aiqbench analysis
This paper uses a neural-network magnetic field model, pretrained on low-fidelity simulated data and then adapted online through a last-layer update inside an MPC loop. The learning component is central to the contribution, not incidental. Its results are simulation-only, and the reported gains (5% for Earth orbit, 61% for Uranus orbit) depend on the simulation setup and on the controller's success metric, so they should be read as preliminary. For ML readers, the useful takeaway is a concrete example of cheap online adaptation in a safety-relevant control loop with limited actuator authority.
Learning-Based Adaptive MPC for Magnetorquer-Only Satellite Attitude Control
arXiv:2610.20280v1 Announce Type: new Abstract: Nanosatellites (e.g., CubeSats) have the potential to carry out useful science and exploration activities for outer solar system exploration, but their small size leads to tradeoffs between attitude control and science capabilities. Magnetorquers offer a unique compromise as compact actuators that enable three-axis attitude control, but suffer from a loss of control authority and require full knowledge of the planetary magnetic field. In this work, we present a learning-based adaptive model predictive control (MPC) algorithm that plans rotational trajectories for the satellite while accounting for this loss of control authority. Key to our approach is the inclusion of a learned magnetic field model that is trained in simulation using low-fidelity magnetic field data. Using measurements collected online, our proposed algorithm adapts this learned magnetic field model using a simple last-layer update scheme, allowing the MPC controller to quickly adapt to the true planetary magnetic field model and more effectively plan attitude trajectories. We demonstrate the results of our proposed approach for two use cases, (1) Earth-orbit and (2) Uranus-orbit, and show that our learning-based adaptive MPC algorithm improves controller success for these system simulations by 5% and 61%, respectively.
Abstract:Nanosatellites (e.g., CubeSats) have the potential to carry out useful science and exploration activities for outer solar system exploration, but their small size leads to tradeoffs between attitude control and science capabilities. Magnetorquers offer a unique compromise as compact actuators that enable three-axis attitude control, but suffer from a loss of control authority and require full knowledge of the planetary magnetic field. In this work, we present a learning-based adaptive model predictive control (MPC) algorithm that plans rotational trajectories for the satellite while accounting for this loss of control authority. Key to our approach is the inclusion of a learned magnetic field model that is trained in simulation using low-fidelity magnetic field data. Using measurements collected online, our proposed algorithm adapts this learned magnetic field model using a simple last-layer update scheme, allowing the MPC controller to quickly adapt to the true planetary magnetic field model and more effectively plan attitude trajectories. We demonstrate the results of our proposed approach for two use cases, (1) Earth-orbit and (2) Uranus-orbit, and show that our learning-based adaptive MPC algorithm improves controller success for these system simulations by 5% and 61%, respectively.
Robotics (cs.RO)
arXiv:2610.20280 [cs.RO]
(or arXiv:2610.20280v1 [cs.RO] for this version)
https://doi.org/10.48550/arXiv.2610.20280 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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