ABC and DE Algorithms based Fuzzy Modeling of Flight Data for Speed and Fuel Computation

It is crucial to evaluate the information obtained from the sensors in a fast and accurate manner in air vehicles exposed to many internal and external influences during their flights. The effectiveness and flexibility of the reasoning method comes to the forefront when the pilot or flight control system makes the necessary decisions in critical situations. The method used should be able to provide the nearest response to the decision that the pilot has to give, even when working with complex, unclear or incomplete data. Thus, it is not the correct approach to think that sensorial measurements and methods that describe them through a model are separate from each other. In this study, a fuzzy rule based model is designed using some important measurement data belonging to a flight control system for simultaneously computation of speed and fuel parameters. The fuzzy model parameters are determined by using artificial bee colony (ABC) and differential evolution (DE) algorithms in the modeling process in which actual flight data of Boeing B-767-200ER type aircraft are used. To demonstrate the efficiency of the modeling approach and the algorithms, fuzzy model structures having 3 inputs-and-2 outputs for different rule numbers are tested. The inputs of the fuzzy models are considered as altitude, weight, and engine pressure ratio. On the other hand, parameters of the flight speed and fuel amount are used for outputs of the models. The results achieved from the models are comparatively presented with each other and actual values