Publication Date
5-2026
School
School of Engineering and Computational Sciences
Major
Engineering: Computer
Keywords
machine learning, apogee prediction, rocketry
Disciplines
Navigation, Guidance, Control and Dynamics | Other Computer Engineering
Recommended Citation
Drawdy, Price Hamilton, "A Machine Learning-Based Apogee Prediction Methodology for Experimental Student Rockets" (2026). Senior Honors Theses. 1581.
https://digitalcommons.liberty.edu/honors/1581
Abstract
The ability to predict the maximum altitude of a rocket (apogee) in real-time is incredibly useful for collegiate-level competition rockets. This project creates a machine learning-based real-time apogee prediction methodology. Three model types were tested: linear regression, random forest, and a 3-layer multi-layer perceptron (MLP) neural network. These models were trained on a large dataset of simulated flights. All models performed well on simulated test flights, with the linear regression model showing most promise for use on edge compute. More development and real-world testing are necessary to determine how applicable this method is for real-time operation. Nevertheless, this methodology provides a highly promising alternative to more complex physics-based apogee estimation alternatives.
