Can a neural network optimize satellite magnetorquer power?
Neural Power-Optimal Magnetorquer Solution for Multi-Agent Formation and Attitude Control
This paper proposes a neural network-based approach to optimize power consumption in multi-agent satellite formation and attitude control using magnetorquers. It demonstrates that finding power-optimal solutions, typically a complex non-convex optimization problem, can be efficiently approximated using a Deep Neural Network (DNN). This allows for real-time calculation of control signals, crucial for precise control in space. The key takeaway for LLM-based multi-agent systems is the demonstration of how DNNs can simplify and accelerate the complex optimization required for coordinating actions among multiple agents, particularly when dealing with constraints and resource limitations, analogous to the power constraints of the satellites. The use of decentralized control strategies and relative coordinate frames further enhances scalability and efficiency, offering potential benefits for complex multi-agent web applications.