Optimizing Solid Rocket Missile Trajectories: A Hybrid Approach Using an Evolutionary Algorithm and Machine Learning

Optimizing Solid Rocket Missile Trajectories: A Hybrid Approach Using an Evolutionary Algorithm and Machine Learning

This paper introduces a novel approach for modeling and optimizing the trajectory and behavior of small solid rocket missiles. The proposed framework integrates a six-degree-of-freedom (6DoF) simulation environment experimentally tuned for accuracy, with a combination of genetic algorithms (GAs) and...

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Bibliographic Details
Main Authors: Carlo Ferro, Matteo Cafaro, Paolo Maggiore
Format: Article
Language:English
Published: MDPI AG 2024-11-01
Series:Aerospace
Subjects:
fluidic thrust vectoring
trajectory optimization
evolutionary algorithm (EA)
genetic algorithm (GA)
machine learning applications
missile guidance systems
Online Access:https://www.mdpi.com/2226-4310/11/11/912
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