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Mathematical Problems in Engineering

Volume 2012 (2012), Article ID 529251, 9 pages

http://dx.doi.org/10.1155/2012/529251

## Adaptive Control and Synchronization of the Shallow Water Model

Department of Mathematics, Faculty of Science, Maejo University, Chiang Mai 50290, Thailand

Received 6 July 2011; Revised 24 September 2011; Accepted 26 September 2011

Academic Editor: Carlo Cattani

Copyright © 2012 P. Sangapate. 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

The shallow water model is one of the important models in dynamical systems. This paper investigates the adaptive chaos control and synchronization of the shallow water model. First, adaptive control laws are designed to stabilize the shallow water model. Then adaptive control laws are derived to chaos synchronization of the shallow water model. The sufficient conditions for the adaptive control and synchronization have been analyzed theoretically, and the results are proved using a Barbalat's Lemma.

#### 1. Introduction

A dynamical system is a system that changes over time. Chaotic systems are dynamical systems that are highly sensitive to initial conditions. Chaos phenomena in weather models were first observed by Lorenz equation; a large number of chaos phenomena and chaos behavior have been discovered in physical, social, economical, biological, and electrical systems.

Atmosphere is a dynamical system. An atmospheric model is a set of equations that describes behavior of the atmosphere. The shallow water model is simple model for the atmosphere. Shallow water model is the set of the equations of motion that describes the evolution of a horizontal structure, hydrostatic homogeneous, and incompressible flow on the sphere [1].

The control of chaotic systems is to design state feedback control laws that stabilize the chaotic systems. Control theory is an interdisciplinary branch of engineering and mathematics that deals with the behavior of dynamical systems. The usual objective of control theory is to calculate solutions for the proper corrective action from the controller that result in system stability.

Synchronization of chaotic systems is phenomena that may occur when two or more chaotic oscillators are coupled or when a chaotic oscillator drives another chaotic oscillator, because of the butterfly effect, which causes the exponential divergence of the trajectories of two identical chaotic systems started with nearby the same initial conditions. Synchronizing two chaotic systems is seemingly a very challenging problem in chaos literature [2–6].

In 1990, Pecora and Caroll [7] introduced a method to synchronize two identical chaotic systems and showed that it was possible for some chaotic systems to be completely synchronized. From then on, chaos synchronization has been widely explored in variety of fields including physical system [8], chemical systems [9], ecological systems [10], secure communications [11, 12], and so forth.

In most of the chaos synchronization approaches, the drive-response formalism has been used. If a particular chaotic system is called the drive system and another chaotic system is called the response system, then the idea of synchronization is to use the output of the drive system to control the response system so that the output of the response system tracks the output of drive system asymptotically stable.

This paper is organized as follows. Section 2 gives notations and definitions of the stability in the chaotic system. Section 3 presents the adaptive control chaos of the shallow water model. Section 4 presents adaptive synchronization of the shallow water model. The conclusion discussion is in Section 5.

#### 2. Notations and Definitions

denotes an infinite dimensional Banach Space with the corresponding norm , denotes the real line.

Consider a nonlinear nonautonomous differential equation of the general form where the state take values in , is a given nonlinear function and , for all . The stability conditions were proposed and presented in [13].

*Definition 2.1. *The zero solution of (2.1) is said to be *stable* if for every , there exists a number (depending upon and) such that for any solution of (2.1) with implies , for all.

*Definition 2.2. *The zero solution of (2.1) is said to be *asymptotically stable* if it is stable and there is a number such that any solution with satisfies .

Consider the control system
where is the external control input. The adaptive control is the control method to design state feedback control laws that stabilize the chaotic systems.

*Definition 2.3. *The control system (2.2) is *stabilizable* if there exists feedback control such that the system
is asymptotically stable.

Consider two nonlinear systems
where is the set of nonnegative real number. Assume that (2.4) is the drive system, (2.5) is the response system, and is the control vector.

*Definition 2.4. *Response system and drive system are said to be *synchronic* if for any initial conditions, .

Lemma 2.5 (Barbalat’s lemma as used in stability). *For nonautonomous system,
**
If there exists a scalar function such that *(1)* has a lower bound,*(2)*,*(3)* is uniformly continuous in time,**then by applying the Barbalat’s Lemma to stabilize the chaotic systems.*

#### 3. Adaptive Control Chaos of the Shallow Water Model

A chaotic system has complex dynamical behaviors; those posses some special features, such as being extremely sensitive to tiny variations of initial conditions. In this section, adaptive control method is applied to control chaos shallow water model.

Shallow water model is the set of the equations of motion that describes the evolution of a horizontal structure, hydrostatic homogeneous, and incompressible flow on the sphere. Euler’s equations of motion of an ideal fluid are as follows: where is the density of the fluid, is the pressure, is the gravity, and is coliolis parameter. Using the hydrostatic approximation, This implies. Assume the pressure of fluid is constant, this implies that and consider the continuity equation (or the incompressibility condition), By solving for and integrating with respect to , then can be expressed as The surface (of the fluid) boundary condition on is that the fluid particles follow the surface. Thus To get an expression for the pressure in the fluid, integrate the hydrostatic equation (3.2) from at the top downward, Take the partial derivatives of (at the surface) with respect to and , Taking (3.2)–(3.7) into (3.1), so the shallow water model in Cartesian coordinates is as follows: In the vector form, the shallow water model is as follows: where is the horizontal velocity, is the geopotential height.

Consider the controlled system of (3.9) which has the form where is external control input which will drag the chaotic trajectory of the shallow water model to equilibrium point which is one of two steady states .

