Artificial Intelligence for Real-Time Tolerance to Critical Flight Data Errors in Large Aircraft

Abstract

The environment in the cockpit of large transport aircraft is highly complex due to an increasing number of automation systems. This complexity can cause pilots to become less aware of how systems interact. It becomes a severe issue when sensor or data failures occur, as such failures can contribute to a situation in which it is difficult for a pilot to assess what actually is happening and, possibly, how to resolve the problem. This paper presents a method, based on artificial intelligence, for identifying incorrect critical flight control data in real-time. A novel combination of reinforcement learning and a denoising autoencoder is proposed to identify failures and to provide inputs to the aircraft’s flight control and guidance systems, allowing for the correct maneuver to counter the failure and/or to avoid or recover from flight upsets. Tests in stall conditions with a partially blocked pitot tube show that the proposed method results in successful detection and recovery. The performance of the system without an autoencoder is compared to highlight the significant advantages, how this relates to creating systems with AI to improve situational awareness for pilots, and to execute appropriate automatic maneuvers to successfully counter the effect of sensor failures.