Least squares based iterative parameter estimation algorithm for multivariable controlled ARMA system modelling with finite measurement data

Authors:
Bo Bao;Yingqin Xu;Jie Sheng;Ruifeng Ding
Affiliations:
Key Laboratory of Advanced Process Control for Light Industry (Ministry of Education), School of IoT Engineering, Jiangnan University, Wuxi 214122, PR China;Key Laboratory of Advanced Process Control for Light Industry (Ministry of Education), School of IoT Engineering, Jiangnan University, Wuxi 214122, PR China;Institute of Technology, University of Washington, Tacoma 98402-3100, USA;Key Laboratory of Advanced Process Control for Light Industry (Ministry of Education), School of IoT Engineering, Jiangnan University, Wuxi 214122, PR China
Venue:
Mathematical and Computer Modelling: An International Journal
Year:
2011

Citing 37
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Abstract

Difficulties of identification for multivariable controlled autoregressive moving average (ARMA) systems lie in that there exist unknown noise terms in the information vector, and the iterative identification can be used for the system with unknown terms in the information vector. By means of the hierarchical identification principle, those noise terms in the information vector are replaced with the estimated residuals and a least squares based iterative algorithm is proposed for multivariable controlled ARMA systems. The simulation results indicate that the proposed algorithm is effective.