Metastability for nonlinear random perturbations of dynamical systems

In this paper we describe the long time behavior of solutions to quasi-linear parabolic equations with a small parameter at the second order term and the long time behavior of the corresponding diffusion processes.     

Equations for the continuous estimation of the perturbations of dynamical systems

A dynamical system acted upon by external perturbations is considered. It is assumed that the phase state of the system (or a part of it) is observed with certain errors. The problem is to construct differential equations for estimating (reconstructing) the perturbations using measurement data. Unlike in papers in which cases of discrete instants of the observations are analysed, the continuous case is considered for which differential equations of an auxiliary system are derived, the controls in which are approximations of an unknown input. The general constructions are illustrated by means of an example.     

The inverse problem for small random perturbations of dynamical systems

What can one get to know about the dynamical system from its small random perturbation? What can one say about solutions of an ordinary differential equation \(\dot x_1 = B(x_1 )\) having some information on its singular perturbation operatorL ^?=?L+(B,?) withL being an elliptic second order operator? These problems are studied in the paper.     

On stability and stabilization of some discrete dynamical systems

The paper formulates effective and nonimprovable stability conditions for a linear difference system involving 2 integer delays. The used technique combines algorithm of the discrete D‐decomposition method with some procedures of the polynomial theory. Contrary to the related existing results, the derived conditions are fully explicit with respect to both delays, which enables their simple applicability in various scientific and engineering areas. As an illustration, we show their importance in delayed feedback controls of discrete dynamical systems, with a particular emphasis put on stabilization of unstable steady states of the discrete logistic map.     

Linear stabilization of the programmed motions of non-linear controlled dynamical systems under parametric perturbations

A non-linear controlled dynamical system that describes the dynamics of a broad class of non-linear mechanical and electromechanical systems (in particular, electromechanical robot manipulators) is considered. It is proposed that the real parameter vector of a non-linear controlled dynamical system belongs to an assigned (admissible) constrained closed set and is assumed to be unknown. The programmed motion of the non-linear controlled dynamical system and the programmed control that produces it are assigned (constructed) by using an estimate, that is, the nominal value of the parameter vector of the non-linear controlled dynamical system, which differs from its actual value. A procedure for synthesizing stabilizing control laws with linear feedback with respect to the state that ensure stabilization of the programmed motions of the non-linear controlled dynamical system under parametric perturbations is proposed. A non-singular linear transformation of the coordinates of the state space that transforms the original non-linear controlled dynamical system in deviations (from the programmed motion and programmed control) into a certain non-linear controlled dynamical system of special form, which is convenient for analysing and synthesizing laws for controlling the motion of the system, is constructed. A certain non-linear controlled dynamical system of canonical form is derived in the original non-linear controlled dynamical system in deviations. The transformation of the coordinates of the state space constructed and the Lyapunov function methodology are used to synthesize stabilizing control laws with linear feedback with respect to the state, which ensure asymptotic stability as a whole of the equilibrium position of the non-linear controlled dynamical system of canonical form and dissipativity “in the large” of the non-linear controlled dynamical system of special form and of the original non-linear controlled dynamical system in deviations. In the control laws synthesized, the formulae for the elements of their matrices of the feedback loop gains do not depend on the real parameter vector of the non-linear controlled dynamical system, and they depend solely on the constants from certain estimates that hold for all of its possible values from an assigned set. Estimates of the region of dissipativity “in the large” of the non-linear controlled dynamical system of special form and the original non-linear controlled dynamical system in deviations closed by the stabilizing control laws synthesized are given, and estimates for their limit sets and regions of attraction are presented.     

Dimension of invariant measures for continuous random dynamical systems

We consider continuous random dynamical systems with jumps. We estimate the dimension of the invariant measures and apply the results to a model of stochastic gene expression. Copyright © 2016 John Wiley & Sons, Ltd.     

Relationships among some chaotic properties of non-autonomous discrete dynamical systems

This paper is concerned with relationships among some chaotic properties of non-autonomous discrete dynamical systems. Some relationships among weak mixing, topologically weak mixing, generic chaos, dense chaos, and sensitivity are investigated. In addition, some equivalent conditions of sensitivity are given and the relationships between sensitivity and Li–Yorke sensitivity are obtained. These results generalize some existing results of autonomous discrete systems, some of which relax the corresponding conditions.     

