Nonlinear state estimation in mobile robots using a fuzzy observer

Rodrigo Carrasco, Aldo Cipriano, Ricardo Carelli

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

4 Scopus citations


The performance of model based fault detection and isolation systems can be improved by designing more accurate estimation methods. This work presents a novel implementation of a nonlinear Kalman filter based on the Takagi-Sugeno (TS) fuzzy structure, for a mobile robot. First, a TS model is derived from the robot kinematic equations, which is optimized through genetic algorithms to obtain an accurate model. Based on this model, several linear Kalman filters are combined using fuzzy logic, designing a nonlinear state estimator. Finally, the resulting fuzzy nonlinear observer is compared with the conventional Extended Kalman Filter, showing an improvement in performance and robustness.

Original languageEnglish
Title of host publicationProceedings of the 16th IFAC World Congress, IFAC 2005
PublisherIFAC Secretariat
Number of pages6
ISBN (Print)008045108X, 9780080451084
StatePublished - 2005

Publication series

NameIFAC Proceedings Volumes (IFAC-PapersOnline)
ISSN (Print)1474-6670


  • Fuzzy modelling
  • Kalman filters
  • Mobile robots
  • Robotics
  • State estimation


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