Robustification of Learning Observers to Uncertainty Identification via Time-varying Learning Intensity

Chengxi Zhang, Choon Ki Ahn, Jin Wu, Wei He, Yi Jiang, Ming Liu

Research output: Contribution to journalArticlepeer-review

Abstract

This brief studies the simultaneous estimation of states and uncertainties in general continuous-time systems. In particular, we present a novel time-varying learning intensity (TLI) learning observer (LO). It has the advantage of inheriting the valuable properties of conventional LOs with a simple structure, i.e., the uncertainty estimation is achieved using simply one algebraic equation with low computational costs. The foremost difference in comparison with conventional LOs is the utilization of the TLI approach, which attenuates the overshooting response in the case of large estimation errors and obtains decent performance improvement. Simulations for constant and time-varying signals demonstrate a notable performance boost of TLI-LO.

Original languageEnglish
JournalIEEE Transactions on Circuits and Systems II: Express Briefs
DOIs
Publication statusAccepted/In press - 2021

Keywords

  • Circuits and systems
  • Estimation
  • Estimation error
  • Learning observer
  • Mathematical model
  • Observers
  • Time-varying systems
  • Uncertainty
  • time-varying learning intensity
  • uncertainty estimation.

ASJC Scopus subject areas

  • Electrical and Electronic Engineering

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