TR2025-100
Station-Keeping on Near-Rectilinear Halo Orbits via Full-State Targeting Model Predictive Control
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- "Station-Keeping on Near-Rectilinear Halo Orbits via Full-State Targeting Model Predictive Control", American Control Conference (ACC), July 2025.BibTeX TR2025-100 PDF
- @inproceedings{Shimane2025jul,
- author = {Shimane, Yuri and {Di Cairano}, Stefano and Ho, Koki and Weiss, Avishai},
- title = {{Station-Keeping on Near-Rectilinear Halo Orbits via Full-State Targeting Model Predictive Control}},
- booktitle = {American Control Conference (ACC)},
- year = 2025,
- month = jul,
- url = {https://www.merl.com/publications/TR2025-100}
- }
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- "Station-Keeping on Near-Rectilinear Halo Orbits via Full-State Targeting Model Predictive Control", American Control Conference (ACC), July 2025.
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MERL Contacts:
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Research Areas:
Abstract:
We develop a model predictive control (MPC) policy for station-keeping (SK) on a Near-Rectilinear Halo Orbit (NRHO). Leveraging the controllability obtained from a control horizon consisting of two maneuvers, the proposed MPC policy achieves full-state tracking of a reference NRHO. By spacing the maneuvers one revolution apart, our method abides by the typical mission requirement that at most one maneuver is utilized for SK during each NRHO revolution. Through full-state tracking, the proposed policy does not suffer from phase deviation in the along-track direction of the reference NRHO, a common drawback of existing SK methods with a single maneuver per revolution. Numerical simulations demonstrate that the proposed approach successfully maintains the spacecraft’s motion both in space and phase along the NRHO, with tighter tracking than state-of-the-art SK methods and comparable delta-V performance.
Related News & Events
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NEWS MERL researchers present 13 papers at ACC 2025 Date: July 8, 2025 - July 10, 2025
Where: Denver, USA
MERL Contacts: Ankush Chakrabarty; Vedang M. Deshpande; Stefano Di Cairano; Purnanand Elango; Jordan Leung; Saviz Mowlavi; Diego Romeres; Abraham P. Vinod; Yebin Wang; Avishai Weiss
Research Areas: Control, Dynamical Systems, Electric Systems, Machine Learning, Multi-Physical Modeling, RoboticsBrief- MERL researchers presented 13 papers at the recently concluded American Control Conference (ACC) 2025 in Denver, USA. The papers covered a wide range of topics including Bayesian optimization for personalized medicine, machine learning for battery performance in eVTOLs, model predictive control for space and building systems, process systems engineering for sustainability, GNSS-RTK optimization, convex set manipulation, PDE control, servo system modeling, battery fault diagnosis, truck fleet coordination, interactive motion planning, and satellite station keeping. Additionally, MERL researchers (Vedang Deshpande and Ankush Chakrabarty) organized an invited session on design and optimization of energy systems.
As a sponsor of the conference, MERL maintained a booth for open discussions with researchers and students, and hosted a special session to discuss highlights of MERL research and work philosophy.
- MERL researchers presented 13 papers at the recently concluded American Control Conference (ACC) 2025 in Denver, USA. The papers covered a wide range of topics including Bayesian optimization for personalized medicine, machine learning for battery performance in eVTOLs, model predictive control for space and building systems, process systems engineering for sustainability, GNSS-RTK optimization, convex set manipulation, PDE control, servo system modeling, battery fault diagnosis, truck fleet coordination, interactive motion planning, and satellite station keeping. Additionally, MERL researchers (Vedang Deshpande and Ankush Chakrabarty) organized an invited session on design and optimization of energy systems.