Mathematical Problems in Engineering

Mathematical Problems in Engineering / 2009 / Article
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Modeling Experimental Nonlinear Dynamics and Chaotic Scenarios

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Research Article | Open Access

Volume 2009 |Article ID 387317 | 14 pages | https://doi.org/10.1155/2009/387317

Trajectory Sensitivity Method and Master-Slave Synchronization to Estimate Parameters of Nonlinear Systems

Academic Editor: Elbert E. Neher Macau
Received01 Feb 2009
Revised21 May 2009
Accepted12 Jun 2009
Published16 Sep 2009

Abstract

A combination of trajectory sensitivity method and master-slave synchronization was proposed to parameter estimation of nonlinear systems. It was shown that master-slave coupling increases the robustness of the trajectory sensitivity algorithm with respect to the initial guess of parameters. Since synchronization is not a guarantee that the estimation process converges to the correct parameters, a conditional test that guarantees that the new combined methodology estimates the true values of parameters was proposed. This conditional test was successfully applied to Lorenz's and Chua's systems, and the proposed parameter estimation algorithm has shown to be very robust with respect to parameter initial guesses and measurement noise for these examples.

1. Introduction

Several algorithms make use of synchronization to estimate parameters of nonlinear systems based on measured data. In [1, 2], for example, a Lyapunov-based design control and synchronization were used to estimate parameters of nonlinear systems. A similar approach that combines synchronization and geometric control is shown in [3]. Other attempts, including synchronization as an auxiliary tool for parameter estimation, are found in [46]. In addition, parameter estimation of delay systems can be found in [7, 8].

The measured output of the real system and the calculated output of an auxiliary system, usually taken as the mathematical model of the real system, are compared. Based on the output mismatch, parameters of the model are updated. When the outputs synchronize, that is, the difference between the real system and the model is sufficiently small, the parameters of the auxiliary system are assumed to be close enough to the real measurements.

However, synchronization is not sufficient to ensure the correct parameter estimation. In [9], for example, the authors follow the same techniques used in [6] and verify using examples that even when the outputs synchronize, the model parameters are far from the real system parameters.

Some studies try to guarantee the correct parameter estimation when the real system and the model outputs synchronize. In [10], for example, the authors prove, for a class of dynamical systems and using a convenient Lyapunov Control Function, that the system globally synchronizes, and synchronization implies convergence to the true parameters if some special conditions are satisfied.

In this paper, master-slave synchronization framework combined with a trajectory sensitivity-based fitting algorithm is used to estimate parameters of nonlinear systems. Trajectory sensitivity method has important characteristics and advantages when compared to other approaches, such as Lyapunov Control Function framework. Trajectory sensitivity-based parameter estimation approach has the advantage of being easily implemented for any nonlinear system while other approaches like Lyapunov-based metholodoly require the existence of a Lyapunov control function. Moreover, Lyapunov-based design may require long time intervals of measured data in order to achieve synchronization while trajectory sensitivity approach can easily deal with short time intervals of measured data [1]. Another interesting feature of trajectory sensitivity approaches is that they provide estimates of the initial conditions (a desired estimate in some problems like the determination of the initial state of population density of a living species in evolutionary studies) that cannot be obtained by the Lyapunov-based approach [1, 11].

In spite of these advantages, it is very difficult to guarantee synchronization and convergence to the true parameters when trajectory sensitivity analysis is used as a fitting algorithm. Usually, these assumptions are not checked, and numerical tests are used to verify the robustness of the algorithm. In general, the estimation fails due to the high relative sensitivities of the trajectories with respect to parameters and initial conditions that leads to a very small convergence region of the algorithm. In this scenario, master-slave synchronization emerges as a good auxiliary tool to increase the robustness of the parameter estimation algorithm with respect to the initial guess of parameters.

