磁気トルカのみによる衛星姿勢制御のための学習ベース適応MPC
Learning-Based Adaptive MPC for Magnetorquer-Only Satellite Attitude Control
低忠実度磁場データで学習した磁場モデルをオンライン測定で適応させ、磁気トルカのみで衛星の姿勢軌道を計画するMPCを提案。地球軌道と天王星軌道のシミュレーションで制御成功率を改善。
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著者: Patrick Schwartz, Abhishek Cauligi
分類: cs.RO
原文アブストラクト
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.
関連論文
- 過剰駆動ティルトロータヘキサコプタの軌道追従のための適応非線形MPC制御/適応MPC
- 分類器学習による適応型モデル予測制御制御/適応MPC
- 適応型非線形MPCによるクアッドロータの性能・精度・ペイロード対応制御/適応MPC
