A fast iterative recursive least squares algorithm for Wiener model identification of highly nonlinear systems

被引：74

作者：

Kazemi, Mandi ^{[1
]}

Arefi, Mohammad Mehdi ^{[1
]}

机构：

[1] Shiraz Univ, Sch Elect & Comp Engn, Dept Power & Control Engn, Shiraz, Iran

来源：

ISA TRANSACTIONS | 2017年 / 67卷

关键词：

Iterative recursive algorithm; Least squares identification; Wiener model; Highly nonlinear systems; Parameter estimation; CSTR process; pH neutralization process; PARAMETER-ESTIMATION; PREDICTIVE CONTROL;

D O I：

10.1016/j.isatra.2016.12.002

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

In this paper, an online identification algorithm is presented for nonlinear systems in the presence of output colored noise. The proposed method is based on extended recursive least squares (ERLS) algorithm, where the identified system is in polynomial Wiener form. To this end, an unknown intermediate signal is estimated by using an inner iterative algorithm. The iterative recursive algorithm adaptively modifies the vector of parameters of the presented Wiener model when the system parameters vary. In addition, to increase the robustness of the proposed method against variations, a robust RLS algorithm is applied to the model. Simulation results are provided to show the effectiveness of the proposed approach. Results confirm that the proposed method has fast convergence rate with robust characteristics, which increases the efficiency of the proposed model and identification approach. For instance, the FIT criterion will be achieved 92% in CSTR process where about 400 data is used.(C) 2016 ISA. Published by Elsevier Ltd. All rights reserved.

引用

页码：382 / 388

页数：7

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