Moreover, for this combined approach, there will be proposed a conditional test to ensure convergence to the true parameter values. That is, if the parameter fitting algorithm provides synchronization in a different sense, as it will explained in Section 3.1, then the convergence to the true parameters is guaranteed. Based on the trajectory sensitivity method and master-slave synchronization, some applications to estimate parameters of synchronous generator in electric power system have been developed by the authors [12, 13].

The structure of this paper is as follow: Section 2 presents a general framework to estimate parameters based on measured data and synchronization. In Section 3, there will be exhibited the trajectory sensitivity method. Following that, master-slave synchronization and trajectory sensitivity method will be combined and tested in two nonlinear systems; Sections3.1 and 3.2. Moreover, the assumptions previously proposed to guarantee convergence to the true parameters will be checked for these examples. The results will be discussed in Section 4.

2. General Framework to Estimate Parameters Based on Measured Data and Synchronization

A common problem that appears in many applications is the necessity of estimating parameters of a system based on the information contained in measured data (time series). Figure 1 illustrates the traditional framework to estimate parameters of a general nonlinear dynamical system based on measured data. In order to accomplish the estimation, an auxiliary system, which is usually taken as the mathematical model of the real system, is employed. Some of the measurements are chosen as inputs of the auxiliary system while the others are used as outputs for comparison. Using the stored measured data of the inputs, the outputs of the auxiliary system are calculated for an initial guess of parameters. Both the measured and the auxiliary system outputs are compared. Based on the output mismatch, the parameters are updated according to some prescribed rule.

The success of the general framework of Figure 1 to estimate parameters is guaranteed if the following assumptions hold.

(A1)Synchronization between the real system and the auxiliary system outputs should imply parameters of the auxiliary system sufficiently close to parameters of the real system.(A2)The parameter fitting algorithm provides output synchronization.

Assumption (A1) is a necessary but not enough property, that has to be satisfied to guarantee the correctness of estimates. Assumption (A2) has to be satisfied to guarantee the convergence of the algorithm.

There are many alternatives to design parameter fitting algorithms; however, in most of the practical cases, it is very difficult to prove that assumption (A2) holds. In general, the success of the parameter estimation algorithm depends on personal experience to choose a convenient auxiliary system and an efficient parameter fitting algorithm. Usually, the effectiveness of the algorithms is numerically checked by means of a large number of tests, and their problems are usually related to two main aspects.

(1)The convergence region of the parameter estimation algorithm, that is a subset of the synchronization region, can be very small; that is, if a good guess of parameters is not available, then the parameter estimation algorithm diverges (synchronization is not achieved) or converges to wrong values (assumption (A1) is not satisfied). (2)They may not be robust with respect to the presence of noise in the measurements (synchronization is not achieved due to noise presence).

In this paper, an unilateral coupling between the real and the auxiliary systems is used to enhance the robustness of synchronization. As a result, parameters of nonlinear systems (including chaotic systems) are correctly estimated, even for bad initial guesses of parameters, in the presence of measurements with noise. The coupling is of unilateral type [14], that is, some of the outputs of the real system are used as inputs to the auxiliary system. This type of coupling is known as master-slave synchronization. In this case, the real system is the master while the auxiliary system is the slave. Figure 2 illustrates the situation.

It is important to emphasize that the authors that use control Lyapunov function approach to adjust parameters have shown conditions to guarantee the satisfaction of condition (A2) for some class of systems [14]. In our case, the adjustment is accomplished by the sensitivity trajectory method using Newton's method; therefore it is not possible to exhibit an analytical condition to guarantee the satisfaction of condition (A2). As a consequence, we pursued another goal; that is, we offered a conditional proof of condition (A2); that is, (A1) is numerically checked, and if synchronization is achieved, then (A2) is true. Although the control Lyapunov approach can provide, for some classes of nonlinear systems, the satisfaction of assumptions (A1) and (A2), the motivation to study theses conditions under the sensitivity trajectory method is that this method can be implemented for any nonlinear system in the form of (3.1)-(3.2), even for those we cannot exhibit a Lyapunov function.

