Variable forgetting factor-based adaptive Kalman filter with disturbance estimation considering observation noise reduction

Takashi Ohhira, Akira Shimada, Toshiyuki Murakami

Research output: Contribution to journalArticlepeer-review

Abstract

This paper addresses the influence reduction of quantization and observation noises in a disturbance observer (DOB) technique. DOB is a disturbance estimation method that makes control systems robust. However, in implementing low-resolution sensors, disturbance estimates from DOB are considerably influenced by observation and quantization noises. In this paper, a novel DOB design method for simultaneous estimation of state and unknown disturbances, including the reduction of noise influences, is proposed. The proposed method is divided into two components. The first component is a Kalman filter (KF)-based DOB for simultaneous estimation of state and unknown disturbances. To improve the estimation performance through the KF-based DOB, a forgetting factor-based adaptive KF (FAKF) was employed. The second component is an adaptive law for the forgetting factor in the FAKF. The adaptive law is used for balancing the estimation accuracy and observation noise reduction. Simulation results involving various types of noise environments demonstrate the effectiveness of the proposed method.

Original languageEnglish
Article number9486918
Pages (from-to)100747-100756
Number of pages10
JournalIEEE Access
Volume9
DOIs
Publication statusPublished - 2021

Keywords

  • Active noise reduction
  • Adaptive estimation
  • Adaptive filters
  • Kalman filters
  • Motion control
  • Motion estimation
  • Observers

ASJC Scopus subject areas

  • Computer Science(all)
  • Materials Science(all)
  • Engineering(all)

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