Recursive coupled projection algorithms for multivariable output-error-like systems with coloured noises

• Published in: IET signal processing Vol. 14; no. 7; pp. 455 - 466
• Main Authors: Pan, Jian, Ma, Hao, Zhang, Xiao, Liu, Qinyao, Ding, Feng, Chang, Yufang, Sheng, Jie
• Format: Journal Article
• Language: English
• Published: The Institution of Engineering and Technology 01.09.2020
• Subjects: autoregressive moving average noise; autoregressive moving average processes; coloured noises; coupled relationship; coupling identification concept; gradient methods; gradient search; hierarchical identification principle; identification; identification model; input–output data; least squares approximations; multivariable control systems; multivariable output‐error‐like system; parameter estimation; parameter estimation algorithm; projection algorithm; recursive coupled projection algorithms; Research Article; state‐space methods; stochastic gradient algorithm; stochastic processes; least squares approximations; coupled relationship; coupling identification concept; autoregressive moving average processes; coloured noises; stochastic gradient algorithm; parameter estimation algorithm; projection algorithm; input–output data; identification; autoregressive moving average noise; multivariable control systems; gradient search; identification model; hierarchical identification principle; parameter estimation; multivariable output-error-like system; stochastic processes; gradient methods; recursive coupled projection algorithms; state-space methods
• ISSN: 1751-9675; 1751-9683; 1751-9683
• DOI: 10.1049/iet-spr.2019.0481

By combining the coupling identification concept with the gradient search, this study develops a partially coupled generalised extended projection algorithm and a partially coupled generalised extended stochastic gradient algorithm to estimate the parameters of a multivariable output-error-like system with autoregressive moving average noise from input–output data. The key is to divide the identification model into several submodels based on the hierarchical identification principle and to establish the parameter estimation algorithm by using the coupled relationship between these submodels. The simulation test results indicate that the proposed algorithms are effective.