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.

翻译：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实现大规模卫星集群控制的原理。该驱动方式为多卫星长期编队维持提供了一种前景广阔的解决方案，并已主要在基于地面测试平台的双卫星位置控制中得到验证。然而，当卫星数量超过三颗时，基本挑战与高度非线性问题交织出现：1）非完整约束，2）欠驱动性，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可解决这些问题，其中平均执行器输出与期望指令保持一致，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）平均系统动力学可控性的增强，并具有理论保证的误差界；（2）去中心化电流管理。我们设计了两轴线圈和一套基于气浮平台的地面实验装置，实现了轨道动力学的数学复现。基于学习得到的相互作用模型的有效性，我们提出了NODA-MMH（基于认证学习模型的磁驱动集群控制神经功率最优磁偶极子分配方法），用于实现基于模型的功率最优集群控制。本研究是对我们关于磁驱动集群长期编队维持问题的教程论文的补充。