Another reason to consider parameter estimation transient performance is that fast and good transient performance can contribute to online real-time adjustment of control parameters and thus improve the control performance.
As a matter of fact, it retains as a provocative and open task to quantitatively assess the estimation error instantaneous performance before realizing steady-state performance. This can be responsible for the fact that because the instantaneous performance is difficult to quantify by using the design algorithm. Few reports on the transient performance of parameter estimation are published. Although the aforementioned-published identification algorithms have achieved good results in sandwich system identification, these algorithms focus on the steady-state performance of sandwich system parameter identification, i.e., t → ∞. for the sandwich model, and the model is used to build a dynamic model of a nonlinear radio system. An efficient gradient estimation method is given in by Campo et al. Li et al reported an adaptive estimator for the considered system where the adaptive law is designed through the usage of the parameter error and initial value. With the help of the auxiliary model, a multi-innovation gradient method is proposed by Xu to address the parameter estimation of sandwich system. Dreesen et al used canonical polyadic decomposition to decompose Volterra model into a sandwich system, and applied least squares to estimate system parameters. Liu proposed an improved bayesian approach to calculate the posterior distribution of the internal variables, used expectation maximization scheme to produce the estimation values of the sandwich system parameters. , a spearman correlation scheme is used to identify the sandwich model, in which the good initial values are obtained by using the best linear approximation approach. Numerous identification methods have been proposed to handle the parameter estimation of the sandwich system. The specific roles of these authors are articulated in the ‘author contributions’ section.Ĭompeting interests: The authors have declared that no competing interests exist. The funder provided support in the form of salaries for authors, but did not have any additional role in the study design, data collection and analysis, decision to publish, or preparation of the manuscript. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.ĭata Availability: All relevant data are within the paper and its Supporting information files.įunding: This paper is supported by the Key Specialized Research and Development Projects of Henan Province under Grant 202102210337. Received: MaAccepted: JPublished: December 14, 2022Ĭopyright: © 2022 Li et al. In comparison with the available estimation schemes, the good instantaneous performance is obtained on the basis of the numerical example and practical process results.Ĭitation: Li Z, Ma L, Wang Y (2022) Parameter estimation for nonlinear sandwich system using instantaneous performance principle.
Sandwich sequences convergence update#
An error equivalent conversion technique is then employed to obtain the transformed error data for establishing an parameter adaptive update law, in which the estimation error convergence and the predefined domain can be achieved. Then, a predefined constraint function is used to prescribe the error convergence boundary, in which the convergence rate is lifted. To achieve the above purpose, the estimation error information reflecting the transient performance of parameter estimation is procured using the developed some intermediate variables. In this study, the parameter estimation of nonlinear sandwich system is studied by using the predefined constraint technology and high-effective filter. With that in mind, we design an identification algorithm to address the transient performance of the parameter estimations. Few findings are reported for the instantaneous performance of parameter estimation because the instantaneous performance is difficult to quantify by using the design algorithm, for example, in the initial stage of parameter estimation, the error of parameter estimation varies in a specific region on the basis of the user’s request. The vast majority of reports mainly focus on the steady-state performance of parameter estimation.
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