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An Online Reinforcement Learning Controller Design For Mars Ascent VehicleThis paper presents a neural network (NN) approximator-based online reinforcement learning (ORL) controller design for Mars Ascent Vehicle (MAV) under parametric variation and significant external disturbances. The ORL controller, which does not require any offline training, involves two NNs where an action NN produces optimal short-term control performance while a critic NN evaluates the performance of the action NN using an approximated cost function. The simulation example with comparisons against baseline Proportional-Integral-Derivative (PID) and gain scheduled pole-placement PID (GS-PP-PID) controllers show the proposed controller’s effectiveness and robustness under parametric variation and high external disturbances.
Document ID
20220000419
Acquisition Source
Marshall Space Flight Center
Document Type
Conference Paper
Authors
Han Woong Bae
(Marshall Space Flight Center Redstone Arsenal, Alabama, United States)
Date Acquired
January 21, 2022
Subject Category
Launch Vehicles And Launch Operations
Space Communications, Spacecraft Communications, Command And Tracking
Report/Patent Number
AAS 22-042
Meeting Information
Meeting: 44th Annual AAS Guidance, Navigation and Control Conference
Location: Breckenridge, Colorado
Country: US
Start Date: February 3, 2022
End Date: February 9, 2022
Sponsors: American Astronautical Society
Funding Number(s)
WBS: SCEX22022D
Distribution Limits
Public
Copyright
Work of the US Gov. Public Use Permitted.
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