In the next sections, this master-slave coupling approach is tested with a trajectory sensitivity-based parameter fitting algorithm.

3. Trajectory Sensitivity Method with Master-Slave Coupling

In this section, trajectory sensitivity analysis and master-slave synchronization will be used to estimate parameters of two chaotic systems. Although assumption (A2) cannot be easily checked, trajectory sensitivity analysis provides, from the practical point of view, advantages that justify its usage.

Trajectory sensitivity method can easily deal with hard nonlinearities. A very interesting extension of sensitivity method for differential algebraical equations (DAE systems) subject to nonsmooth events, like switchings, is presented in [15].

Consider a nonlinear system modeled by

𝑑𝑑𝑡𝑥(𝑡)=𝑓(𝑥(𝑡),𝑝,𝑢(𝑡)),(3.1)𝑦(𝑡)=𝑔(𝑥(𝑡),𝑝,𝑢(𝑡)),(3.2) where 𝑥𝜖𝑅𝑛 is the state vector, 𝑦𝜖𝑅𝑚 is the output vector, 𝑢𝜖𝑅𝑙 is the input vector, and 𝑝𝜖𝑅𝑘 is the parameter vector. Functions 𝑓 and 𝑔 are nonlinear, continuous, and Lipschitz with respect to 𝑥, 𝑝, and 𝑢. Let 𝑝𝑖 be the 𝑖th component of 𝑝. We assume that 𝑓 and 𝑔 are differentiable with respect to every component 𝑝𝑖 of 𝑝. In case they are not differentiable, a numerical approximation can be used to evaluate these derivatives [16]. The trajectory sensitivities 𝜕𝑥(𝑡)/𝜕𝑝𝑖 and 𝜕𝑦(𝑡)/𝜕𝑝𝑖 are computed, respectively, as

𝑑𝑑𝑡𝜕𝑥(𝑡)𝜕𝑝𝑖=𝜕𝑓(𝑥(𝑡),𝑝,𝑢(𝑡))𝜕𝑥𝜕𝑥(𝑡)𝜕𝑝𝑖+𝜕𝑓(𝑥(𝑡),𝑝,𝑢(𝑡))𝜕𝑝𝑖,𝜕𝑦(𝑡)𝜕𝑝𝑖=𝜕𝑔(𝑥(𝑡),𝑝,𝑢(𝑡))𝜕𝑥𝜕𝑥(𝑡)𝜕𝑝𝑖+𝜕𝑔(𝑥(𝑡),𝑝,𝑢(𝑡))𝜕𝑝𝑖.(3.3)

Trajectory sensitivities quantify the variation of a trajectory with respect to small variations in parameters. This quantification is used to update the model parameters in order to minimize the distance between the outputs of the real system and the mathematical model.

The parameter fitting algorithm is formulated as an optimization problem; that is, we try to minimize the error function 𝐽, which is given by

1𝐽(𝑝)=2𝑇𝑜0(𝑦𝑤)𝑡(𝑦𝑤)𝑑𝑡,(3.4) where, 𝑤 is the output vector of the real system, 𝑦 is the output of the auxiliary system (3.2), and [0,𝑇𝑜] is the time interval considered in the analysis. In other words, the minimization searches for output synchronization.

Given an initial value 𝑝=𝑝(𝑜), this optimization problem can be solved computing the sensitivity 𝜕𝐽(𝑝)/𝜕𝑝 and using a least squares method:

𝐺(𝑝)=𝜕𝐽(𝑝)=𝜕𝑝𝑇𝑜0𝜕𝑦𝜕𝑝𝑡||||(𝑦𝑤)𝑑𝑡𝑝=𝑝(𝑖).(3.5) Expanding 𝐺(𝑝) in Taylor series around 𝑝=𝑝(𝑖) and neglecting high-order terms one has

