Hybrid Automata-Based Control Framework for Real-Time Optimization in Space-Based Solar Power Transmission

The Space-Based Solar Power (SBSP) system is a new solution for energy production via solar collection on orbiting platforms and its transmission to Earth. SBSP systems suffer from some serious challenges, such as beam angle error deviations, power transmission efficiency reduction, atmospheric disturbance, and space debris impact. While usual machine learning algorithms may predict the production of energy, they cannot respond sufficiently in real time to alter according to dynamic environmental conditions. This piece suggests a hybrid structure predictive and control framework, mixing DFA and PDA units inside the SBSP structure. The DFA processes inputs such as beam accuracy, atmospheric loss, and collision probability and maps them to operation states, while the PDA buffers threat reports and detected anomalies. The system enhances stability in systems through the employment of machine learning predictions as inputs to automata-based reasoner-driven decision rules, ensuring real-time response. The system improves SBSP reliability, minimizes power loss, and forms a foundation for the development of autonomous SBSP infrastructure, which can be utilized to respond to different environmental challenges while providing energy transmission efficiency optimization.