RII Track-4:@NSF: Surrogate-based Optimal Atmospheric Entry Guidance using High-fidelity Simulation Data

This Research Infrastructure Improvement Track-4 EPSCoR Research Fellows (RII Track-4) project would provide a fellowship to an Assistant professor and training for a graduate student at Iowa State University. This work would be conducted in collaboration with researchers at the NASA Ames Research Center. For planetary exploration, spacecraft must pass through atmospheric entry and powered descent stages to safely decelerate and accurately land.

Dating back to the Apollo mission era, Atmospheric Entry Guidance (AEG) controls the atmospheric drag of a spacecraft to achieve these objectives. Researchers have been working on optimal AEG to maximize fuel savings during the subsequent powered descent by terminating the entry phase with a minimum velocity trajectory. These optimal AEG methods have relied on ideal dynamic models with uncertain differences from the actual entry environment.

Therefore, more advanced and complex computational modeling and simulation technologies have been developed and utilized to minimize these discrepancies. Despite the advantages of Monte Carlo simulation, the increased complexity makes it an impractical method to quantify modeling uncertainty. In addition, the entry vehicle's onboard computer is not powerful enough to run optimal AEG with a complex model.

To address these limitations, this research aims to create a surrogate-based optimal guidance system, trained on high-fidelity data from complex simulations. The proposed guidance method enhances safety and efficiency in space exploration by reducing computational burden, saving spacecraft fuel, and enabling modeling uncertainty quantification.

The need for a new optimal AEG that reduces computational costs and enables modeling uncertainty quantification is evident. To satisfy this need, a surrogate-based AEG system, trained using high-fidelity simulation data from advanced Entry System Modeling (ESM), will be developed. For the development, preparing precise and computationally efficient training data that effectively encapsulates the core of atmospheric entry is crucial.

The proposed research will identify the dominant variables influencing AEG performance and generate the required training data using NASA's entry simulation tool. Various surrogate models for training, such as Gaussian Process Regression and Generalized Additive Model, will also be explored. The ultimate objective is establishing an onboard optimal AEG framework using a trained surrogate.

This framework can incorporate various feedback control algorithms to aid in planetary entry missions on Earth, Mars, Venus, and Titan. While prior research has focused on applying surrogates for subcomponent modeling, such as air density and fluid and aerothermal dynamics, this approach targets application to optimal guidance and will accelerate calculation speed for implementation on embedded platforms.

To reduce computations and training time, this research proposes a simplification method for the entry guidance profile that can also reduce the dimension of the training data. The success of this project will pave the way for extending the proposed surrogate-based technique to other space applications, such as spacecraft orbit or attitude guidance, and contribute significantly to extending the traditional space Guidance, Navigation, and Control (GNC) approach to data-based learning techniques.

This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.