Rapid transfer of controllers between UAVs using learning-based adaptive control

Commonly used Proportional-Integral-Derivative based UAV flight controllers are often seen to provide adequate trajectory-tracking performance, but only after extensive tuning. The gains of these controllers are tuned to particular platforms, which makes transferring controllers from one UAV to other time-intensive. This paper formulates the problem of control-transfer from a source system to a transfer system and proposes a solution that leverages well-studied techniques in adaptive control. It is shown that concurrent learning adaptive controllers improve the trajectory tracking performance of a quadrotor with the baseline linear controller directly imported from another quadrotor whose inertial characteristics and throttle mapping are very different. Extensive flight-testing, using indoor quadrotor platforms operated in MIT's RAVEN environment, is used to validate the method.United States. Office of Naval Research. Multidisciplinary University Research Initiative (Grant N000141110688

Using learning from demonstration to enable automated flight control comparable with experienced human pilots

Modern autopilots fall under the domain of Control Theory which utilizes Proportional Integral Derivative (PID) controllers that can provide relatively simple autonomous control of an aircraft such as maintaining a certain trajectory. However, PID controllers cannot cope with uncertainties due to their non-adaptive nature. In addition, modern autopilots of airliners contributed to several air catastrophes due to their robustness issues. Therefore, the aviation industry is seeking solutions that would enhance safety. A potential solution to achieve this is to develop intelligent autopilots that can learn how to pilot aircraft in a manner comparable with experienced human pilots. This work proposes the Intelligent Autopilot System (IAS) which provides a comprehensive level of autonomy and intelligent control to the aviation industry. The IAS learns piloting skills by observing experienced teachers while they provide demonstrations in simulation. A robust Learning from Demonstration approach is proposed which uses human pilots to demonstrate the task to be learned in a flight simulator while training datasets are captured. The datasets are then used by Artificial Neural Networks (ANNs) to generate control models automatically. The control models imitate the skills of the experienced pilots when performing the different piloting tasks while handling flight uncertainties such as severe weather conditions and emergency situations. Experiments show that the IAS performs learned skills and tasks with high accuracy even after being presented with limited examples which are suitable for the proposed approach that relies on many single-hidden-layer ANNs instead of one or few large deep ANNs which produce a black-box that cannot be explained to the aviation regulators. The results demonstrate that the IAS is capable of imitating low-level sub-cognitive skills such as rapid and continuous stabilization attempts in stormy weather conditions, and high-level strategic skills such as the sequence of sub-tasks necessary to takeoff, land, and handle emergencies