Adaptive Estimation for Uncertain Nonlinear Systems: A Sliding-Mode Observer Approach

R. Franco, H. Rios, D. Efimov, W. Perruquetti

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

This paper deals with the problem of adaptive estimation, i.e. the simultaneous estimation of the state and parameters, for a class of uncertain nonlinear systems. A nonlinear adaptive sliding-mode observer is proposed based on a nonlinear parameter estimation algorithm. The nonlinear parameter estimation algorithm provides a rate of convergence faster than exponential while the sliding-mode observer ensures ultimate boundness for the state estimation error attenuating the effects of the external disturbances. Linear matrix inequalities (LMIs) are provided for the synthesis of the adaptive observer and some simulation results show the feasibility of the proposed approach.

Original languageEnglish
Title of host publication2018 IEEE Conference on Decision and Control, CDC 2018
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages5506-5511
Number of pages6
ISBN (Electronic)9781538613955
DOIs
StatePublished - 18 Jan 2019
Event57th IEEE Conference on Decision and Control, CDC 2018 - Miami, United States
Duration: 17 Dec 201819 Dec 2018

Publication series

NameProceedings of the IEEE Conference on Decision and Control
Volume2018-December
ISSN (Print)0743-1546

Conference

Conference57th IEEE Conference on Decision and Control, CDC 2018
Country/TerritoryUnited States
CityMiami
Period17/12/1819/12/18

Bibliographical note

Publisher Copyright:
© 2018 IEEE.

Keywords

  • Adaptive Observer
  • Nonlinear Systems
  • Sliding-Modes

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