A Comparative Study of Machine Learning Techniques for Aircraft Loss of Control Prediction*

This paper presents a comparative analysis of different machine learning algorithms for aircraft loss of control (LOC) prediction. Aircraft LOC is a flight dynamics and control phenomenon that has been associated with a significant number of aircraft accidents worldwide. The NASA Generic Transport Model (GTM) is used as a representative aircraft to study the efficacy of machine learning based prediction algorithms under LOC scenarios. Extensive simulation of the GTM model in normal and LOC flight conditions are carried out to generate training and testing data for the machine learning algorithms. Different machine learning methods, viz. neural networks, support vector machines, decision trees, ensemble subspace kNN, ensemble boosted trees, and ensemble bagged trees are examined for their effectiveness in predicting LOC. A multi class classification problem is solved to build the machine learning models. It is observed from the analysis of prediction results that neural network method provides the best predictive model. Furthermore, the neural network predictive model performs quite well in correctly classifying the flight conditions under LOC, with an overall accuracy of 97%.