Adaptive eXogenous Kalman Filter for Actuator Fault Diagnosis in Robotics and Autonomous Systems

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Abstract

This paper presents an algorithm for actuator fault diagnosis in robotics and autonomous systems under random uncertainties based on a cascade of nonlinear observer and linearized Kalman filter. The two-stage estimation method assumes uniform complete observability and controllability conditions and persistent excitation condition. To this end, we consider dynamical systems of robotics and autonomous systems with one-sided Lipschitz nonlinearity. To demonstrate the effectiveness of the proposed algorithm, numerical simulations in a single-link flexible joint robot are performed.

Original languageEnglish
Title of host publication2019 IEEE 7th International Conference on Control, Mechatronics and Automation, ICCMA 2019
Number of pages6
PublisherIEEE
Publication date10. Feb 2020
Pages162-167
ISBN (Print)978-1-7281-3788-9
ISBN (Electronic)9781728137872
DOIs
Publication statusPublished - 10. Feb 2020
Event7th IEEE International Conference on Control, Mechatronics and Automation, ICCMA 2019 - Delft, Netherlands
Duration: 6. Nov 20198. Nov 2019

Conference

Conference7th IEEE International Conference on Control, Mechatronics and Automation, ICCMA 2019
CountryNetherlands
CityDelft
Period06/11/201908/11/2019
SponsorDelft University of Technology, IEEE
Series2019 IEEE 7th International Conference on Control, Mechatronics and Automation, ICCMA 2019

Keywords

  • autonomous systems
  • fault diagnosis
  • Kalman filter

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