Research Article  Open Access
Recursive LeastSquares Estimation for Hammerstein Nonlinear Systems with Nonuniform Sampling
Abstract
This paper focuses on the identification problem of Hammerstein nonlinear systems with nonuniform sampling. Using the keyterm separation principle, we present a discrete identification model with nonuniform sampling input and output data based on the frame period. To estimate parameters of the presented model, an auxiliary modelbased recursive leastsquares algorithm is derived by replacing the unmeasurable variables in the information vector with their corresponding recursive estimates. The simulation results show the effectiveness of the proposed algorithm.
1. Introduction
In actual industrial processes, there exist widely nonlinear systems which are described by blockoriented nonlinear systems [1–3]. Blockoriented nonlinear models are in general divided into Hammerstein systems and Wiener systems [4]. A Hammerstein system, which consists of a static nonlinear subsystem followed by a linear dynamic subsystem, can represent some nonlinear systems [5]. Many publications have been reported for the identification of the Hammerstein systems [6, 7]. For example, Chen et al. studied identification problems for the Hammerstein systems with saturation and deadzone nonlinearities by choosing an appropriate switching function [8]; Ding et al. presented the projection, the stochastic gradient, and the Newton recursive and the Newton iterative identification algorithms for the Hammerstein nonlinear systems, and then they analyzed and compared the performances of these approaches by numerical examples [9]. Li et al. derived a leastsquares based iterative algorithm for the Hammerstein output error systems with nonuniform sampling by using the overparameterization model [10].
The different inputoutput updating period (or called multirate sampling) is inevitable in discretetime systems [11–13]. The identification of multirate sampled systems have attracted much attention of many researchers. Recently, Liu et al. proposed a novel hierarchical leastsquares algorithm for a class of nonuniformly sampled systems based on the hierarchical identification principle [14]. Shi et al. presented a crosstalk identification algorithm for multirate xDSL FIR systems [15]. Han et al. gave statespace models for multirate multiinput sampleddata systems and derived an auxiliary modelbased recursive leastsquares algorithm for identifying the parameters of multirate systems [16].
The recursive leastsquares algorithm is a class of basic parameter estimation approaches which are suitable for online applications. In this literature, Wang adopted a filtering auxiliary modelbased recursive leastsquares identification algorithm for output error moving average systems [17]. Differing from the work in [14, 16], this paper discusses the parameter estimation problem for nonuniformly sampled Hammerstein nonlinear systems. The basic idea is, to combine the auxiliary model identification idea [18–24] and the keyterm separation principle to derive the auxiliary modelbased recursive leastsquares algorithm for the Hammerstein nonlinear systems with nonuniform sampling.
The rest of this paper is organized as follows. Section 2 establishes the identification model of the Hammerstein nonlinear systems with nonuniform sampling. Section 3 derives a recursive leastsquares parameter estimation algorithm based on the auxiliary model identification idea. Section 4 provides an example to illustrate the effectiveness of the proposed algorithm. The conclusions of the paper are summarized in Section 5.
2. The Identification Model
Let us introduce some notations. The superscript denotes the matrix transpose; stands for an identity matrix of appropriate sizes; represents an dimensional column vector whose elements are 1; “” stands for “ is defined as ”; and is a unit backward shift operator; that is, .
Consider a Hammerstein nonlinear system with nonuniform sampling shown in Figure 1, where is a nonuniform zeroorder hold with irregularly updating intervals , dealing with a discretetime signal and producing the input of the nonlinear subsystem ; is the output of the nonlinear subsystem; is a continuoustime process; is the true output of but is unmeasurable; is a sampler that produces a discretetime signal with period ; and is the system output but is corrupted by the additive noise .
Assuming that the input has the updating intervals , we have [11, 25] where is the frame period. The nonlinear subsystem in the Hammerstein nonlinear system is a polynomial of a known order: where is the polynomial order.
