Submitting Campus

Daytona Beach

Student Status

Graduate

Class

Graduate Student Works

Advisor Name

Dr. Riccardo Bevilacqua

Abstract/Description

Machine learning regression techniques have shown success at feedback control to perform near-optimal pinpoint landings for low fidelity formulations (e.g. 3 degree-of-freedom). Trajectories from these low-fidelity landing formulations have been used in imitation learning techniques to train deep neural network policies to replicate these optimal landings in closed loop. This study details the development of a near-optimal, neural network feedback controller for a 6 degree-of-freedom pinpoint landing system. To model disturbances, the problem is cast as either a multi-phase optimal control problem or a triple single-phase optimal control problem to generate examples of optimal control through the presence of disturbances. By including these disturbed examples and leveraging imitation learning techniques, the loss of optimality is reduced for pinpoint landing scenario.

Document Type

Article

DOI

https://doi.org/10.2514/6.2023-0689

Publisher

American Institute of Aeronautics and Astronautics, Inc.

Location

National Harbor, MD & Online

Grant or Award Name

NASA Space Technology Graduate Research Opportunity (Grant Number 80NSSC20K1188)

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