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
Published in:	Aerospace Vol. 11; no. 11; p. 912
Main Authors:	Ferro, Carlo, Cafaro, Matteo, Maggiore, Paolo
Format:	Journal Article
Language:	English
Published:	Basel MDPI AG 01.11.2024
Subjects:	Accuracy Aerospace engineering Algorithms Computing costs Data acquisition Design evolutionary algorithm (EA) Evolutionary algorithms fluidic thrust vectoring genetic algorithm (GA) Genetic algorithms Gravity Guided missiles Machine learning machine learning applications Maneuverability Mathematical optimization missile guidance systems Missile trajectories Missiles Moving targets Neural networks Optimization algorithms Ordinary differential equations Parameters Real time Roads & highways Robust control Rockets Rockets (Ordnance) Target acquisition Thrust vector control Trajectory optimization Turbulence models Variables Velocity Italy
ISSN:	2226-4310, 2226-4310
Online Access:	Get full text
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