Surrogate Neural Networks for Efficient Simulation-based Trajectory Planning Optimization

03/30/2023
by   Evelyn Ruff, et al.
0

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.

READ FULL TEXT

page 1

page 6

research
04/23/2022

Use of Multifidelity Training Data and Transfer Learning for Efficient Construction of Subsurface Flow Surrogate Models

Data assimilation presents computational challenges because many high-fi...
research
02/11/2020

On transfer learning of neural networks using bi-fidelity data for uncertainty propagation

Due to their high degree of expressiveness, neural networks have recentl...
research
11/23/2021

Asteroid Flyby Cycler Trajectory Design Using Deep Neural Networks

Asteroid exploration has been attracting more attention in recent years....
research
08/15/2020

Enhanced data efficiency using deep neural networks and Gaussian processes for aerodynamic design optimization

Adjoint-based optimization methods are attractive for aerodynamic shape ...
research
11/30/2021

Surrogate-based optimization using an artificial neural network for a parameter identification in a 3D marine ecosystem model

Parameter identification for marine ecosystem models is important for th...
research
08/14/2023

Autocalibration of the E3SM version 2 atmosphere model using a PCA-based surrogate for spatial fields

Global Climate Model (GCM) tuning (calibration) is a tedious and time-co...
research
09/07/2022

SmOOD: Smoothness-based Out-of-Distribution Detection Approach for Surrogate Neural Networks in Aircraft Design

Aircraft industry is constantly striving for more efficient design optim...

Please sign up or login with your details

Forgot password? Click here to reset