A reliable system design for nondeterministic adaptive controllers in small UAV autopilots

Despite the tremendous attention Unmanned Aerial Vehicles (UAVs) have received in recent years for applications in transportation, surveillance, agriculture, and search and rescue, as well as their possible enormous economic impact, UAVs are still banned from fully autonomous commercial flights. One of the main reasons for this is the safety of the flight. Traditionally, pilots control the aircraft when complex situations emerge that even advanced autopilots are not able to manage. Artificial Intelligence based methods and Adaptive Controllers have proven themselves to be efficient in scenarios with uncertainties; however, they also introduce another concern: nondeterminism. This research endeavors to find a solution on how such algorithms can be utilized with higher reliability. Our method is based on using an adaptive model to verify the performance of a control parameter - proposed by a nondeterministic adaptive controller or AI-based optimizer - before it is deployed on the physical platform. Furthermore, a backup mechanism is engaged to recover the drone in case of failure. A Neural Network is employed to model the aircraft, and a Genetic Algorithm is utilized to optimize the PID controller of a quadcopter. The initial experimental results from test flights indicate the feasibility of this method.