Juan Contreras
Fuzzy identification, fuzzy control, ship steering dynamics, ship control
In this paper we present a new method to generate fuzzy controller from training data. The antecedent fuzzy sets are defined by triangular membership function with 0.5 interpolations avoiding the presence of complex overlapping that happens in other methods. Averaging operator is used for combining fuzzy rules and the least square method is used to adjust singleton consequents. The maximum number of fuzzy rules is calculated summing the number of membership function of each input variable; it means that the number of fuzzy rules is reduced with respect to classical fuzzy systems. This method is used to generate and validate the course driver of a tanker, with a length of 350 meters, at different load conditions.
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