Two-stage Gradient-based Iterative Estimation Methods for Controlled Autoregressive Systems Using the Measurement Data

This paper considers the parameter identification problems of controlled autoregressive systems using observation information. According to the hierarchical identification principle, we decompose the controlled autoregressive system into two subsystems by introducing two fictitious output variables....

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Bibliographic Details
Published inInternational journal of control, automation, and systems Vol. 18; no. 4; pp. 886 - 896
Main Authors Ding, Feng, Lv, Lei, Pan, Jian, Wan, Xiangkui, Jin, Xue-Bo
Format Journal Article
LanguageEnglish
Published Bucheon / Seoul Institute of Control, Robotics and Systems and The Korean Institute of Electrical Engineers 01.04.2020
Springer Nature B.V
제어·로봇·시스템학회
Subjects
Control
Control methods
Engineering
Innovations
Iterative algorithms
Mechatronics
Parameter identification
Performance enhancement
Robotics
Subsystems
제어계측공학
hierarchical identification
mathematical modeling
Gradient search
parameter estimation
iterative technique
multi-innovation identification
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Summary:This paper considers the parameter identification problems of controlled autoregressive systems using observation information. According to the hierarchical identification principle, we decompose the controlled autoregressive system into two subsystems by introducing two fictitious output variables. Then a two-stage gradient-based iterative algorithm is proposed by means of the iterative technique. In order to improve the performance of the tracking the time-varying parameters, we derive a two-stage multi-innovation gradient-based iterative algorithm based on the multi-innovation identification theory. Finally, an example is provided to illustrate the effectiveness of the proposed algorithms.
Bibliography:http://link.springer.com/article/10.1007/s12555-019-0140-3
ISSN:1598-6446
2005-4092
DOI:10.1007/s12555-019-0140-3