Inverse fuzzy model control with online adaptation via Big Bang-Big Crunch optimization

Kumbasar T., Yesil E., Eksin İ., Guezelkaya M.

3rd IEEE International Symposium on Control, Communications and Signal Processing (ISCCSP 2008), St Julians, Malta, 12 - 14 March 2008, pp.697-702 identifier identifier

  • Publication Type: Conference Paper / Full Text
  • Doi Number: 10.1109/isccsp.2008.4537313
  • City: St Julians
  • Country: Malta
  • Page Numbers: pp.697-702
  • Istanbul Technical University Affiliated: Yes


Fuzzy logic modeling is a powerful tool in representing nonlinear systems. Moreover, inverse fuzzy model can be used as a controller in an open loop fashion to produce perfect control. However, in the case of modeling mismatches and disturbances that might occur on the system, open loop control would not be sufficient. In that case, the modeling errors and disturbances could be compensated by Internal Model Control (IMC) with an on-line model adaptation scheme. The on-line adaptation is usually accomplished via recursive least square algorithm. In this study, Big Bang-Big Crunch (BB-BC) optimization method, which has a low computational time and high convergence speed, has been used as an on-line adaptation scheme. The inverse fuzzy model based IMC and the BB-BC optimization method based adaptation have been implemented and tested on a real time beating process system.