Recursive Least-squares Estimation for Multivariable Systems Based on the Maximum Likelihood Principle

This paper studies the identification problem of multivariable controlled autoregressive moving average systems. For the case with a parameter matrix and an unmeasurable vector in the system identification model, we transform the model into several submodels based on the number of the outputs. A max...

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Bibliographic Details
Published in:International journal of control, automation, and systems 2020, Automation, and Systems, 18(2), 1, pp.503-512
Main Authors: Xia, Huafeng, Yang, Yongqing, Ding, Feng
Format: Article
Language:English
Subjects:
Algorithms
Autoregressive moving average
Computer simulation
Control
Engineering
Least squares
Mathematical models
Matrix algebra
Matrix methods
Mechatronics
Multivariable control
Parameter identification
Regular Papers
Robotics
System identification
제어계측공학
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Summary:This paper studies the identification problem of multivariable controlled autoregressive moving average systems. For the case with a parameter matrix and an unmeasurable vector in the system identification model, we transform the model into several submodels based on the number of the outputs. A maximum likelihood-based recursive least-squares algorithm is derived to identify the parameters of each submodel. A multivariable recursive extended least-squares algorithm is provided as a comparison. The effectiveness of the proposed identification algorithm is verified by simulation examples.
ISSN:1598-6446
2005-4092
DOI:10.1007/s12555-018-0912-1