Identification of discrete-time output error model for industrial processes with time delay subject to load disturbance

Abstract

In this paper, a bias-eliminated output error model identification method is proposed for industrial processes with time delay subject to unknown load disturbance with deterministic dynamics. By viewing the output response arising from such load disturbance as a dynamic parameter for estimation, a recursive least-squares identification algorithm is developed in the discrete-time domain to estimate the linear model parameters together with the load disturbance response, while the integer delay parameter is derived by using a one-dimensional searching approach to minimize the output fitting error. An auxiliary model is constructed to realize consistent estimation of the model parameters against stochastic noise. Moreover, dual adaptive forgetting factors are introduced with tuning guidelines to improve the convergence rates of estimating the model parameters and the load disturbance response, respectively. The convergence of model parameter estimation is analyzed with a rigorous proof. Illustrative examples for open-and closed-loop identification are shown to demonstrate the effectiveness and merit of the proposed identification method.

Parametric identification of output error model for sampled systems with integer‐type time delay subject to load disturbance with unknown dynamics

“…Table 1 lists the identification results, where the estimated result for each parameter is indicated by the mean value together with the standard deviation in parentheses, and the relative percentage error E (k) is evaluated by It is seen that without any a priori knowledge of the load disturbance response, the proposed identification algorithm gives evidently improved accuracy on estimating the system model parameters and load disturbance response, together with faster convergence rates on estimating these parameters and load disturbance response, respectively, compared to the OE identification methods given in [28,34].…”

Recursive identification for Wiener non‐linear systems with non‐stationary disturbances

“…Note that the proof technique is similar to the methods presented in [31, 33, 43]. The interested readers are recommended to study them for detail on the formula derivation.…”

“…In discrete‐time domain, we use interactive estimation theory to derive a robust identification algorithm with two RLS methods and adaptive FFs for Hammerstein non‐linear or linear dual‐rate sampled system under load disturbance [31, 32]. On the basis of the adaptive variable FF matrix scheme, a robust method is presented for linear output error models with load disturbance [33]. A fast algorithm is proposed to solve an outlier detection problem and recover ‘clean’ data to give better parameter estimation for linear systems [34].…”

Maximum likelihood hierarchical least squares‐based iterative identification for dual‐rate stochastic systems

“…System identification and model parameter estimation are basic in controller design, dynamic systems modeling, and signal processing. 1,2 Different identification methods have been proposed for linear systems and nonlinear systems, such as the least squares methods, [3][4][5] the maximum likelihood methods, [6][7][8] the gradient methods, 9 the orthogonal matching pursuit methods, 10 and the robust identification methods. 11,12 However, most of these methods assumed that the input-output data are available at every sampling instant.…”