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A Bi-fidelity Strategy for Optimization under Uncertainty with Applications to Aircraft Trajectory Optimization

AIAA 2024-1025
Session: Uncertainty Quantification and Analysis of Complex Aerospace Systems (joint NDA/GNC)
Published Online:https://doi.org/10.2514/6.2024-1025
Abstract:

We test a simple bi-fidelity strategy for accelerating trajectory optimization under uncertainty in the presence of robust constraints. Our approach combines the accuracy of high-fidelity models with the computational efficiency of low-fidelity counterparts to increase the accuracy of constraint computation for a given cost. Specifically, we accelerate sample average approaches that use high-fidelity models, with additional samples of low-fidelity models for assessing whether or not a risk constraint is violated. The low-fidelity model's purpose is thus to reduce the uncertainty in the estimate of constraint violation, at the expense of introduction of bias. Our empirical test on a simulation of glider dynamics tasked with avoiding a region indicates an order-of-magnitude less failures using the bi-fidelity strategy.