Kalman filtering for TS fuzzy state estimation

Sun Young Noh, Jin Bae Park, Young Hoon Joo

Research output: Chapter in Book/Report/Conference proceedingConference contribution

3 Citations (Scopus)


This paper studies the T-S fuzzy model-based state estimator which the dynamic system can be approximated as linear system. It is suggested for a steady state estimator using standard Kalman filter theory. In that case, the steady state of nonlinear system can be represented by the T-S fuzzy model structure, which is further rearranged to give a set of a linear model. The steady state solutions can be found for a liner model method and dynamic system can be approximated as locally linear system. And then, linear modeled filter is corrected by the fuzzy gain which is a fuzzy system using the relation between the filter residual and its variation. It reduces the measurement residual with noise. Finally, the proposed state estimator is demonstrated on a truck-trailer.

Original languageEnglish
Title of host publication2006 SICE-ICASE International Joint Conference
Number of pages4
Publication statusPublished - 2006
Event2006 SICE-ICASE International Joint Conference - Busan, Korea, Republic of
Duration: 2006 Oct 182006 Oct 21

Publication series

Name2006 SICE-ICASE International Joint Conference


Other2006 SICE-ICASE International Joint Conference
Country/TerritoryKorea, Republic of

All Science Journal Classification (ASJC) codes

  • Computer Science Applications
  • Control and Systems Engineering
  • Electrical and Electronic Engineering


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