𝑝𝐺(𝑝)𝐺(𝑖)+ΓΔ𝑝,(3.6) where Γ(𝑝)=𝜕𝐺(𝑝)/𝜕𝑝. Then Δ𝑝=Γ1𝐺(𝑝(𝑖)). Parameters are fitted for the 𝑖th iteration by 𝑝(𝑖+1)=𝑝(𝑖)+Δ𝑝(𝑖+1). Matrix Γ(𝑝) can be computed by differentiating (3.5) and neglecting higher-order terms, thus

Γ(𝑝)𝑇0𝜕𝑦𝜕𝑝𝑡𝜕𝑦||||𝜕𝑝𝑑𝑡𝑝=𝑝(𝑖)(3.7) When the measurements are sampled at discrete time intervals, the previous integrals are replaced by summations. For more details see reference [17].

The error function indirectly depends on the parameters. Its evaluation requires the solution of a set of ordinary differential equations (the model). Usually, nonlinear dynamic models do not have closed analytical solutions; that is, the evaluation of the error function is made via numerical integration algorithms. Newton's method, which is used to achieve synchronization, demands the evaluation of trajectory sensitivity equations that are also obtained via numerical integration algorithms. As far as we know, there is no general condition to guarantee convergence of Newton's algorithm (or similar one) in this case.

The use of trajectory sensitivities in the framework described in Figure 1 has two main problems: (i) the trajectory sensitivity method is very sensitive to initial conditions (first parameter guess), and (ii) parameters with very low sensitivities (as compared to other parameters) are not numerically identifiable due to ill-conditioned calculations. These problems become worst especially when many parameters have to be simultaneously estimated and/or chaotic behavior is present. Very often, the trajectory sensitivity method leads to erroneous estimations or even divergence of the numerical algorithm.

In this section, a master-slave coupling and trajectory sensitivity-based approach are combined to estimate parameters of chaotic systems. Comparisons between the traditional trajectory sensitivity approach (presented in Figure 1) and the proposed methodology, which includes a master-slave coupling to enhance synchronization robustness, are made in order to show the advantages of the proposed methodology.

3.1. Lorenz's System

Consider Lorenz's system as the real system,

̇𝑥1=𝜎𝑟𝑥1+𝜎𝑟𝑥2,̇𝑥2=𝑥2𝑥1𝑥3+𝑟𝑟𝑥1,̇𝑥3=𝑏𝑟𝑥3+𝑥1𝑥2,(3.8) and suppose that at least two states can be measured. Let 𝑤=(𝑥1,𝑥2)𝑇 be the output vector of the real system.

In the traditional trajectory sensitivity approach, the following auxiliary system would be chosen to estimate parameters of Lorenz's system:

̇𝑧1=𝜎𝑧1+𝜎𝑧2,̇𝑧2=𝑧2𝑧1𝑧3+𝑟𝑧1,̇𝑧3=𝑏𝑧3+𝑧1𝑧2.(3.9)

This system has exactly the same structure as Lorenz's system model; the only difference is that the state variables 𝑥𝑖, 𝑖=1,2,3 were replaced by 𝑧𝑖, 𝑖=1,2,3, and the parameters do not have the subindex 𝑟 used for the real parameters. Assuming that measurements are subject to random noise, the initial conditions of states 𝑧𝑖, 𝑖=1,2,3 are unknown and have to be estimated. Thus, the extended parameter vector is 𝑝𝑟=(𝜎𝑟,𝑟𝑟,𝑏𝑟,𝑧1𝑜,𝑧2𝑜,𝑧3𝑜).

Our experience shows that Lorenz's system parameters cannot be simultaneously estimated using the traditional trajectory sensitivity methodology even for very small displacements of the initial conditions and parameter values from real values. Nonlinear systems with chaotic behavior, such as Lorenz's system, have a very small convergence region, because oftheir high relative output sensitivities with respect to the parameters.

