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)
Bibliographic record
Abstract
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,
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".