Title
Robust Global Identification of LPV Errors-in-Variables Systems With Incomplete Observations
Abstract
This article develops a robust global strategy for identifying the linear parameter varying (LPV) errors-in-variables (EIVs) systems subjected to randomly missing observations and outliers. The parameter interpolated LPV autoregressive exogenous model with an uncertain/noisy input is investigated and a nonlinear state-space model is considered for the input generation model (IGM). The parameters estimation of the LPV EIV systems with nonideal observations is realized using the expectation–maximization algorithm which is particular effective for the incomplete data issue. To ensure the robustness in the identification, the Student’s t-distribution which is characterized by its adjustable degree of freedom, is used to handle the measurement non-normality. Since the posterior distributions of the latent states in the IGM are also involved in the identification process and they are difficult to calculate directly, the particle filter is introduced to recursively approximate them instead. Finally, the verification examples are given to demonstrate the effectiveness of the developed strategy.
Year
DOI
Venue
2022
10.1109/TSMC.2021.3071137
IEEE Transactions on Systems, Man, and Cybernetics: Systems
Keywords
DocType
Volume
Expectation–maximization (EM) algorithm,linear parameter varying (LPV) errors-in-variable (EIV) systems,particle filter,randomly missing observations,robust global approach,Student’s t-distribution
Journal
52
Issue
ISSN
Citations 
6
2168-2216
0
PageRank 
References 
Authors
0.34
24
3
Name
Order
Citations
PageRank
Xin Liu13311.67
Guangjie Han21890172.76
xianqiang yang35910.79