arXiv 2510.202312025-10-23cs.RO

NODA-MMH：面向在轨验证的磁驱动集群实验的认证学习辅助非线性控制

NODA-MMH: Certified Learning-Aided Nonlinear Control for Magnetically-Actuated Swarm Experiment Toward On-Orbit Proof

Graduate Student of Mechanical Engineering, Institute of Science Tokyo（东京科学研究所机械工程研究生）；Researcher and Engineer of Satellite R&D Division, Interstellar Technologies Inc.（Interstellar Technologies Inc.卫星研发部门研究员和工程师）；Senior Associate Professor of Computer Science and Engineering, University of Aizu（宇智大学计算机科学与工程高级副教授）；Professor of Spacecraft Engineering, Institute of Space and Astronautical Science（空间与航天科学研究所航天工程教授）

Yuta Takahashi, Atsuki Ochi, Yoichi Tomioka, Shin-Ichiro Sakai

备注：: Accepted for presentation at the 2025 International Conference on Space Robotics (iSpaRo 2025)

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AI总结：

本研究通过地面实验验证了学习辅助磁驱动集群控制的关键方面，并提出NODA-MMH方法实现功率最优的集群控制，为在轨验证奠定基础。

摘要：

本研究通过实验验证了利用卫星搭载磁力矩器产生的学习辅助磁场相互作用进行大规模卫星集群控制的原理。这种驱动方式为多卫星长期编队保持提供了一种有前景的解决方案，并主要在基于地面的双卫星位置控制测试平台上得到了验证。然而，当卫星数量超过三颗时，伴随高非线性会出现根本性挑战：1）非完整约束，2）欠驱动，3）可扩展性，以及4）计算成本。先前研究表明，时间积分电流控制理论上解决了这些问题，其中平均执行器输出与期望指令一致，而基于学习的技术进一步提升了其性能。通过多次实验，我们验证了学习辅助时间积分电流控制的关键方面：（1）平均系统动力学的可控性增强，具有理论保证的误差界，以及（2）分散式电流管理。我们设计了两轴线圈和利用气浮平台的基于地面的实验装置，能够数学上复现轨道动力学。基于学习到的相互作用模型的有效性，我们引入了NODA-MMH（用于认证学习模型磁驱动集群控制的神经功率最优偶极子分配）以实现基于模型的功率最优集群控制。本研究补充了我们关于磁驱动集群长期编队保持问题的教程论文。

英文摘要：

This study experimentally validates the principle of large-scale satellite swarm control through learning-aided magnetic field interactions generated by satellite-mounted magnetorquers. This actuation presents a promising solution for the long-term formation maintenance of multiple satellites and has primarily been demonstrated in ground-based testbeds for two-satellite position control. However, as the number of satellites increases beyond three, fundamental challenges coupled with the high nonlinearity arise: 1) nonholonomic constraints, 2) underactuation, 3) scalability, and 4) computational cost. Previous studies have shown that time-integrated current control theoretically solves these problems, where the average actuator outputs align with the desired command, and a learning-based technique further enhances their performance. Through multiple experiments, we validate critical aspects of learning-aided time-integrated current control: (1) enhanced controllability of the averaged system dynamics, with a theoretically guaranteed error bound, and (2) decentralized current management. We design two-axis coils and a ground-based experimental setup utilizing an air-bearing platform, enabling a mathematical replication of orbital dynamics. Based on the effectiveness of the learned interaction model, we introduce NODA-MMH (Neural power-Optimal Dipole Allocation for certified learned Model-based Magnetically swarm control Harness) for model-based power-optimal swarm control. This study complements our tutorial paper on magnetically actuated swarms for the long-term formation maintenance problem.

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