Robust adaptive estimators for nonlinear systems

Hamimi Fadziati Binti Abdul Wahab, Reza Katebi

Research output: Contribution to conferencePaperpeer-review

3 Citations (Scopus)
114 Downloads (Pure)


This paper is concerned with the development of new adaptive nonlinear estimators which incorporate adaptive estimation techniques for system noise statistics with the robust technique. These include Extended H∞ Filter (EHF), State Dependent H∞ Filter (SDHF) and Unscented H∞ Filter (UHF). The new filters are aimed at compensating the nonlinear dynamics as well as the system modeling errors by adaptively estimating the noise statistics and unknown parameters. For comparison purposes, this adaptive technique has also being applied to the Kalman-based filter which include extended Kalman filter (EKF), state dependent Kalman filter (SDKF) and Unscented Kalman filter (UKF). The performance of the proposed estimators is demonstrated using a two-state Van der Pol oscillator as a simulation example.
Original languageEnglish
Number of pages6
Publication statusPublished - 11 Oct 2013
EventConference on Control and Fault-Tolerant Systems (SysTol), 2013 - Nice, France
Duration: 9 Oct 201311 Oct 2013


ConferenceConference on Control and Fault-Tolerant Systems (SysTol), 2013


  • fault monitoring
  • nonlinear
  • adaptive nonlinear estimators
  • Kalman-based filter
  • state dependent Kalman filter
  • nonlinear systems

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