# 基于学习的自适应 MPC 用于仅使用磁力矩器的卫星姿态控制

> 原文链接：https://robotics.hellobike.com/papers/2026-10-14-2610-20280
> 频道：papers | 更新日期：2026-10-14 | 标签：arxiv, cs.RO
> 核心摘要：针对磁力矩器卫星姿态控制受控权损失和磁场未知问题，提出含学习磁场模型的自适应MPC，并在地球与天王星轨道仿真验证。

# 基于学习的自适应 MPC 用于仅使用磁力矩器的卫星姿态控制

::: v-pre
> 原文链接：[Learning-Based Adaptive MPC for Magnetorquer-Only Satellite Attitude Control](https://arxiv.org/abs/2610.20280)（arXiv 2610.20280 · 发布日期：2026-10-14）

**Learning-Based Adaptive MPC for Magnetorquer-Only Satellite Attitude Control**

针对磁力矩器卫星姿态控制受控权损失和磁场未知问题，提出含学习磁场模型的自适应MPC，并在地球与天王星轨道仿真验证。

## 核心要点

- 纳卫星尺寸受限，姿态控制与科学能力需权衡；磁力矩器紧凑但依赖完整行星磁场。
- 方法在MPC中规划转动轨迹并考虑控制权损失，加入用低保真磁场数据仿真训练的学习磁场模型。
- 利用在线测量，通过简单的最后一层更新自适应磁场模型，使MPC更快适应真实行星磁场。
- 在地球轨道和天王星轨道用例中，控制器成功率分别提升5%和61%。

**验证范围与局限**：验证限于地球轨道和天王星轨道的系统仿真。

## 英文摘要 (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.

作者：Patrick Schwartz, Abhishek Cauligi
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## 原始参考来源 (Sources)

- [arXiv 2610.20280：Learning-Based Adaptive MPC for Magnetorquer-Only Satellite Attitude Control](https://arxiv.org/abs/2610.20280)