Figure 3 shows the sensitivities of output 1 with respect to Lorenz's system parameters. The sensitivity of output 𝑧1 with respect to parameter 𝜎 is very small when compared with sensitivities of the 𝑧1 output with respect to parameter 𝑏 or 𝑟. Such behavior makes the approach of Figure 1 inappropriate for parameter estimation of chaotic systems. In the particular Lorenz's system case, errors as small as ±1% in the initial parameter and initial condition guesses lead the algorithm to nonconvergence due to lack of synchronization between the real system and the auxiliary system.

A master-slave coupling between the real system and the auxiliary system will be used to enhance the numerical robustness of the trajectory sensitivity-based parameter estimation algorithm. For this purpose, the next auxiliary system will be employed:

̇𝑧1=𝜎𝑧1+𝜎𝑧2,(3.10)̇𝑧2=𝑧2𝑥1𝑧3+𝑟𝑥1,(3.11)̇𝑧3=𝑏𝑧3+𝑥1𝑧2,(3.12) where 𝑧=(𝑧1,𝑧2,𝑧3)𝑇 is the state space vector. In this case, the states will be taken as the outputs of the auxiliary system, that is, 𝑦=(𝑧1,𝑧2,𝑧3)𝑇. The auxiliary system (3.10) to (3.12) resembles Lorenz's system; the difference is that state variable 𝑧1 was replaced by the coupling variable 𝑥1 in some convenient positions, in order to decrease nonlinearities of the auxiliary system.

The replacing of the coupling variable is accomplished in order to eliminate the nonlinear terms in the difference system (3.13). The substitution of the coupling variable 𝑥1 in (3.10) does not have any influence in the estimate process, for being a linear term. However, we still do not have a systematic procedure to determine the best terms to substitute.

Although there is no rules in how to do this replacement, our experience shows that these modifications enlarge the stability region of the parameter estimation algorithm. Figure 4 illustrates the schematic diagram with this approach.

The error 𝑒𝑖=𝑧𝑖𝑥𝑖, 𝑖=1,2,3 is derived from the difference between the outputs of both the auxiliary and the real systems:

̇𝑒1=𝜎𝑒1+𝜎𝑒2+𝑥2𝑥1𝜎𝑒,̇𝑒2=𝑒2𝑥1𝑒3+𝑥1𝑟𝑒,̇𝑒3=𝑥1𝑒2𝑏𝑒3𝑥3𝑏𝑒,(3.13) where 𝜎𝑒=𝜎𝜎𝑟,𝑟𝑒=𝑟𝑟𝑟, and 𝑏𝑒=𝑏𝑏𝑟 are the error of parameter estimation.

It is very difficult to prove assumption (A2). However, it will be shown that assumption (A1) is satisfied, for this choice of auxiliary system, in a different sense of synchronization.

Definition 3.1. The outputs of the real system   𝑤(𝑡)    and the auxiliary system   𝑦(𝑡)𝒞1-synchronize in the interval   [𝑇𝑎,𝑇𝑏]    with precision   𝜀   if sup𝑇𝑎𝑡𝑇𝑏𝑤(𝑡)𝑦(𝑡)+sup𝑇𝑎𝑡𝑇𝑏̇𝑤(𝑡)̇𝑦(𝑡)<𝜀.(3.14)

It is possible to prove, using (3.13) and The Implicit Function Theorem, that 𝒞1-synchronization, in the sense of Definition 3.1, implies that the estimated parameters are 𝜀-close to the real parameters.

Although assumption (A2) cannot be easily proven, it can be easily checked at the end of iterations. Moreover, the proposed master-slave coupling brings more robustness to synchronization. Lorenz's system parameters were estimated with good accuracy even for cases where the first guess of parameter values was displaced up to ±54% from the real value. Table 1 shows the estimation results under the presence of a ±5% white Gaussian random noise in all the measurements.


