ON-LINE RULE WEIGHTING FOR PID-TYPE FUZZY LOGIC CONTROLLERS USING EXTENDED KALMAN FILTER

Nasser Arghavani, Moayed Almobaied, Mujde Guzelkaya, Ibrahim Eksin

Research output: Contribution to journalArticlepeer-review

3 Citations (Scopus)

Abstract

In this study, we propose a method for online tuning of fuzzy rule weights of PID-type fuzzy logic controller via Extended Kalman Filter. In the application phase of Extended Kalman Filter to this new online parameter optimization setting, suitable state and observation vectors is needed; in this framework, the rule weights of the rule base are defined as the states and the output of the fuzzy system is defined as the observation vector. We apply the weight adjustment not to the consequent of the rule but instead to the complete rule. The effectiveness of the proposed on-line weight adjustment method is then demonstrated on linear and non-linear systems by simulations. The performance of the proposed tuning method is evaluated according to four performance measures and performance amelioration is observed in all measures. Moreover, the proposed on-line tuned PID-type FLC can handle the noise more successfully than the conventional one.

Original languageEnglish
Pages (from-to)6946-6951
Number of pages6
JournalIFAC-PapersOnLine
Volume50
Issue number1
DOIs
Publication statusPublished - Jul 2017

Bibliographical note

Publisher Copyright:
© 2017

Keywords

  • Extended Kalman Filter (EKF)
  • Fuzzy Logic Controller (FLC)
  • Fuzzy Rule Weights
  • Optimization
  • Proportional Integral Derivative (PID)

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