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

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...

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Vydáno v:International journal of control, automation, and systems Ročník 15; číslo 3; s. 1189 - 1197
Hlavní autoři: Pan, Jian, Jiang, Xiao, Wan, Xiangkui, Ding, Wenfang
Médium: Journal Article
Jazyk:angličtina
Vydáno: 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
Témata:
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
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Shrnutí: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.
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http://link.springer.com/article/10.1007/s12555-016-0081-z
ISSN:1598-6446
2005-4092
DOI:10.1007/s12555-016-0081-z