Nonlinear observer for autonomous switching systems with jumps

This work deals with nonlinear observer synthesis for a particular class of Hybrid Dynamic Systems (HDS): autonomous switching systems with jumps. The jumps can result from the system’s dynamics or from the diffeomorphism which makes it possible to lead the system to an observability canonical form. The contribution of this work relates to the design of a second order sliding mode based observer (“Super Twisting Algorithm”). It allows estimating both continuous and discrete states related to the system active dynamic. On the other hand these observers ensure a finite time convergence of the estimation error.     

Backward stochastic partial differential equations with jumps and application to optimal control of random jump fields

We prove an existence and uniqueness result for a general class of backward stochastic partial differential equations (SPDE) with jumps. This is a type of equations, which appear as adjoint equations in the maximum principle approach to optimal control of systems described by SPDE driven by Lévy processes.     

On a classification of dynamic systems subject to noise

A classification of alternative mathematical schemes which determine possible impositions of noise on dynamic systems either of a continuous or a discrete formulation in time is presented. We refer to the results obtained concerning the influence of noise on the correlation dimension and on the largest Lyapunov exponent. A method of constructing models for effecting predictions of time series with a finite correlation dimension is presented. Numerical results concerning the influence of various level of additive noise to the Henon attractor are also provided.     

Stability with general decay rates of stochastic differential delay equations with Poisson jumps and Markovian switching

In this paper, some criteria on pth moment stability and almost sure stability with general decay rates of stochastic differential delay equations with Poisson jumps and Markovian switching are obtained. Two examples are presented to illustrate our theories.     

Numerical methods for controls for nonlinear stochastic systems with delays and jumps: applications to admission control

We consider numerical methods of the Markov chain approximation type for computing optimal controls and value functions for systems governed by nonlinear stochastic delay equations. Earlier work did not allow Poisson random measure driving processes or delays that are concentrated on points with positive probability. In addition, the Poisson measures can be controlled. Previous proofs are not adequate for the present case. The algorithms are developed and convergence proved as the approximating parameters go to their limits. One motivating example concerns admissions control to a network, where the file arrival process is governed by a Poisson process, and arrivals might be admitted or not, according to the control, which leads to a controlled Poisson process. Numerical data for such an example are presented. The original problem is recast in terms of a transportation equation, which allows the development of practical algorithms. For the problems of interest, alternative methods can entail prohibitive memory and computational requirements.     

Numerical solutions for optimal control of stochastic Kolmogorov systems with regime-switching and random jumps

Statistical Inference for Stochastic Processes - This work is devoted to numerical solutions of controlled stochastic Kolmogorov systems with regime switching and random jumps. Markov chain...     

Averaging in hyperbolic systems subject to weakly dependent random perturbations

The first initial boundary value problem is considered for a hyperbolic equation with a small parameter for an external action described by some stochastic process satisfying some of the conditions of weak dependence. Averaging of the coefficients over the temporal variable is conducted. The existence is assumed of a unique generalized solution both for the initial stochastic problem and for the problem with an averaged equation, which turns out to be deterministic. For the probability of deviation of a solution of the initial equation from the solution of the averaged problem, exponential bounds are established of the type of S. N. Bernshtein inequalities for the sums of independent random variables.Translated from Ukrainskii Matematicheskii Zhurnal, Vol. 44, No. 8, pp. 1011–1020, August, 1992.     

Parameter estimation for partially observable systems subject to random failure

In this paper, we present a parameter estimation procedure for a condition‐based maintenance model under partial observations. Systems can be in a healthy or unhealthy operational state, or in a failure state. System deterioration is driven by a continuous time homogeneous Markov chain and the system state is unobservable, except the failure state. Vector information that is stochastically related to the system state is obtained through condition monitoring at equidistant sampling times. Two types of data histories are available — data histories that end with observable failure, and censored data histories that end when the system has been suspended from operation but has not failed. The state and observation processes are modeled in the hidden Markov framework and the model parameters are estimated using the expectation–maximization algorithm. We show that both the pseudolikelihood function and the parameter updates in each iteration of the expectation–maximization algorithm have explicit formulas. A numerical example is developed using real multivariate spectrometric oil data coming from the failing transmission units of 240‐ton heavy hauler trucks used in the Athabasca oil sands of Alberta, Canada. Copyright © 2012 John Wiley & Sons, Ltd.     

Optimal control for infinite dimensional stochastic differential equations with infinite Markov jumps and multiplicative noise

In this paper we solve an infinite-horizon linear quadratic control problem for a class of differential equations with countably infinite Markov jumps and multiplicative noise. The global solvability of the associated differential Riccati-type equations is studied under detectability hypotheses. A nonstochastic, operatorial approach is used. Some properties of the linear stochastic systems, such as stability, stabilizability and detectability, are also discussed on the basis of a new solution representation result. A generalized Ito's formula which applies to infinite dimensional stochastic differential equations with countably infinite Markov jumps is also provided.