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.

翻译：本文提出了一种新颖的方法，利用神经网络形式的替代模型来减少基于仿真的参考轨迹优化计算时间。当系统没有解析形式可用，仅有可用于创建仿真替代模型的输入输出数据时，基于仿真的优化是必要的。与许多高保真仿真一样，该轨迹规划仿真具有高度非线性和计算成本高昂的特点，使得迭代优化极具挑战性。通过梯度下降优化，我们的方法找到了高超音速飞行器着陆的最优参考轨迹。与先前文献中用于创建替代模型的大型数据集不同，我们的方法专门设计用于最小化梯度下降优化器所需的仿真执行次数。我们证明了该方法比通过试错手动调整输入或随机采样输入参数空间的标准实践更高效。由于对仿真输入值的智能选择，我们的方法能以更快的速度获得更高精度的仿真结果。由于仿真的极端非线性，优化高超音速飞行器的参考轨迹极具挑战性，但即便如此，这种新方法找到的参考轨迹性能比标称值提高了74%，数值结果清楚表明，未来轨迹设计的计算时间显著减少。