Discrete Dynamics in Nature and Society

Volume 2015, Article ID 658048, 5 pages

http://dx.doi.org/10.1155/2015/658048

## Controlling the Stochastic Sensitivity in Nonlinear Discrete-Time Systems with Incomplete Information

Ural Federal University, Lenina, 51, Ekaterinburg 620000, Russia

Received 19 April 2015; Revised 13 September 2015; Accepted 16 September 2015

Academic Editor: Zhan Zhou

Copyright © 2015 Lev Ryashko and Irina Bashkirtseva. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

#### Abstract

For stochastic nonlinear discrete-time system with incomplete information, a problem of the stabilization of equilibrium is considered. Our approach uses a regulator which synthesizes the required stochastic sensitivity. Mathematically, this problem is reduced to the solution of some quadratic matrix equations. A description of attainability sets and algorithms for regulators design is given. The general results are applied to the suppression of unwanted large-amplitude oscillations around the equilibria of the stochastically forced Verhulst model with noisy observations.

#### 1. Introduction

Controlling of the complex systems in nature and society is a challenging and fundamental problem of the modern mathematical theory of nonlinear dynamics and engineering. Discrete dynamic models because of widely used computer-oriented technologies, attract attention of many researchers [1, 2]. Even in simple discrete models, due to nonlinearity, a variety of dynamic regimes, both regular and chaotic, is observed [3–5]. An interplay of nonlinearity and stochasticity can generate new unexpected phenomena [6–10].

A lot of nonlinear systems operate in zones of stochastic transitions from order to chaos. After the pioneering work [11], a problem of controlling chaos is extensively studied [12–14]. Most of the reported results are based on the direct numerical simulation. A detailed theoretical description of stochastic attractor is given by the stationary probabilistic density function. For discrete systems, this function is governed by Frobenius-Perron equation [15]. Unfortunately, an analytical solution of this equation is possible only in very special cases, so, a development of the asymptotic approximations is a highly relevant area of research.

For the constructive analysis of the stochastic attractors of nonlinear discrete-time dynamical systems, a stochastic sensitivity functions technique was elaborated [16]. This technique was applied to the analysis on noise-induced intermittency [17] and neuron excitability [18]. On the base of this technique, a new approach for the solution of control problems in stochastic discrete-time systems was suggested in [19]. In these studies, it was supposed that the complete information about current state of the controlled system is known. However, in many practical situations, the system data are far from complete. For example, only some coordinates of the system state are observable, and, moreover, these observations contain stochastic errors. So, the control of stochastic systems with incomplete information is an urgent research domain [20–22].

In present paper, we further develop a theory for the synthesis of the stochastic sensitivity for the equilibria in a randomly forced control discrete system with incomplete information. Mathematically, presence of noise in the observations leads to a new algebraic analysis of quadratic matrix equations. In Section 2, we introduce the stochastic sensitivity matrix as a basic probabilistic characteristics for the randomly forced equilibria. A problem of the synthesis of this matrix is considered. A important notion of the attainability is discussed here. A problem of the stochastic sensitivity matrix synthesis is reduced to the analysis of the corresponding quadratic matrix equation. Results of this theoretical analysis in the general multidimensional case are presented in a Theorem. This Theorem gives a description of attainability sets and algorithms for regulators design.

One-dimensional case is discussed in details in Section 3. In Section 4, we apply the results to the suppression of unwanted large-amplitude oscillations around the equilibria of the stochastically forced Verhulst model with noisy observations. We show that our regulator can be used for the suppression of chaos.

#### 2. Synthesis of Stochastic Sensitivity

Consider a nonlinear controlled discrete-time stochastic systemwhere , , , and is a control input. Here, is an uncorrelated random sequence with parameters and , and is the identity -matrix and is a scalar parameter of noise intensity.

It is supposed that the corresponding deterministic uncontrolled system (1) (with and therein) has an equilibrium . Stability of is not assumed.

In present paper, we consider a case of incomplete information when the measurement vector is known only:where , . Here, is an uncorrelated random sequence with parameters and , and is the identity -matrix.