Parameter Initial value Deviation (%) Estimated value True value Error (%)

𝜎 4 . 6 0 5 4 % 1 0 . 0 3 4 1 0 . 0 0 . 3 4
𝑟 9 . 2 0 5 4 % 1 9 . 9 6 2 0 . 0 0 . 2
𝑏 4 . 1 0 + 5 4 % 2 . 6 6 2 . 6 6 0 . 0 0
𝑧 1 𝑜 1 . 9 0 + 5 % 2 . 0 7 2 . 0 0 3 . 5
𝑧 2 𝑜 2 . 8 5 + 5 % 2 . 9 4 3 . 0 0 2 . 0
𝑧 3 𝑜 3 . 2 2 5 4 % 6 . 9 6 7 . 0 0 0 . 5 7

The outputs of the real and the auxiliary system at the beginning and at the end of iterations are shown in Figure 5.

3.2. Chua's System

Chua's circuit is a singular example of chaotic system, because it is the simplest circuit that exhibits this kind of phenomenon. It is composed only by one inductor, two capacitors, one resistor, and one nonlinear active resistor with a three-segment picewise-linear volt-current (V-I) characteristics, called “Chua`s Diode,” as shown in Figure 6.

Let the dimensionless equations of Chua's circuit [18] represent the real system:

̇𝑥1=𝛼𝑟𝑥2𝑥1𝑥𝑓1,(3.15)̇𝑥2=𝑥1𝑥2+𝑥3,(3.16)̇𝑥3=𝛽𝑟𝑥2𝛾𝑟𝑥3𝑓𝑥,(3.17)1=𝑏𝑥1+12||𝑥(𝑎𝑏)1||||𝑥+11||,1(3.18) where 𝛼,𝛽,𝛾,𝑎, and 𝑏 are the parameters to be estimated. We assume that states are measured; that is, 𝑤=(𝑥1,𝑥2,𝑥3)𝑇 is the output vector of the real system. Assuming that the initial conditions of differential equations of the model are unknown and they also have to be estimated. Thus, the extended parameter vector is 𝑝=(𝛼,𝛽,𝛾,𝑎,𝑏,𝑥1𝑜,𝑥2𝑜,𝑥3𝑜), whose true values are 𝛼𝑟=6.5792, 𝛽𝑟=10.9024, 𝛾𝑟=0.0445, 𝑎𝑟=1.1829, 𝑏𝑟=0.6524, 𝑥1𝑜=0.15, 𝑥2𝑜=0.90, and 𝑥3𝑜=0.80. Measurements of the real system were obtained by numerical integration of (3.15) to (3.18), with their true parameters and initial conditions. Random white Gaussian noise of ±5% of the peak value was added to the real measurements.

Like Lorenz's system, parameters of Chua's circuit cannot be simultaneously estimated using the traditional trajectory sensitivity approach of Figure 1 due to high relative sensitivity of trajectories with respect to parameters and initial conditions. Our experience shows that errors as small as ±1% on the initial parameter guesses lead the algorithm to nonconvergence due to lack of synchronization.

In order to overcome this difficulty, the following auxiliary system was chosen:

̇𝑧1𝑧=𝛼2𝑥1𝑥𝑓1𝑧𝑘1𝑥1,̇𝑧2=𝑥1𝑧2+𝑧3,̇𝑧3=𝛽𝑧2𝛾𝑧3,𝑓𝑥1=𝑏𝑥1+12||𝑥(𝑎𝑏)1||||𝑥+11||.1(3.19)

The auxiliary system resembles Chua's circuit model; the difference is that a master-slave coupling between the real and the auxiliary systems was employed; that is, state variable 𝑧1 was replaced by the measured real variable 𝑥1 in some convenient positions to reduce nonlinearities in the auxiliary system. Moreover, to enhance synchronization robustness, an extra term was added to (3.15), 𝑔(𝑧1,𝑥1)=𝑘(𝑧1𝑥1), where 𝑘=10. Figure 7 illustrates this parameter estimation scheme.

