  In this paper, we consider the problem of controlling an underactuated system
in unknown, and potentially adversarial environments. The emphasis will be on
autonomous aerial vehicles, modelled by Dubins dynamics. The proposed control
law is based on a variable integrator via online prediction for target
tracking. To showcase the efficacy of our method, we analyze a pursuit evasion
game between multiple autonomous agents. To obviate the need for perfect
knowledge of the evader's future strategy, we use a deep neural network that is
trained to approximate the behavior of the evader based on measurements
gathered online during the pursuit.
