Aurora Flight Sciences proposes to develop a system for robust engine control based on incremental sampling, specifically Rapidly-Expanding Random Tree (RRT) algorithms. In this concept, the task of accelerating or decelerating the engine is treated as a path planning exercise. The control system actively searches for actuator inputs that allow the engine to traverse power settings without entering undesired regions of operation. The search is based on the sequential construction of control actions that satisfy feasibility constraints given the system dynamics. These algorithms have been proven to converge to the optimal solution through repeated iteration. RRTs allow for an efficient search of the solution space, reducing the computational expense of determining the best sequence of inputs with which to control the engine. This allows an efficient, online method for an engine to adapt and recalibrate to unexpected operational conditions.