Graduate Student Works
Dr. Riccardo Bevilacqua
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.
American Institute of Aeronautics and Astronautics, Inc.
AIAA SCITECH 2023 Forum
National Harbor, MD & Online
Scholarly Commons Citation
Mulekar, O. S., Cho, H., & Bevilacqua, R. (2023). Six-degree-of-freedom Optimal Feedback Control of Pinpoint Landing using Deep Neural Networks. , (). https://doi.org/10.2514/6.2023-0689
Grant or Award Name
NASA Space Technology Graduate Research Opportunity (Grant Number 80NSSC20K1188)