F. Cheong and R. Lai (Australia)
Fuzzy Logic Controller, Evolutionary Algorithms, Differ ential Evolution, Control System, ATM
We describe a method for tuning Fuzzy Logic Controllers (FLCs) which is not based on expert knowledge or trial and error. The method consists of generating one or more con trol tables and using an Evolutionary Algorithm (EA) to optimize only the membership functions as it is straightfor ward to map the entries of the control table to a rule base. We tested our approach on two different controllers: a con troller which is not related to the family of PID controllers and a PD-like Mamdani FLC where the inputs are the error and the change in error. In both cases, our approach was successful as both controllers performed very well.
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