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ARTICLE
State Estimation Moving Window Gradient Iterative Algorithm for Bilinear Systems Using the Continuous Mixed p-norm Technique
Wentao Liu, Junxia Ma, Weili Xiong*
Key Laboratory of Advanced Process Control for Light Industry (Ministry of Education), School of Internet of Things
Engineering, Jiangnan University, Wuxi, 214122, China
* Corresponding Author: Weili Xiong. Email:
(This article belongs to the Special Issue: Advances on Modeling and State Estimation for Industrial Processes)
Computer Modeling in Engineering & Sciences 2023, 134(2), 873-892. https://doi.org/10.32604/cmes.2022.020565
Received 30 November 2021; Accepted 28 March 2022; Issue published 31 August 2022
Abstract
This paper studies the parameter estimation problems of the nonlinear systems described by the bilinear state space
models in the presence of disturbances. A bilinear state observer is designed for deriving identification algorithms
to estimate the state variables using the input-output data. Based on the bilinear state observer, a novel gradient
iterative algorithm is derived for estimating the parameters of the bilinear systems by means of the continuous
mixed
p-norm cost function. The gain at each iterative step adapts to the data quality so that the algorithm has
good robustness to the noise disturbance. Furthermore, to improve the performance of the proposed algorithm, a
dynamic moving window is designed which can update the dynamical data by removing the oldest data and adding
the newest measurement data. A numerical example of identification of bilinear systems is presented to validate the
theoretical analysis.
Keywords
Cite This Article
Liu, W., Ma, J., Xiong, W. (2023). State Estimation Moving Window Gradient Iterative Algorithm for Bilinear Systems Using the Continuous Mixed
p-norm Technique.
CMES-Computer Modeling in Engineering & Sciences, 134(2), 873–892. https://doi.org/10.32604/cmes.2022.020565