The error 𝑒𝑖=𝑧𝑖𝑥𝑖, 𝑖=1,2,3 is derived from the difference between the outputs of both the auxiliary and the real systems:

̇𝑒1=𝑏𝑒1+𝛼𝑒2+𝛼𝑒𝑥2𝑥1𝑥1𝛼𝑏𝑒𝑏𝑟𝛼𝑒12𝛼𝑒𝑎𝑟𝑏𝑟𝑎𝛼𝑒𝑏𝑒||𝑥1||||𝑥+11||,1̇𝑒2=𝑒2+𝑒3,̇𝑒3=𝛽𝑒2𝛾𝑒3𝑥3𝛾𝑒𝑥2𝛽𝑒,(3.20) where 𝛼𝑒=𝛼𝛼𝑟, 𝛽𝑒=𝛽𝛽𝑟, 𝛾𝑒=𝛾𝛾𝑟, 𝑎𝑒=𝑎𝑎𝑟, and 𝑏𝑒=𝑏𝑏𝑟 are the error of parameter estimation.

It is practicable proved that 𝒞1-synchronization of outputs, in the sense of Definition 3.1, implies that parameters of the auxiliary system 𝑝=(𝛼,𝛽,𝛾,𝑎,𝑏) are 𝜀-close to the real parameter values 𝑝𝑟=(𝛼𝑟,𝛽𝑟,𝛾𝑟,𝑎𝑟,𝑏𝑟); that is, assumption (A1) is satisfied. For this purpose,the Implicit Function Theorem and (3.20) must be used.

Although assumption (A2) cannot be easily proven, it can be checked at the end of the iterations. Chua's circuit parameters were successfully estimated even for parameter initial guess deviations as large as 65% from the real values. Table 2 shows the estimation results.


Parameter Initial value Deviation Estimated value True value Error (%)

𝛼 2 . 3 0 2 7 6 5 % 6 . 5 4 3 5 6 . 5 7 9 2 0 . 5 4
𝛽 3 . 8 1 5 8 6 5 % 1 0 . 8 9 0 6 1 0 . 9 0 2 4 0 . 1 1
𝛾 0 . 0 1 5 6 6 5 % 0 . 0 4 4 4 0 . 0 4 4 5 0 . 2 7
𝑎 0 . 4 1 3 7 6 5 % 1 . 1 8 0 7 1 . 1 8 2 0 0 . 1 2
𝑏 0 . 2 2 8 3 6 5 % 0 . 6 5 3 9 0 . 6 5 2 4 0 . 2 4
𝑥 0 0 . 0 5 2 5 6 5 % 0 . 1 5 4 2 0 . 1 5 0 0 2 . 8 3
𝑦 0 0 . 3 1 5 0 6 5 % 0 . 9 0 0 9 0 . 9 0 0 0 0 . 1 0
𝑧 0 0 . 2 8 0 0 6 5 % 0 . 8 1 8 5 0 . 8 0 0 0 2 . 3 1

The output of the real and the auxiliary system at the beginning and at the end of iterations are shown in Figure 8.

4. Conclusions

A combination of trajectory sensitivity method and master-slave synchronization was proposed to parameter estimation of nonlinear systems. It was shown that master-slave coupling increases the robustness of the trajectory sensitivity algorithm with respect to the initial guess of parameters. Since synchronization is not a guarantee that the estimation process converges to the correct parameters, a conditional test that guarantees the new combined methodology estimates that the true values of parameters was proposed. This conditional test was successfully applied to Lorenz's and Chua's systems and the proposed parameter estimation algorithm has shown to be very robust with respect to parameter initial guesses and measurement noise for these examples.

Acknowlegments

This research was supported in part by Fundação de Amparo a Pesquisa do Estado de São Paulo (FAPESP) and Conselho Nacional de Desenvolvimento Cientfico e Tecnológico (CNPq).

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Copyright © 2009 Elmer P. T. Cari et al. 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.


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