An Intelligent Autopilot System that learns flight emergency procedures by imitating human pilots

Abstract

We propose an extension to the capabilities of the Intelligent Autopilot System (IAS) from our previous work, to be able to learn handling emergencies by observing and imitating human pilots. The IAS is a potential solution to the current problem of Automatic Flight Control Systems of being unable to handle flight uncertainties, and the need to construct control models manually. A robust Learning by Imitation approach is proposed which uses human pilots to demonstrate the task to be learned in a flight simulator while training datasets are captured from these demonstrations. The datasets are then used by Artificial Neural Networks to generate control models automatically. The control models imitate the skills of the human pilot when handling flight emergencies including engine(s) failure or fire, Rejected Take Off (RTO), and emergency landing, while a flight manager program decides which ANNs to be fired given the current condition. Experiments show that, even after being presented with limited examples, the IAS is able to handle such flight emergencies with high accuracy.