In this case the control law is where (estimate of , , resp.) are updated according to the following adaptive algorithm: where is adaption gains. Then the controlled systems have the following form:

Theorem 3.1. *For , the equilibrium point of the system (3.13), (3.14) is asymptotically stable.*

*Proof. *Let us consider the Lyapunov function
The time derivative of is
By substituting (3.13)-(3.14) in (3.16),
Let . Since is an equilibrium point of the uncontrolled system (3.9), becomes
It is clear that if we choose and , then is negative semidefinite. Since is positive definite and is negative semidefinite, . From , we can easily show that the square of is integrable with respect to , namely, . From (3.13)-(3.14), for any initial conditions, we have . By the well-known Barbalat’s Lemma, we conclude that as. Therefore, the equilibrium point of the system (3.13)-(3.14) is asymptotically stable.

#### 4. Adaptive Synchronization of the Shallow Water Model

In this section, the adaptive synchronization is introduced to make two of the shallow water model. The sufficient condition for the synchronization has been analyzed theoretically, and the result is proved using a Barbalat’s Lemma. Assume that there are two shallow water models such that the drive system is to control the response system. The drive and response system are given as where is the controller. We choose where and are the error states which are defined as follows

Theorem 4.1. *Let be property chosen so that the following matrix inequalities holds:
**
then the two shallow water models (4.1) can be synchronized under the adaptive control (4.2).*

*Proof. *It is easy to see from (4.1) that the error system is
Let . Choose the Lyapunov function as follows:
Then the differentiation of along trajectories of (4.6) is
where is as in (4.4). Since is positive definite and is negative semidefinite, it follows that . From, we can easily show that the square of is integrable with respect to , namely, . From (4.5), for any initial conditions, we have . By the well-known Barbalat’s Lemma, we conclude that as . Therefore, in the closed-loop system, as . This implies that the two shallow water models have synchronized under the adaptive controls (4.2).

#### 5. Conclusions

In this paper, we applied adaptive control theory for the chaos control and synchronization of the shallow water model. First, we designed adaptive control laws to stabilize the shallow water model based on the adaptive control theory and stability theory. Then, we derived adaptive synchronization to the shallow water model. The sufficient conditions for the adaptive control and synchronization of the shallow water model have been analyzed theoretically, and the results are proved using a Barbalat’s Lemma.

#### References

- J. Tribbia,
*An Introduction to Three-Dimensional Climate Modeling*, Oxford University, New York, NY, USA, 1992. - E. Ott, C. Grebogi, and J. A. Yorke, “Controlling chaos,”
*Physical Review Letters*, vol. 64, no. 11, pp. 1196–1199, 1990. View at Publisher · View at Google Scholar · View at Zentralblatt MATH · View at MathSciNet - J. H. Park and O. M. Kwon, “A novel criterion for delayed feedback control of time-delay chaotic systems,”
*Chaos, Solitons and Fractals*, vol. 23, no. 2, pp. 495–501, 2005. View at Publisher · View at Google Scholar · View at Zentralblatt MATH · View at MathSciNet - T. L. Liao and S. H. Tsai, “Adaptive synchronization of chaotic systems and its application to secure communications,”
*Chaos, Solitons and Fractals*, vol. 11, no. 9, pp. 1387–1396, 2000. View at Publisher · View at Google Scholar · View at Zentralblatt MATH - T. Yang and L. O. Chua, “Generalized synchronization of chaos via linear transformations,”
*International Journal of Bifurcation and Chaos*, vol. 9, no. 1, pp. 215–219, 1999. View at Publisher · View at Google Scholar · View at Zentralblatt MATH · View at MathSciNet - Y. G. Yu and S. Zhang, “Adaptive backstepping synchronization of uncertain chaotic system,”
*Chaos, Solitons and Fractals*, vol. 27, no. 3, pp. 643–649, 2004. View at Publisher · View at Google Scholar · View at Zentralblatt MATH - L. M. Pecora and T. L. Carroll, “Synchronization in chaotic systems,”
*Physical Review Letters*, vol. 64, no. 8, pp. 821–824, 1990. View at Publisher · View at Google Scholar · View at Zentralblatt MATH · View at MathSciNet - M. Lakshmanan and K. Murali,
*Chaos in Nonlinear Oscillators*, World Scientific Publishing, River Edge, NJ, USA, 1996. - S. K. Han, C. Kerrer, and K. Kuramoto, “Dephasing and brusting in coupled neutral oscillators,”
*Physical Review Letters*, vol. 75, pp. 3190–3193, 1995. View at Publisher · View at Google Scholar - B. Blasius, A. Huppert, and L. Stone, “Complex dynamics and phase synchronization in spatially extended ecological systems,”
*Nature*, vol. 399, no. 6734, pp. 354–359, 1999. View at Publisher · View at Google Scholar · View at PubMed - K. Murali and M. Lakshmanan, “Secure communication using a compound signal from generalized synchronizable chaotic systems,”
*Physical Review Letters*, vol. 241, no. 6, pp. 303–310, 1998. View at Google Scholar · View at Zentralblatt MATH - M. Feki, “An adaptive chaos synchronization scheme applied to secure communication,”
*Chaos, Solitons and Fractals*, vol. 18, no. 1, pp. 141–148, 2003. View at Publisher · View at Google Scholar · View at Zentralblatt MATH · View at MathSciNet - V. Lakshmikantham, S. Leela, and A. A. Martynyuk,
*Stability Analysis of Nonlinear Systems*, Marcel Dekker, New York, NY, USA, 1989.