Inverse fuzzy Model Control with online adaptation via Big Bang-Big Crunch optimization

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 (INIC) 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 heating process system.