Recursive generalized extended least squares and RML algorithms for identification of bilinear systems with ARMA noise

Zahra Hafezi; Mohammad Mehdi Arefi

doi:10.1016/j.isatra.2018.12.015

Recursive generalized extended least squares and RML algorithms for identification of bilinear systems with ARMA noise

ISA Trans. 2019 May:88:50-61. doi: 10.1016/j.isatra.2018.12.015. Epub 2018 Dec 13.

Authors

Zahra Hafezi¹, Mohammad Mehdi Arefi²

Affiliations

¹ Department of Power and Control Engineering, School of Electrical and Computer Engineering, Shiraz University, Shiraz, Iran. Electronic address: zhafezi1994@gmail.com.
² Department of Power and Control Engineering, School of Electrical and Computer Engineering, Shiraz University, Shiraz, Iran. Electronic address: arefi@shirazu.ac.ir.

PMID: 30580882
DOI: 10.1016/j.isatra.2018.12.015

Abstract

Bilinear systems are considered as a particular class of nonlinear systems including the state variables which are typically used for online identification. By using a recursive identification method and the maximum likelihood principle, this paper presents two recursive-based algorithms to identify the parameters of bilinear in parameter systems with ARMA noise. In this regard, recursive generalized extended least squares (RGELS) and recursive Maximum Likelihood (RML) algorithms have been proposed for identification of bilinear systems. These algorithms can be used as an alternative choice in system identification with acceptable performance. The proposed algorithms estimate the correlated noise parameters with high accuracy by making full use of the measurement data. Simulation results indicate that the proposed algorithms are effective for online identification of bilinear in parameter systems with high convergence speed.

Keywords: Bilinear systems; Recursive generalized extended least squares; Recursive maximum likelihood; System identification; pH neutralization process..