This paper presents the use of active disturbance rejection control method (ADRC) to synchronize two different chaotic systems. The master system and slave systems have uncertainties and external disturbances. The numerical results are presented for the synchronization between the Duffing-Holmes system and the van der pol system. The numerical results presented show the effectiveness of the proposed method.
The extreme sensitivity to initial conditions is a fundamental characteristic of a chaotic system. The response of a chaotic system depends largely on the initial conditions. A small change in the initial conditions can lead to big differences in the system states responses. A complete synchronization between two chaotic systems is defined as the act of forcing the states of a slave system to track exactly the states of a master system. Chaos synchronization has potential applications in secure communication and other applications.
A variety of approaches have been proposed and tested to achieve synchronization. Based on the Lyapunov method and linear matrix inequality (LMI) method, the adaptive synchronization of the Genesio-Tesi chaotic systems with three uncertain parameters was achieved in [
Most of the studies concerning chaos synchronization assume that all states of the systems are totally known and the parameters of the chaotic system are exact, which is not the case in practice. Therefore, a chaos synchronization study must consider the presence of uncertain parameters and external disturbances. One possible and efficient solution of the synchronization problem is to use an observer coupled with a controller. This methodology can be found in the Active Disturbance Rejection Control method (or ADRC) proposed in [
In the present paper, the synchronization of different chaotic systems using the ADRC is presented. As an example, the synchronization of the Duffing- Holms and the van der Pol systems is considered to verify the proposed methodology.
A second order chaotic system such as the Duffing-Holmes (DHS) system and the van der Pol (VdP) systems can be written in the following general form
x ˙ 1 ( t ) = x 2 ( t ) x ˙ 2 ( t ) = f ( t , x 1 , x 2 ) + Δ f ( t , x 1 , x 2 ) + δ m ( t ) (1)
x 1 ( t ) , x 2 ( t ) are the states of the master system, f ( t , x 1 , x 2 ) is the nonlinear function of the master system Δ f ( t , x 1 , x 2 ) is the uncertainty and δ m ( t ) is the external disturbance.
A salve system can be written as:
y 1 ( t ) = y 2 ( t ) y 2 ( t ) = g ( t , y 1 , y 2 ) + Δ g ( t , y 1 , y 2 ) + δ s + u ( t ) . (2)
y 1 ( t ) and y 2 ( t ) are the states of the slave system, g ( x 1 , x 2 , t ) is the nonlinear function of the slave system Δ g ( x 1 , x 2 , t ) is the uncertainty and δ s ( t ) is the external disturbance, and u ( t ) is the control input.
A totally unknown total disturbance can be defined as
σ ( y 1 , y 2 , t ) = g ( y 1 , y 2 , t ) + Δ g ( y 1 , y 2 , t ) + δ s ( t ) . (3)
The controlled slave system can be written as:
y 1 ( t ) = y 2 ( t ) y 2 ( t ) = σ ( t , y 1 , y 2 ) + u ( t ) . (4)
The unknown total disturbance σ ( x , y , t ) is a bounded continuous and differentiable function with respect to time:
σ ( x , y , t ) ≤ D 1 σ ˙ ( x , y , t ) ≤ D 2 . (5)
The synchronization error between the slave system (2) and the master system (1) is defined as follows:
e i = y i − x i . (6)
A complete synchronization is to find a controller u(t) so that the error states e 1 and e 2 converge to zero in finite time. In mathematical form
lim t − T ‖ e ( t ) ‖ (7)
where T is a finite and ‖ ⋅ ‖ is the Euclidean norm.
The synchronization of two chaotic systems is considered as a tracking control problem. A tracking control problem can be formulated as: for the bounded states of the master system (1) design a controller u ( t , e 1 , e 2 , σ ) that forces the states of the slave system y 1 and y 2 to track the states of the master system x 1 and x 2 in finite time. To solve this problem, the ADRC method is used as described below.
To design a controller based on the measurement of the first state of the slave system, the errors e 1 , e 2 and σ must be available. One possible way to construct them is to use an extended observer (ESO) which is the main part of the ADRC. The main advantage of ESO is its ability to simultaneously estimate the total uncertainties online without the knowledge of an accurate mathematical model of the system. The ESO treats the total uncertainties as new state. An ESO for the slave system (4) is constructed as follows [
y ^ ˙ 1 = y ^ 2 ( t ) − α 1 l 1 ( y ^ 1 ( t ) − y 1 ( t ) ) y ^ ˙ 2 = y ^ 3 − α 2 l 2 ( y ^ 1 ( t ) − y 1 ( t ) ) + u y ^ ˙ 3 ( t ) = − α 3 l 3 ( y ^ 1 ( t ) − y 1 ( t ) ) . (8)
The observer provides an estimate of the states of the slave system y ^ 1 , y ^ 2 . The third state y ^ 3 of the observer approximates σ ( t , y 1 , y 2 ) . Here, l 1 , l 2 and l 3 are nonlinear function and α i are tuning parameters. Using a constant high gain ϵ − 1 with linear gain function the ESO can be written as:
y ^ ˙ 1 = y ^ 2 ( t ) + α 1 ε ( y 1 ( t ) − y ^ 1 ( t ) ) y ^ ˙ 2 = y ^ 3 + α 2 ε 2 ( y 1 ( t ) − y ^ 1 ( t ) ) + u y ^ ˙ 3 ( t ) = α 3 ε 2 ( y 1 ( t ) − y ^ 1 ( t ) ) . (9)
The tuning parameters α i can be determined by using the pole placement method. Once the pole locations are chosen, the observer gains are obtained as follows [
The characteristic polynomial is calculated as:
λ ( s ) = s 2 + α 1 s 2 + α 2 s + α 3 . (10)
Placing all the observers poles in the left half plane at − ω 0 to make the characteristic polynomial Hurwitz.
