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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.

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Keywords

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