Generating fuzzy controllers for ship steering

In this paper we present a new method to generate fuzzy controller from training data. The antecedent partition uses triangular sets with 0.5 interpolations avoiding the presence of complex overlapping that happens in other methods. Singleton consequents are employed and least square method is used to adjust the consequents. This approach is not a hybrid system and does not employ other techniques, like neural network or genetic algorithm. The applicability of the proposed approach is demonstrated by application for controlling the directional heading of a cargo ship.