Suppose that has the following statespace representation: where is the state vector, and are the input and output of the continuoustime process, respectively, and , , , and are matrices of appropriate sizes. Referring to [25] and discretizing (3) with the frame period , we have where The output at the sampling instant can be expressed as Hence, the system output is written as Referring to [26] and from (4) and (6), we have where Equation (8) can be transformed into Substituting (10) into (7), the system output can be expressed as Equation (11) can be rewritten equivalently as Here, substituting (2) into (12) results in a complex expression containing the products of parameters. To solve this problem, we use the keyterm separation principle presented in [27], and let . Then, the identification model of the proposed system is as follows: The objective of this paper is to develop a recursive leastsquares algorithm for estimating the parameters of the nonuniformly sampled Hammerstein systems by using the auxiliary model identification idea in [11].
3. The Recursive LeastSquares Algorithm
In this section, we derive the recursive leastsquares estimation algorithm for the Hammerstein nonlinear systems with nonuniform sampling, referring to the method in [1].
Define the information vectors and the parameter vectors as Equation (13) can be written in a regressive form as Define a quadratic criterion function as Let be the estimate of at time . Minimizing gives the following recursive leastsquares algorithm: Note that the information vector in (17) contains unknown inner variables and ; the parameter vector cannot be estimated by the standard leastsquares method. The solution is based on the auxiliary model identification idea [11]: to replace the unmeasurable term in with its estimate Replacing in (2) with its estimate , we can obtain the estimate of as follows:
Define the estimate of as Using in place of in (17) and (18), we have Equations (19) to (22) form the AMRLS algorithm for the not uniformly sampled Hammerstein nonlinear systems, which can be summarized as To initialize the algorithm, we take to be a small real vector; for example, and with normally a large positive number (e.g., ).
4. Example
An example is given to demonstrate the feasibility of the proposed algorithm. Assume that the dynamical linear subsystem has the following statespace representation: and the static nonlinear subsystem is denoted by Let , s, and s; that is, s and s. Discretizing with the frame period , we obtain Thus, the corresponding inputoutput expression is given by
In simulation, the inputs and are taken as persistent excitation signal sequences with zero mean and unit variance; is a white noise with zero mean and variance . Applying the proposed algorithm to estimate the parameters of this system, the estimates of and their errors with different noise variances are shown in Tables 1 and 2, and the parameter estimation errors versus are shown in Figure 2. When and , the corresponding signaltonoise ratios (the square root of the ratio of output and noise variances) are and , respectively.


From Tables 1 and 2 and Figure 2, we can draw the following conclusions.(i)The parameter estimation errors of the AMRLS algorithm become (generally) smaller as increases; see the estimation errors of the last columns of Tables 1 and 2 and Figure 2. (ii)Under different noise levels, the parameter estimates can converge to the true value, and a lower noise level results in a faster convergence rate of the parameter estimates to the true parameters; see the error curves in Figure 2 and the estimation errors in Tables 1 and 2. (iii)The proposed recursive algorithm differs from the iterative identification approach in [10] and can be used as an online identification.
5. Conclusions
In this paper, we have established the identification model of the Hammerstein nonlinear systems with nonuniform sampling by using the keyterm separation principle. To estimate the parameters of the proposed model, the recursive leastsquares parameter estimation algorithm is derived based on the auxiliary model identification idea. The proposed algorithm can simultaneously estimate the parameters of the linear and nonlinear subsystems of the Hammerstein nonlinear systems with nonuniform sampling. The simulation results show that the parameters of the Hammerstein systems with nonuniform sampling can be estimated effectively by the proposed algorithm. Although the algorithm is presented for a class of nonuniformly sampled Hammerstein nonlinear systems, the basic idea can also be extended to identify other linear and nonlinear systems [28, 29] and can combine the hierarchical identification methods [30–34], the multiinnovation identification methods [35–44], and other identification methods [45–58] to present new identification algorithms for linear or nonlinear and scalar or multivariable systems [59].
Acknowledgments
This work was supported by the Fundamental Research Funds for the Central Universities (JUDCF11042 and JUDCF12031) and the PAPD of Jiangsu Higher Education Institutions and the 111 Project (B12018).
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Copyright © 2013 Xiangli Li 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.