In this circumstance, we consider the following regulator:The dynamics of the closed-loop stochastic system (1) with the regulator (3) using noisy observations (2) is governed by the following system:For the asymptotics of the deviations of solutions of system (4) from the equilibrium , the following stochastic system can be written:where Due to the uncorrelatedness of random terms and , the second moments matrix is governed by the equationwhere , . A set of matrices that provide an exponential stability to the equilibrium of the closed deterministic system (4) (with therein) has the following form: where is a spectral radius of the matrix . We suppose that the set is not empty.

For any , (7) has a unique stable stationary solution satisfying the equationThis matrix is called the stochastic sensitivity matrix of the equilibrium for system (4). The stochastic sensitivity matrix approximates a limit behavior of the second moments for deviations of solutions from : So, the matrix characterizes a dispersion of the stationary distributed random states of system (4) around the equilibrium .

For any , the regulator (3) forms a corresponding stochastic equilibrium of system (4) with the stochastic sensitivity matrix which is a solution of (9).

Consider further the following inverse problem.

*Problem of Stochastic Sensitivity Synthesis.* Let be a set of symmetric and positive-definite -matrices. Let be some assigned matrix. The problem is to find a feedback matrix of regulator (3) such that the equality holds. Here, is a solution of (9).

In some cases, this problem can be unsolvable. Therefore, we consider an important notion of the attainability.

*Definition 1. *An element is said to be attainable for system (4) if the equality holds for some .

*Definition 2. *The set of all attainable elements, is called the attainability set for system (4).

As it follows from (9), the attainability analysis is reduced to the study of solvability of the quadratic matrix equation:Rewrite (12) with respect to a new unknown matrixin the following form:Denote . Suppose that the matrix is positive-definite . A substitution transforms (14) into the following equation:where A necessary condition of (15) solvability is in the nonnegative definiteness of the matrix :Let condition (17) be fulfilled. Then, quadratic equation (15) is equivalent to the linear equationwhere is an arbitrary orthogonal -matrix. It follows from (18) that the feedback matrix of the regulator (3) which synthesizes the stochastic sensitivity matrix , satisfies to the linear matrix equationIn the following theorem, we summarize our theoretical results.

Theorem 3. *Let noises in system (1) and observations (2) be nonsingular (, ).**(a) If the matrix is quadratic and nonsingular () then and, for any matrix , (19) has a solution(b) If then and, for any matrix , (19) has a solutionHere, is an arbitrary orthogonal -matrix, is a projective matrix, and a “+” sign means a pseudoinversion [23].*

#### 3. Controlling of One-Dimensional Stochastic System

Consider one-dimensional discrete stochastic controlled systemwith noisy observationsHere, , , are scalar variables of state, output, and control input; , are uncorrelated random scalar sequences with parameters , , , and , and , , are scalar parameters of noise intensities.

It is supposed that the corresponding deterministic uncontrolled system (24) with and therein has an equilibrium . In what follows, we use the regulatorFor the synthesis of the assigned scalar stochastic sensitivity of the equilibrium , we apply theoretical results presented above.

At first describe the attainability set for the considered example. The function from (17) has here the following representation:The attainability condition (see Theorem 3 in Section 2) is equivalent to quadratic inequalityThus, all attainable values of have to satisfy the inequalityNote that the value is a minimal value of the stochastic sensitivity that we can provide by this regulator.

Our regulator will synthesize any assign stochastic sensitivity if we will take (see Theorem 3 in Section 2) a feedback coefficient as follows:

Note that the optimal regulator synthesizing the minimal value of the stochastic sensitivity has the feedback coefficient

#### 4. Example: Controlling Stochastic Verhulst System

Consider stochastically forced well-known Verhulst system with control and noisy observations:The corresponding deterministic uncontrolled system (32) has a nontrivial equilibrium . This equilibrium is stable for and unstable for .

At first consider the influence of random disturbances for system (32) without control (). Under the stochastic disturbances, for random states of this system some probabilistic distribution is formed [24]. In Figure 1(a), random states of system (32), with for , calculated by direct numerical simulation, are plotted by red color.