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


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
Publication dateNov 2019
Article number8987708
ISBN (Print)978-1-7281-4782-6
ISBN (Electronic)978-1-7281-4781-9
Publication statusPublished - Nov 2019
EventInternational Conference on System Reliability and Safety - Rom, Italy
Duration: 20. Nov 201922. Nov 2019


ConferenceInternational Conference on System Reliability and Safety


  • Fault diagnosis
  • Kalman filter
  • Nonlinear systems


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