Optimal parametric synthesis of continuous/discrete stochastic terminal control systems

We propose an efficient numerical algorithm for computer parametric synthesis of linear continuous/discrete stochastic dynamic terminal control systems. The algorithm is based on the application of the method of inverse-conjugate systems and conjugate variables. Translated fromDinamicheskie Sistemy, Vol. 11, 1992.     

Entropy formula of Pesin type for noninvertible random dynamical systems

In this paper we consider random dynamical systems (abbreviated henceforth as RDS's) generated by compositions of random endomorphisms (maybe noninvertible and with singularities) of class of a compact manifold. Entropy formula of Pesin type is proved for such RDS's under some absolute continuity conditions on the associated invariant measures. Received October 17, 1997; in final form January 5, 1998     

On the predictability of discrete dynamical systems

Let be a metric space. A function is said to be non-sensitive at a point if for every 0$"> there is a 0$"> such that for any choice of points , , , we have that for every . Let be the set of all homeomorphisms from onto endowed with the topology of uniform convergence. The main goal of the present paper is to prove that for certain spaces , ``most' functions in are non-sensitive at ``most' points of .

     

The exit problem for small random perturbations of dynamical systems with a hyperbolic fixed point

We consider the Markov diffusion process ξ^∈(t), transforming when ɛ=0 into the solution of an ordinary differential equation with a turning point ℴ of the hyperbolic type. The asymptotic behevior as ɛ→0 of the exit time, of its expectation of the probability distribution of exit points for the process ξ^∈(t) is studied. These indicate also the asymptotic behavior of solutions of the corresponding singularly perturbed elliptic boundary value problems.     

Modelling of uncertainty in discrete dynamical systems

Dynamic systems that are not Gaussian, stationary and linear are difficult to model by full probabilistic analysis. Sufficient information for practical application can often be obtained by second moment analysis, described in the paper. Alternatively, second moment analysis can be performed using point distributions. Two new methods in this class, one exact for linear systems and one approximate, are described. Examples show the application and illustrate the accuracy.     

On the predictability of discrete dynamical systems III

Given an integer n?2, a metrizable compact topological n-manifold X with boundary and a finite positive Borel measure μ on X, we prove that “most” homeomorphisms are non-sensitive μ-almost everywhere on X. Moreover, we also prove that for “most” homeomorphisms the non-wandering set Ωf has μ-measure zero.     

On the predictability of discrete dynamical systems II

Given a metrizable compact topological -manifold with boundary and a metric compatible with the topology of , we prove that ``most' continuous functions are non-sensitive at ``most' points of but are sensitive at every point of an infinite set which is dense in the set of all periodic points of . We also establish some results concerning sets of periodic points and non-wandering points.

     

Qualitative analysis of the influence of random perturbations of “white-noise” type applied along the vector of phase velocity on a harmonic oscillator with friction

We consider representations in the phase plane for the harmonic oscillator with friction under random perturbations applied along the vector of phase velocity. We investigate the behavior of the amplitude, phase, and total energy of the damped oscillator.     

An exact minimum variance filter for a class of discrete time systems with random parameter perturbations

An exact, closed-form minimum variance filter is designed for a class of discrete time uncertain systems which allows for both multiplicative and additive noise sources. The multiplicative noise model includes a popular class of models (Cox-Ingersoll-Ross type models) in econometrics. The parameters of the system under consideration which describe the state transition are assumed to be subject to stochastic uncertainties. The problem addressed is the design of a filter that minimizes the trace of the estimation error variance. Sensitivity of the new filter to the size of parameter uncertainty, in terms of the variance of parameter perturbations, is also considered. We refer to the new filter as the ‘perturbed Kalman filter’ (PKF) since it reduces to the traditional (or unperturbed) Kalman filter as the size of stochastic perturbation approaches zero. We also consider a related approximate filtering heuristic for univariate time series and we refer to filter based on this heuristic as approximate perturbed Kalman filter (APKF). We test the performance of our new filters on three simulated numerical examples and compare the results with unperturbed Kalman filter that ignores the uncertainty in the transition equation. Through numerical examples, PKF and APKF are shown to outperform the traditional (or unperturbed) Kalman filter in terms of the size of the estimation error when stochastic uncertainties are present, even when the size of stochastic uncertainty is inaccurately identified.