  This paper presents a novel methodology that uses surrogate models in the
form of neural networks to reduce the computation time of simulation-based
optimization of a reference trajectory. Simulation-based optimization is
necessary when there is no analytical form of the system accessible, only
input-output data that can be used to create a surrogate model of the
simulation. Like many high-fidelity simulations, this trajectory planning
simulation is very nonlinear and computationally expensive, making it
challenging to optimize iteratively. Through gradient descent optimization, our
approach finds the optimal reference trajectory for landing a hypersonic
vehicle. In contrast to the large datasets used to create the surrogate models
in prior literature, our methodology is specifically designed to minimize the
number of simulation executions required by the gradient descent optimizer. We
demonstrated this methodology to be more efficient than the standard practice
of hand-tuning the inputs through trial-and-error or randomly sampling the
input parameter space. Due to the intelligently selected input values to the
simulation, our approach yields better simulation outcomes that are achieved
more rapidly and to a higher degree of accuracy. Optimizing the hypersonic
vehicle's reference trajectory is very challenging due to the simulation's
extreme nonlinearity, but even so, this novel approach found a 74%
better-performing reference trajectory compared to nominal, and the numerical
results clearly show a substantial reduction in computation time for designing
future trajectories.
