Partially-coupled least squares based iterative parameter estimation for multi-variable output-error-like autoregressive moving average systems

• Published in: IET control theory & applications Vol. 13; no. 18; pp. 3040 - 3051
• Main Authors: Ma, Hao, Pan, Jian, Ding, Feng, Xu, Ling, Ding, Wenfang
• Format: Journal Article
• Language: English
• Published: The Institution of Engineering and Technology 17.12.2019
• Subjects: autoregressive moving average noise; autoregressive moving average processes; gradient methods; identification problem; information vector; iterative methods; iterative parameter estimation; iterative search; least squares approximations; multivariable output‐error‐like autoregressive moving average systems; multivariable output‐error‐like system; parameter estimation; parameter vector; recursive least squares algorithm; redundant parameter estimates; Research Article; unknown variables; vectors; iterative methods; least squares approximations; autoregressive moving average processes; multivariable output-error-like autoregressive moving average systems; recursive least squares algorithm; redundant parameter estimates; vectors; autoregressive moving average noise; parameter vector; iterative search; information vector; identification problem; parameter estimation; multivariable output-error-like system; iterative parameter estimation; unknown variables; gradient methods
• ISSN: 1751-8644; 1751-8652
• DOI: 10.1049/iet-cta.2019.0112

This study considers the parameter estimation of a multi-variable output-error-like system with autoregressive moving average noise. In order to solve the problem of the information vector containing unknown variables, a least squares-based iterative algorithm is proposed by using the iterative search. The original system is divided into several subsystems by using the decomposition technique. However, the subsystems contain the same parameter vector, which poses a challenge for the identification problem, the approach taken here is to use the coupling identification concept to cut down the redundant parameter estimates. In addition, the recursive least squares algorithm is provided for comparison. The simulation results indicate that the proposed algorithms are effective.