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.     

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.     

Robust finite horizon minimax filtering for discrete-time stochastic uncertain systems

We study a finite-horizon robust minimax filtering problem for time-varying discrete-time stochastic uncertain systems. The uncertainty in the system is characterized by a set of probability measures under which the stochastic noises, driving the system, are defined. The optimal minimax filter has been found by applying techniques of risk-sensitive LQG control. The structure and properties of resulting filter are analyzed and compared to H_∞ and Kalman filters.     

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 filtering for networked systems with stochastic sensor gain degradation

In this paper, the optimal filtering problem is investigated for a class of networked systems in the presence of stochastic sensor gain degradations. The degradations are described by sequences of random variables with known statistics. A new measurement model is put forward to account for sensor gain degradations, network-induced time delays as well as network-induced data dropouts. Based on the proposed new model, an optimal unbiased filter is designed that minimizes the filtering error variance at each time-step. The developed filtering algorithm is recursive and therefore suitable for online application. Moreover, both currently and previously received signals are utilized to estimate the current state in order to achieve a better accuracy. A numerical simulation is exploited to illustrate the effectiveness of the proposed algorithm.     

Optimal nonlinear estimation for a class of discrete-time stochastic systems

Recursive estimation for nonlinear discrete-time stochastic systems with additive white Gaussian observation noise is investigated. It is proved that for certain classes of systems, described either by finite Volterra series expansions or by state-linear realizations under certain algebraic conditions, the optimal conditional mean estimator is recursive and of fixed finite dimension. An example is presented to illustrate the structure of the estimators.     

Comments on: Optimal stochastic control for discrete-time linear systems with interrupted observations

In a recent paper [1] a control law was found, which was claimed to be optimal. Here we point out an error in the proof and give a counterexample. In fact, the control law in [1] can be seen as a “passive open loop approximation”. We suggest also a control law which actively identifies the random variables in the loop.     

Optimal linear filtering for systems of stochastic differential equations with poisson perturbations

The Kalman–Bucy filter that can be modeled by computer statistical design is constructed for stochastic dynamic systems with Poisson perturbations. It is proved that a stationary filter coincides with the Wiener filter for the optimal mean-square filtering of stationary sequences in the absence of Poisson perturbations.     

Stabilization of discrete-time systems with stochastic parameters

This letter presents a solution to the problem of stabilization by a constant feedback of a linear system whose system and control matrices are multiplied by scalar correlated noise sequences.     

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     

Control of discrete-time hybrid stochastic systems

A realistic stochastic control problem for hybrid systems with Markovian jump parameters can have switching parameters in both the state and the measurement equations. Furthermore, both the `base' and jump states, in general, are not perfectly observed. There are only two existing controllers for this problem, both with complexity exponentially increasing with time. The authors present another control algorithm for stochastic systems with Markovian jump parameters. This algorithm is derived through the use of stochastic dynamic programming and is designed to be used for realistic stochastic control problems, i.e., with noisy state observations. This scheme has fixed computational requirements at each stage and a natural parallel implementation. Simulation results are used to compare the algorithm with previous schemes     

Optimal control of discrete-time systems with time-lag controls

Various optimal control problems for discrete-time systems with time-lag controls are discussed. Some of the basic features of this type of system are noted. A simple example is given for illustrative purpose.     

Polynomial filtering of discrete-time stochastic linear systemswith multiplicative state noise

The problem of finding an optimal polynomial state estimate for the class of stochastic linear models with a multiplicative state noise term is studied. For such models, a technique of state augmentation is used, leading to the definition of a general polynomial filter. The theory is developed for time-varying systems with nonstationary and non-Gaussian noises. Moreover, the steady-state polynomial filter for stationary systems is also studied. Numerical simulations show the high performances of the proposed method with respect to the classical linear filtering techniques     

Optimal filtering for systems with unknown inputs

An optimal filtering formula is derived for linear time-varying discrete systems with unknown inputs. By making use of the well-known innovations filtering technique, the derivation is an extension of a new observer design method for time-invariant deterministic systems with unknown inputs. The systems under consideration have the most general form. The derived optimal filter has a similar form to the standard Kalman filter with some modified covariance and gain matrices     

Minimax control for discrete-time time-varying stochastic systems

This paper gives a self-contained presentation of minimax control for discrete-time time-varying stochastic systems under finite- and infinite-horizon expected total cost performance criteria. Suitable conditions for the existence of minimax strategies are proposed. Also, we prove that the values of the finite-horizon problem converge to the values of the infinite-horizon problems. Moreover, for finite-horizon problems an algorithm of calculation of minimax strategies is developed and tested by using time-varying stochastic systems.     

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.