Robust Neurocontrol for Autonomous Dynamic Soaring

Abstract

The flight endurance of small unmanned aerial vehicles can be significantly extended through the exploitation of naturally occurring wind phenomena. However, due to the limited computational hardware on board such aircraft and the uncertain, stochastic nature of real-world environments, there is a need for efficient and robust strategies that exhibit generalized behavior. In addressing these objectives, recent efforts have explored the use of artificial intelligence training algorithms and neural networks for the design of autonomous control schemes that exploit such wind phenomena. This study incorporates the Neuroevolution of Augmenting Topologies algorithm with domain randomization to train robust neurocontrollers that can control an aircraft along sustained traveling dynamic soaring trajectories in the presence of uncertainties and disturbances. This work presents the developed strategy for integrating robustness in neural network control systems, provides a method for quantifying and comparing robustness, and introduces an approach for identifying the network characteristics that contribute to the evolved robust behavior.