Classification of Flying Qualities with Machine Learning Methods

Sammanfattning: The primary objective of this thesis is to evaluate the prospect of machine learning methods being used to classify flying qualities based on simulator data (with the focus being on pitch maneuvers). If critical flying qualities could be identified earlier in the verification process, they can be further invested in and focused on with less cost for design changes of the flight control system. Information from manned simulations with given flying quality levels are used to create a replication of the performed pitch maneuver in a desktop simulator. The generated flight data is represented by different measures in the classification to separately train and test the machine learning models against the given flying quality level. The models used are Logistic Regression, Support Vector Machines with radial basis functions (RBF), linear and polynomial kernels along with Artificial Neural Networks.  The results show that the classifiers correctly identify at least 80% of cases with critical flying qualities. The classification shows that the statistical measures of the time signals and first order time derivatives of pitch, roll and yaw rates are enough for classification within the scope of this thesis. The different machine learning models show no significant difference in performance in the scope of this thesis. In conclusion, machine learning methods show good potential for classification of flying qualities, and could become an important tool for evaluating flying qualities of large amounts of simulations, in addition to manned simulations.