Student Name: Lander W. Schillinger Arana

Advisor: Dr. Sarah Li

Milestone: MS Thesis Final Examination (Defense)

Degree Program: Aerospace Engineering

Title: Optimal High- and Low-Thrust Trajectory Implementation in a Markov Decision Process Framework for Early Maneuver Decisions in Satellite Collision Avoidance

Abstract: We extend upon a Markov decision process (MDP) framework that autonomously makes guidance decisions for satellite collision avoidance maneuver (CAM) [Ferrara 2024]. In this framework, a reinforcement learning policy gradient (RL-PG) algorithm is used to enable direct optimization of guidance policy using historic CAM data. In addition to maintaining acceptable collision risks, this approach seeks to minimize the average propellant consumption of CAMs by making early maneuver decisions. To determine propellant consumption, a high-thrust impulsive phasing maneuver is utilized. The CAM is modeled as a continuous state, discrete action and finite horizon MDP, where the critical decision is determining when to initiate the maneuver. The MDP models decision rewards us using analytical models of collision risk, propellant consumption, and transit orbit geometry. By deciding to maneuver earlier than conventional methods, the Markov policy effectively favors CAMs that achieve comparable rates of collision risk reduction while consuming less propellant. In this thesis, we verify this framework using historical data of tracked conjunction events and conduct an extensive parameter-sensitivity study to investigate the influence of variations in parameters such as hard-body radius and phasing angle. When evaluated on synthetic conjunction events, the trained policy consumes significantly less propellant overall and per maneuver in comparison to a conventional cut-off policy that initiates maneuver 24 hours before the time of closest approach (TCA). On historical conjunction events, the trained policy consumes more propellant overall but consumes less propellant per maneuver. For both historical and synthetic conjunction events, the trained policy is slightly more conservative in identifying conjunctions events that warrant CAMs in comparison to cutoff policies. That is to say, maintaining the accuracy of the decision space within safe margins comparable to the cut-off policy. Furthermore, we explore a constrained propellant consumption optimization to determine the optimal transit period for high-thrust CAMs to optimize the current MDP model and optimize its performance. We demonstrate that the minimum propellant transit period results in a significant reduction in propellant mass across the board for the optimal policy. Additionally, we develop an alternative low-thrust maneuver with a continuous thrust model and implement it into the MDP model via a numerically linearized approximation of the optimal propellant solution versus input phasing and maneuver time requirements and compare its performance to the high-thrust case.

Date and time: 2026-04-09, 4:00 pm

Location: MK-317 Conference Room

Committee:
Dr. Sarah Li (advisor), School of Aerospace Engineering
Dr. Lakshmi Sankar, School of Aerospace Engineering
Dr. Glenn Lightsey, School of Aerospace Engineering
Dr. Brian Gunter, School of Aerospace Engineering
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