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’s
Artemis lunar base program and the Canadian Space Agency’s Gateway
power systems, demand resilient, autonomous, and intelligent energy control
solutions. These systems operate in dynamic, resource-constrained,
and fault-prone environments where traditional SCADA or PLC-based
controls lack adaptability and predictive capability.
This paper presents HIRACLE—Hybrid Intelligent Resilient Adaptive
Control and Learning Engine—a novel parallel AI framework specifically
designed for microgrid systems in extraterrestrial habitats and highaltitude
UAV missions. HIRACLE features a modular, edge-deployable
architecture combining transformer-based forecasting, deep reinforcement
learning, spiking neural fault detection, and graph-based rerouting, all
supported by meta-learning for continuous mission adaptation.
The software implementation utilizes containerized deep learning models
(TensorFlow/PyTorch) optimized for edge inference using platforms
such as NVIDIA Jetson AGX Orin and Xilinx Versal AI Edge SoCs.
These models are deployed as distributed agents capable of parallel operation
via high-speed buses (CAN-FD, SpaceWire), ensuring real-time
coordination across subsystems. Fault classification, ripple anticipation,