Recursive Algorithms for Multivariable Output-Error-Like ARMA Systems

Hao Ma, Jian Pan, Lei Lv, Guanghui Xu, Feng Ding, Ahmed Alsaedi and Tasawar Hayat
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Hao Ma: Hubei Key Laboratory for High-efficiency Utilization of Solar Energy and Operation Control of Energy Storage System, School of Electrical and Electronic Engineering, Hubei University of Technology, Wuhan 430068, China
Jian Pan: Hubei Key Laboratory for High-efficiency Utilization of Solar Energy and Operation Control of Energy Storage System, School of Electrical and Electronic Engineering, Hubei University of Technology, Wuhan 430068, China
Lei Lv: Hubei Key Laboratory for High-efficiency Utilization of Solar Energy and Operation Control of Energy Storage System, School of Electrical and Electronic Engineering, Hubei University of Technology, Wuhan 430068, China
Guanghui Xu: Hubei Key Laboratory for High-efficiency Utilization of Solar Energy and Operation Control of Energy Storage System, School of Electrical and Electronic Engineering, Hubei University of Technology, Wuhan 430068, China
Feng Ding: College of Automation and Electronic Engineering, Qingdao University of Science and Technology, Qingdao 266061, China
Ahmed Alsaedi: Department of Mathematics, King Abdulaziz University, Jeddah 21589, Saudi Arabia
Tasawar Hayat: Department of Mathematics, King Abdulaziz University, Jeddah 21589, Saudi Arabia

Mathematics, 2019, vol. 7, issue 6, 1-18

Abstract: This paper studies the parameter identification problems for multivariable output-error-like systems with colored noises. Based on the hierarchical identification principle, the original system is decomposed into several subsystems. However, each subsystem contains the same parameter vector, which leads to redundant computation. By taking the average of the parameter estimation vectors of each subsystem, a partially-coupled subsystem recursive generalized extended least squares (PC-S-RGELS) algorithm is presented to cut down the redundant parameter estimates. Furthermore, a partially-coupled recursive generalized extended least squares (PC-RGELS) algorithm is presented to further reduce the computational cost and the redundant estimates by using the coupling identification concept. Finally, an example indicates the effectiveness of the derived algorithms.

Keywords: system identification; recursive algorithm; least squares; multivariable system; coupling identification concept (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2019
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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