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Reinforcement learning for spacecraft attitude control
Vedant
,
James T. Allison
,
Matthew West
, Alexander Robin Mercantini Ghosh
Carl R. Woese Institute for Genomic Biology
Industrial and Enterprise Systems Engineering
Aerospace Engineering
Mechanical Science and Engineering
National Center for Supercomputing Applications (NCSA)
Climate, Meteorology and Atmospheric Sciences
Research output
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Contribution to journal
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Conference article
›
peer-review
Overview
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Keyphrases
Applied Torque
25%
Attitude Control
75%
Attitude Control System
100%
Attitude Controller
25%
Constraints on Control
25%
Control Agents
50%
Control Authority
25%
Control Policy
25%
Control Problem
50%
Controller
25%
Curriculum Learning
25%
Efficient Learning
25%
Existing Problems
25%
Feedback Control Strategy
25%
Feedback Controller
75%
Full-actuation
25%
General Attitudes
25%
General Reinforcement Learning
25%
Inertia
25%
Learning-based
50%
Mass Range
25%
Nominal Performance
25%
Numerical Problems
25%
Optimal Control Strategy
25%
Proximal Policy Optimization
25%
Quaternion
100%
Reaction Wheel
50%
Reinforcement Learning
100%
Reinforcement Learning Agent
50%
Reinforcement Learning Algorithm
25%
Saturation Constraint
25%
Spacecraft Attitude Control
100%
State Feedback Control
25%
System Dynamics
25%
System Issues
25%
Thruster
25%
Computer Science
Applied Torque
14%
Attitude-Controller
14%
Authority Control
14%
Control Strategy
28%
Established State
14%
Feedback Control
14%
Feedback Controller
42%
Learning Agent
28%
Optimization Policy
14%
Reinforcement Learning
100%
State Feedback
14%
System Dynamics
14%
Engineering
Proximal Policy Optimization
11%