Adaptive Extended Kalman Filter for Actuator Fault Diagnosis

M. Skriver, Jannes Helck, A. Hasan

Research output: Chapter in Book/Report/Conference proceedingArticle in proceedingsResearchpeer-review

Abstract

This paper presents an algorithm for actuator fault diagnosis of nonlinear systems. The method is derived under classical uniform complete observability, controllability, and persistent excitation condition. To this end, the fault is modeled as a constant or a piecewise constant parameter vector. The diagnosis algorithm is based on the Extended Kalman Filter (EKF) with an explicit update law for the actuator fault estimation. From a practical point of view, the proposed algorithm can be used for general nonlinearity. To illustrate the effectiveness of the diagnosis algorithm, we present two numerical examples using the models of an autonomous car and a gantry crane.
Original languageEnglish
Title of host publication2019 4th International Conference on System Reliability and Safety, ICSRS 2019
Number of pages6
PublisherIEEE
Publication dateNov 2019
Pages339-344
Article number8987708
ISBN (Print)978-1-7281-4782-6
ISBN (Electronic)978-1-7281-4781-9
DOIs
Publication statusPublished - Nov 2019
EventInternational Conference on System Reliability and Safety - Rom, Italy
Duration: 20. Nov 201922. Nov 2019

Conference

ConferenceInternational Conference on System Reliability and Safety
CountryItaly
CityRom
Period20/11/201922/11/2019

Keywords

  • Fault diagnosis
  • Kalman filter
  • Nonlinear systems

Fingerprint Dive into the research topics of 'Adaptive Extended Kalman Filter for Actuator Fault Diagnosis'. Together they form a unique fingerprint.

Cite this