A filtering based multi-innovation extended stochastic gradient algorithm for multivariable control systems

• Published in: International journal of control, automation, and systems Vol. 15; no. 3; pp. 1189 - 1197
• Main Authors: Pan, Jian, Jiang, Xiao, Wan, Xiangkui, Ding, Wenfang
• Format: Journal Article
• Language: English
• Published: Bucheon / Seoul Institute of Control, Robotics and Systems and The Korean Institute of Electrical Engineers 01.06.2017; Springer Nature B.V; 제어·로봇·시스템학회
• Subjects: Algorithms; Autoregressive moving average; Computer simulation; Control; Control systems; Engineering; Filtering systems; Filtration; Innovations; Mathematical models; Mechatronics; Multivariable control; Parameter estimation; Parameter identification; Regular Papers; Robotics; Simulation; Stochastic models; Studies; 제어계측공학; stochastic system; parameter estimation; filtering; gradient search; Estimation algorithm; multi-innovation identification
• ISSN: 1598-6446; 2005-4092
• DOI: 10.1007/s12555-016-0081-z

For a multivariable system with moving average noise (i.e., a multivariable controlled autoregressive moving average system), this paper proposes a filtering based extended stochastic gradient (ESG) algorithm and a filtering based multi-innovation ESG algorithm for improving the parameter estimation accuracy. The key is using the filtering technique and the multi-innovation identification theory. The proposed algorithms can identify the parameters of the system model and the noise model. The filtering based multi-innovation ESG algorithm can give more accurate parameter estimates. The numerical simulation results demonstrate that the proposed algorithms work well.