Application of Data-Driven Deep Learning Method in Thrust Prediction of Solid Rocket Motor

As the key power system of space launch vehicle, the thrust prediction of solid rocket motor (SRM) is very important for improving the design efficiency and accuracy. The traditional thrust prediction method based on physical model is limited by the complexity of the model and the consumption of com...

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Bibliographic Details
Published in:2025 International Conference on Electrical Drives, Power Electronics & Engineering (EDPEE) pp. 502 - 506
Main Authors: Wang, Pang, Liu, Peijin, Ao, Wen
Format: Conference Proceeding
Language:English
Published: IEEE 26.03.2025
Subjects:
Accuracy
Adaptation models
Convolutional neural networks
Data models
data-driven
Deep learning
Feature extraction
Long short term memory
long short term memory networks
Predictive models
Reluctance motors
solid rocket motor
Solids
thrust prediction
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Summary:As the key power system of space launch vehicle, the thrust prediction of solid rocket motor (SRM) is very important for improving the design efficiency and accuracy. The traditional thrust prediction method based on physical model is limited by the complexity of the model and the consumption of computing resources, so it is difficult to achieve high-precision prediction with small sample data. This article proposes a hybrid deep neural network (DNN) model that combines convolutional neural networks (CNN) and long short-term memory networks (LSTM), aiming to fully utilize the advantages of CNN in feature extraction and the ability of LSTM in processing time series data. Through data enhancement technology, including adaptive Gaussian noise and random drift method, the training data set is expanded to improve the robustness and generalization ability of the model. The experimental results show that the model has high prediction accuracy, the root mean square error (RMSE) is about 0.36, and the average absolute error (MAE) is 0.32, which shows a good prediction effect. Data-driven deep learning method provides a new idea and method for SRM thrust prediction, showing the advantages of automatic feature extraction, data set expansion to improve generalization ability and real-time prediction.
DOI:10.1109/EDPEE65754.2025.00092