🤖 AI Summary
To address four key challenges—nonholonomic constraints, underactuation, poor scalability, and high computational complexity—in large-scale magnetically actuated satellite formations, this paper proposes NODA-MMH: a decentralized, power-optimal magnetic current allocation framework integrating time-averaged dynamical modeling, neural-network-based learning modeling, and model predictive control. For the first time, the controllability enhancement of multi-satellite magnetic control systems is experimentally validated on a ground-based air-bearing platform equipped with a dual-axis coil system, with theoretical error bounds rigorously established. Results demonstrate that the proposed learning-enhanced time-averaged current control significantly improves both controllability and energy efficiency. This work establishes a novel paradigm and delivers critical enabling technologies for long-duration autonomous formation flying of on-orbit satellite swarms.
📝 Abstract
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.