Adaptive time-varying parameter estimation of nonlinearly parameterized systems

Fujin Luan, Xinkai Chen, Jing Na, Yashan Xing, Guanbin Gao

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Although adaptive estimation of constant parameter has been studied for decades, most of existing methods cannot achieve satisfactory performance for timevarying parameters in particular for nonlinearly parameterized systems. In this paper, a novel adaptive parameter estimation framework based on parameter estimation errors, is proposed to estimate time-varying parameters for generally nonlinearly parameterized systems. The main idea is to restructure the system as a linearly parameterized form through Taylor expansion. Following the introduction of auxiliary filtered variables, the estimation errors of the unknown parameter are derived and used to design an adaptive law to achieve uniform ultimate boundedness under the persistent excitation condition. Furthermore, it is verified that the suggested methods are robust against bounded disturbances. Numerical simulation results validate the effectiveness of adaptive time-varying parameter estimation methods.

Original languageEnglish
Title of host publicationProceedings of 2023 Chinese Intelligent Systems Conference - Volume II
EditorsYingmin Jia, Weicun Zhang, Yongling Fu, Jiqiang Wang
PublisherSpringer Science and Business Media Deutschland GmbH
Pages295-304
Number of pages10
ISBN (Print)9789819968817
DOIs
Publication statusPublished - 2023
Event19th Chinese Intelligent Systems Conference, CISC 2023 - Ningbo, China
Duration: 2023 Oct 142023 Oct 15

Publication series

NameLecture Notes in Electrical Engineering
Volume1090 LNEE
ISSN (Print)1876-1100
ISSN (Electronic)1876-1119

Conference

Conference19th Chinese Intelligent Systems Conference, CISC 2023
Country/TerritoryChina
CityNingbo
Period23/10/1423/10/15

Keywords

  • Adaptive estimation
  • Nonlinear parameterization
  • System modeling
  • Time-varying parameters

ASJC Scopus subject areas

  • Industrial and Manufacturing Engineering

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