Multi-Innovation Stochastic Gradient Identification Algorithm for Hammerstein Controlled Autoregressive Autoregressive Systems Based on the Key Term Separation Principle and on the Model Decomposition

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Introduction
There exist many nonlinear systems in process control [1][2][3].A nonlinear system can be modeled by input nonlinear systems [4] and output nonlinear systems [5], input-output nonlinear systems [6], feedback nonlinear systems [7], and so on.Input nonlinear systems, which are called Hammerstein systems [8], include input nonlinear equation error type systems and input nonlinear output error type systems.Recently, many identification algorithms have been developed for input nonlinear systems, such as the iterative methods [9][10][11], the separable least squares methods [12,13], the blind methods [14], the subspace methods [15], and the overparameterization methods [16,17].Some methods require paying much extra computation.
The stochastic gradient (SG) algorithm is widely applied to parameter estimation.For example, Wang and Ding presented an extended SG identification algorithm for Hammerstein-Wiener ARMAX systems [18], but it is well known that the SG algorithm has slower convergence rates.In order to improve the convergence rate of the SG algorithm, Xiao et al. presented a multi-innovation stochastic gradient parameter estimation algorithm for input nonlinear controlled autoregressive (IN-CAR) models using the over-parameterization method [19]; Chen et al. proposed a modified stochastic gradient algorithm by introducing a convergence index in order to improve the convergence rate of the parameter estimation [20]; Han and Ding developed a multi-innovation stochastic gradient algorithm for multi-input single-output systems [21]; Liu et al. studied the performance of the stochastic gradient algorithm for multivariable systems [22].
The decomposition identification techniques include matrix decomposition and model decomposition.Hu and Ding presented a least squares based iterative identification algorithm for controlled moving average systems using the matrix decomposition [23]; Ding derived an iterative least squares algorithm to estimate the parameters of output error systems, and the matrix decomposition can enhance computational efficiencies [24].Ding also divided a Hammerstein nonlinear system into two subsystems based on the model decomposition and presented a hierarchical multiinnovation stochastic gradient algorithm for Hammerstein nonlinear systems [25].
This paper discusses identification problems of input nonlinear controlled autoregressive autoregressive (IN-CARAR) systems or Hammerstein controlled autoregressive autoregressive (H-CARAR) systems, which is one kind of input nonlinear equation error type systems.The basic idea is using the key term separation principle [26] and the decomposition technique [24] to derive a multi-innovation stochastic gradient identification algorithm, which is different from the work in [19,21,25].
The rest of this paper is organized as follows.Section 2 gives the identification model for the IN-CARAR systems.Section 3 introduces the SG algorithm for the IN-CARAR system.Section 4 deduces a multi-innovation SG algorithm for IN-CARAR system using the decomposition technique.Section 5 provides a numerical example to show the effectiveness of the proposed algorithm.Finally, Section 6 offers some concluding remarks.

The System Identification Model
The paper focuses on the parameter estimation of a Hammerstein nonlinear controlled autoregressive autoregressive (H-CARAR) system, that is, an input nonlinear controlled autoregressive autoregressive (IN-CARAR) system, which consists of a nonlinear block and a linear dynamic subsystem.It is worth noting that Xiao and Yue discussed a data filtering based recursive least squares algorithm for H-CARAR systems [27] and a multi-innovation stochastic gradient parameter estimation algorithm for input nonlinear controlled autoregressive (IN-CAR) systems.
Define the parameter vectors: and the information vectors: Equation ( 2) can be written as Using the key term separation principle [26], (1) can be written as =   () .(11) This is the identification model of the IN-CARAR system.

The Stochastic Gradient Algorithm
According to [1] and based on the identification model in (11), we can obtain the stochastic gradient (SG) algorithm: where X() represents the estimate of  at time ; for example,

The Multi-Innovation Stochastic Gradient Algorithm
This section deduces the multi-innovation stochastic gradient identification algorithm for the IN-CARAR system using the decomposition technique [1].Define two intermediate variables, From ( 10), we have These two subsystems include the parameter vectors   and   , respectively.  contains the parameters od the system model and   contains the parameters od the noise model.

Numerical Examples
Consider the following IN-CARAR system: In this example, the input {()} is taken as a persistent excitation signal sequence with zero mean and unit variance and {V()} as a white noise sequence with zero mean and variance  2 = 0.50 2 .Applying the SG algorithm and the MISG algorithm to estimate the parameters of this IN-CARAR system, the parameter estimates and their estimation errors are shown in Tables 1 and 2 with the data length  = 3000 and the estimation error  := ‖ θ() − ‖/‖‖ versus  being shown in Figures 2 and 3.
From Tables 1 and 2 and Figures 2 and 3, we can draw the conclusions.The parameters estimation errors become smaller with the data length  increasing.The estimation errors given by the MISG algorithm are much smaller than that of the SG algorithm.The convergence speed of the multiinnovation SG algorithm is faster than those of the SG algorithm.These indicate that the MISG algorithm has better performance than the SG algorithm.

Conclusions
The gradient and least squares algorithms are two different kinds of important identification methods.It is well known that the gradient algorithm has poor convergence rates.This paper studies the multi-innovation SG identification methods for IN-CARAR systems.The numerical examples show that the proposed MISG algorithm can estimate effectively the parameters of input nonlinear systems and indicate that increasing the innovation length can improve parameter estimation accuracy of the multi-innovation identification algorithm because the algorithm uses more information in each recursion for a large innovation length.The proposed method can be applied to nonlinear output error systems.Although the algorithm is presented for the IN-CARAR systems, the basic idea can be extended to other linear or nonlinear systems with colored noises [42][43][44][45][46][47][48][49][50][51][52][53][54][55][56][57][58][59][60][61].

Table 1 :
The SG parameter estimates and errors.

Table 2 :
The MISG parameter estimates and errors.