Discrete-time Kalman filter for Takagi–Sugeno fuzzy models

Discrete-time Kalman filter for Takagi–Sugeno fuzzy models In this work, the Kalman Filter (KF) and Takagi–Sugeno fuzzy modeling technique are combined to extend the classical Kalman linear state estimation to the nonlinear field. The framework for such extension is given, and in this sense the discrete-time fuzzy Kalman filter (DFKF) is obtained. It will be shown that the fuzzy version gives some advantages when is compared with the Extended Kalman Filter (EKF), which is the most typical extension of the KF to the nonlinear field. The proposed approach provides a significantly smaller processing time than the processing time of the EKF while the mean square error is also reduced. Finally, some examples, such as the Lorenz chaotic attractor and under actuated mechatronic system (pendubot), are used to compare the DFKF and EKF. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Evolving Systems Springer Journals

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/lp/springer_journal/discrete-time-kalman-filter-for-takagi-sugeno-fuzzy-models-IeDHRGn8nG
Publisher
Springer Berlin Heidelberg
Copyright
Copyright © 2017 by Springer-Verlag Berlin Heidelberg
Subject
Engineering; Complexity; Artificial Intelligence (incl. Robotics); Complex Systems
ISSN
1868-6478
eISSN
1868-6486
D.O.I.
10.1007/s12530-017-9181-0
Publisher site
See Article on Publisher Site

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