizon indefinite stochastic linear quadratic control for discrete-time systems

This paper discusses discrete-time stochastic linear quadratic (LQ) problem in the infinite horizon with state and control dependent noise, where the weighting matrices in the cost function are assumed to be indefinite. The problem gives rise to a generalized algebraic Riccati equation (GARE) that involves equality and inequality constraints. The well-posedness of the indefinite LQ problem is shown to be equivalent to the feasibility of a linear matrix inequality (LMI). Moreover, the existence of a stabilizing solution to the GARE is equivalent to the attainability of the LQ problem. All the optimal controls are obtained in terms of the solution to the GARE. Finally, we give an LMI -based approach to solve the GARE via a semidefinite programming.     

Sliding mode control-based linear functional observers for discrete-time stochastic systems

Sliding mode control (SMC) is one of the most popular techniques to stabilise linear discrete-time stochastic systems. However, application of SMC becomes difficult when the system states are not available for feedback. This paper presents a new approach to design a SMC-based functional observer for discrete-time stochastic systems. The functional observer is based on the Kronecker product approach. Existence conditions and stability analysis of the proposed observer are given. The control input is estimated by a novel linear functional observer. This approach leads to a non-switching type of control, thereby eliminating the fundamental cause of chatter. Furthermore, the functional observer is designed in such a way that the effect of process and measurement noise is minimised. Simulation example is given to illustrate and validate the proposed design method.     

Structure of stationary finite observation records of discrete-time stochastic linear systems

We discuss the structural features of finite observation records from discrete-time stationary Gaussian stochastic processes with rational power spectra. The processes may be viewed as arising from discrete-time linear systems excited by white noise or as autoregressive-moving average processes. The latter parametrization is chosen for convenience, and the existence of nontrivial sufficient statistics is studied. It is shown that only autoregressive processes have sufficient statistics whose dimension is less that the number of observations. Some connections with the theory of stochastic realization are described.     

Optimal control of discrete-time bilinear systems with applications to switched linear stochastic systems

This paper aims at characterizing the most destabilizing switching law for discrete-time switched systems governed by a set of bounded linear operators. The switched system is embedded in a special class of discrete-time bilinear control systems. This allows us to apply the variational approach to the bilinear control system associated with a Mayer-type optimal control problem, and a second-order necessary optimality condition is derived. Optimal equivalence between the bilinear system and the switched system is analyzed, which shows that any optimal control law can be equivalently expressed as a switching law. This specific switching law is most unstable for the switched system, and thus can be used to determine stability under arbitrary switching. Based on the second-order moment of the state, the proposed approach is applied to analyze uniform mean-square stability of discrete-time switched linear stochastic systems. Numerical simulations are presented to verify the usefulness of the theoretic results.     

Neural-network-based stochastic linear quadratic optimal tracking control scheme for unknown discrete-time systems using adaptive dynamic programming

In this paper, a stochastic linear quadratic optimal tracking scheme is proposed for unknown linear discrete-time (DT) systems based on adaptive dynamic programming (ADP) algorithm. First, an augmented system composed of the original system and the command generator is constructed and then an augmented stochastic algebraic equation is derived based on the augmented system. Next, to obtain the optimal control strategy, the stochastic case is converted into the deterministic one by system transformation, and then an ADP algorithm is proposed with convergence analysis. For the purpose of realizing the ADP algorithm, three back propagation neural networks including model network, critic network and action network are devised to guarantee unknown system model, optimal value function and optimal control strategy, respectively. Finally, the obtained optimal control strategy is applied to the original stochastic system, and two simulations are provided to demonstrate the effectiveness of the proposed algorithm.     

Design of low-order delayed-measurement observers for discrete-time linear systems

This paper considers the development of a discrete-time low-order delayed-measurement observer for a discrete time-invariant linear system. The observer has several unique features. It uses discrete-time delayed measurements as part of its inputs and it is an rth-order observer for an nth-order linear system with q linearly dependent outputs, where r <n ? q. The purpose of this observer is to implement the control directly without estimating the state first. It is shown that under certain conditions the dimension of the observer is much lower than the standard observer of dimension n?q. The procedure employed here presents an approach which can be very important in large-scale control implementation.