HIRACLE: A Parallel AI Framework for Autonomous Microgrid Control in Aerospace Systems Application Potential for NASA and the Canadian Space Agency for Deep Neural Control Module (DNCM)

Recent advancements in space exploration platforms, such as NASA’sArtemis lunar base program and the Canadian Space Agency’s Gatewaypower systems, demand resilient, autonomous, and intelligent energy controlsolutions. These systems operate in dynamic, resource-constrained,and fault-prone environments where traditional SCADA or PLC-basedcontrols lack adaptability and predictive capability.This paper presents HIRACLE—Hybrid Intelligent Resilient AdaptiveControl and Learning Engine—a novel parallel AI framework specificallydesigned for microgrid systems in extraterrestrial habitats and highaltitudeUAV missions. HIRACLE features a modular, edge-deployablearchitecture combining transformer-based forecasting, deep reinforcementlearning, spiking neural fault detection, and graph-based rerouting, allsupported by meta-learning for continuous mission adaptation.The software implementation utilizes containerized deep learning models(TensorFlow/PyTorch) optimized for edge inference using platformssuch as NVIDIA Jetson AGX Orin and Xilinx Versal AI Edge SoCs.These models are deployed as distributed agents capable of parallel operationvia high-speed buses (CAN-FD, SpaceWire), ensuring real-timecoordination across subsystems. Fault classification, ripple anticipation,