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’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, load optimization, and health-aware scheduling are executed concurrently without centralized computation. On the hardware front, HIRACLE integrates reconfigurable logic (FPGAs), neuromorphic processors (Intel Loihi 2), SiC-based power conditioning units, and secure telemetry interfaces into a ruggedized control environment. A new chip-level proposal—HIRACLE-IC—is introduced, consolidating all AI, logic, sensing, and secure communication into a single embedded platform ready for deployment in lunar, Martian, or stratospheric UAV energy systems. This approach not only surpasses existing state-of-the-art autonomous energy controls but also positions HIRACLE as a foundational control paradigm for future NASA and CSA missions requiring scalable, intelligent, and mission-adaptive microgrid autonomy.
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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) | 0.004 | 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".