NDLI: State estimation and parameter identification method for dual-rate system based on improved Kalman prediction

Content Provider	Springer Nature Link
Author	Huang, Panfeng Lu, Zhenyu Liu, Zhengxiong
Copyright Year	2016
Abstract	For the dual-rate system, such as the process of space teleoperation whose control signals is partly determined by delayed feedback states, the state values and system parameters are coupled and influenced each other, which are hard to be estimated simultaneously. In this paper, we propose a novel method for this problem. Firstly, considering the asynchronism of the input and output sampling signals, an auxiliary model is modeled as a medium to the state and output functions. Secondly, the Kalman prediction algorithm is improved to estimate the state values at output signals of the dual-rate system. The general step is using the output estimated errors in original and auxiliary systems to modify the estimated state values of the auxiliary model, and then the unknown state values in original system is defined by the ones in auxiliary model. Based on improved Kalman algorithm and hierarchical identification algorithm, we present the detailed procedures of state estimation and parameter identification method for the dual-rate system. The processes of state estimation and parameter identification are calculated and modified alternately. Finally, the simulation results reveal that the state and parameters both approach to the real values and the state values converge faster than the parameters.
Starting Page	998
Ending Page	1004
Page Count	7
File Format	PDF
ISSN	15986446
Journal	International Journal of Control, Automation and Systems
Volume Number	14
Issue Number	4
e-ISSN	20054092
Language	English
Publisher	Institute of Control, Robotics and Systems and The Korean Institute of Electrical Engineers
Publisher Date	2016-08-04
Publisher Place	Bucheon, Seoul
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Auxiliary model dual-rate system hierarchical identification algorithm improved Kalman prediction parameter identification state estimation Control, Robotics, Mechatronics
Content Type	Text
Resource Type	Article
Subject	Control and Systems Engineering Computer Science Applications

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