2 citations · 3 across the 5 of their papers we have counts for
5 papers
MPC-based Deep Reinforcement Learning Method for Space Robotic Control with Fuel Sloshing Mitigation
Mahya Ramezani, M. Amin Alandihallaj, Barış Can Yalçın +2
This paper presents an integrated Reinforcement Learning (RL) and Model Predictive Control (MPC) framework for autonomous satellite docking with a partially filled fuel tank. Tradi…
CubeSat Orbit Insertion Maneuvering Using J2 Perturbation
M. Amin Alandihallaj, M. Reza Emami
The precise insertion of CubeSats into designated orbits is a complex task, primarily due to the limited propulsion capabilities and constrained fuel reserves onboard, which severe…
Autonomous Orbital Correction for Nano Satellites Using J2 Perturbation and LSTM Networks
Mahya Ramezani, Mohammadamin Alandihallaj, Andreas M. Hein
CubeSats offer a cost-effective platform for various space missions, but their limited fuel capacity and susceptibility to environmental disturbances pose significant challenges fo…
PPO-based Dynamic Control of Uncertain Floating Platforms in the Zero-G Environment
Mahya Ramezani, M. Amin Alandihallaj, Andreas M. Hein
In the field of space exploration, floating platforms play a crucial role in scientific investigations and technological advancements. However, controlling these platforms in zero-…
Safe Hierarchical Reinforcement Learning for CubeSat Task Scheduling Based on Energy Consumption
Mahya Ramezani, M. Amin Alandihallaj, Jose Luis Sanchez-Lopez +1
This paper presents a Hierarchical Reinforcement Learning methodology tailored for optimizing CubeSat task scheduling in Low Earth Orbits (LEO). Incorporating a high-level policy f…