G ( s ) = ( s + ω 0 ) 3 . (11)
Setting the above two equations equal and solving results in the following observer gains:
α = [ 3 ω 0 3 ω 0 2 ω 0 3 ] .
ω 0 is called the observer bandwidth and it is the only tuning parameter. If the observer tuning procedure is adequate, the observer states converge to the system states y ^ 1 → y 1 , y ^ 2 → y 2 , and y ^ 3 → σ ^ .
The control objective is to cancel the total disturbance and in the same time satisfying the tracking task. The ADRC controller is defined as follows:
u = − y ^ 3 + u 0 . (12)
An example of such u 0 is a control signal from a feedback controller. Substituting (12) in (4) and assuming an accurate estimation of the total disturbance, the controlled slave system becomes:
y 2 = σ ( t , y 1 , y 2 ) − y ^ 3 + u 0 ≈ u 0 . (13)
The slave system is reduced to just a double integrator, which can be controlled with any classical controller design. If a linear proportional and derivative (PD) controller is used, then the following control law can be obtained:
u 0 = k p ( x 1 − y ^ 1 ) − k d ( x 2 − y ^ 2 ) (14)
where x 1 , x 2 are the states of the master system and y ^ 1 , y ^ 2 are the estimated states of the slave system. One possible way to simplify the tuning of the controller is to set k d = 2 ω c and k p = ω c 2 , where the ω c is the closed-loop bandwidth of the controller. The methodology applied is represented in
As an example, the synchronization of the DHS and the VdP chaotic systems is considered. The DHS system is the master system and VdP system is the slave system. The DHS equation describes the dynamics of nonlinear mechanical oscillator. This system has a cubic stiffness term to describe the hardening spring effect observed in many mechanical systems. The Duffing-Holmes is given by the following second order equation:
m x ¨ + p 0 x ˙ + p 1 x + p 2 x 3 = q cos ( ω t ) (15)
where x is the oscillation displacement, p0 is the damping constant, p1 is the linear stiffness constant, p2 is the cubic stiffness constant, q is the excitation amplitude, and ω is excitation frequency. Equation (15) can be rewritten as two- first order ordinary differential equations:
x ˙ 1 = x 2 x ˙ 2 = p 0 x 1 ( t ) − p 1 x 2 ( t ) − p 2 x 1 3 ( t ) + q cos ( ω t ) . (16)
To show the behavior of uncontrolled DHS, the following values of parameters are assumed: p 0 = 1 , p 1 = 0.25 , p 2 = 1 , q = 0.3 and ω = 1.2 rad / sec .
x 10 = − 2 , x 20 = 3.
The slave system is the Van der Pol system.
y ¨ − μ ( 1 − y 2 ) y ˙ + y = 2 sin ( 0.1 π t ) (17)
where μ > 0 is a scalar parameter. Equation (17) can be rewritten as two-first order ordinary differential equations:
y ˙ 1 = y 2 y ˙ 2 = − 2 y 1 + 3 ( 1 − y 1 2 ) y 2 + 2 sin ( 0.1 π t ) . (18)
To show the behavior of uncontrolled vdp system, its response to the initial conditions x 10 = 0.8 and x 20 = 0.5 is depicted in
The chaos synchronization based on the methodology described in Section 3 is presented. Simulations are run for 50 seconds with a step size of 0.01. The initial conditions for the two systems are the same as in the uncontrolled cases.
The following bounded external disturbance δ m ( t ) is added to the master system:
δ m ( t ) = 0.1 sin ( t ) . (19)
To the slave system a bounded uncertainty Δ f ( y 1 , y 2 , t ) for simulation purposes is added as:
Δ f ( y 1 , y 2 , t ) = 0.1 sin ( t ) y 1 2 + y 2 2 (20)
and the external disturbance is given by:
δ s ( t ) = 2 sin ( 0.1 π t ) + 3 sin ( 0.2 t + 1 ) . (21)
The controller parameter is ω c = 10 . The extended observer parameter is selected as ω 0 = 200 .
From
In this paper, the active disturbance rejection control methodology is used to synchronize two different chaotic systems. The idea of the ADRC is the use of a feedback controller coupled with an extended observer. The performance of the controller was verified through the numerical simulation. The results verified the performance of the controller in providing the convergence of error in a very short time. From the simulation results, it is concluded that the suggested method can be effectively used to synchronize chaotic systems in the presence of disturbances and uncertainties.
Khadra, F.A. (2017) Synchronization of Chaotic Systems via Active Disturbance Rejection Control. Intelligent Control and Automation, 8, 86- 95. https://doi.org/10.4236/ica.2017.82007