Research on Solid Motor Performance Prediction Method Based on Deep Meta Learning

Total impulse characterizes the overall propulsive capability of a solid rocket motor. The cost and resources required for ground static firing tests limit data availability, while temperature-dependent thrust variations complicate modeling with few records. This study develops an MT model that combines model-agnostic meta-learning with a Transformer. Complete thrust records are divided into three segments using the thrust peak and an amplitude threshold, then downsampled. The model receives the compressed sequences and their actual timestamps, extracts temporal relationships through self-attention, and learns a shared initialization from high- and low-temperature tasks. This initialization supports total impulse prediction from a small set of ambient-temperature records. Model comparisons, sampling analyses, and cross-temperature validation assess the use of compressed thrust information and historical operating-condition data. With three-segment, 500-point inputs, MT achieves a mean absolute percentage error (MAPE) of 0.429511% on the ambient-temperature query set. Record-level error analysis and cross-temperature validation show that information learned from high- and low-temperature tasks improves ambient-temperature total impulse estimation with limited target records. Combining sequence representation with task adaptation improves the use of existing temperature-specific test data for few-shot total impulse prediction and motor performance analysis from compressed records.