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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Vydané v:	Aerospace Ročník 11; číslo 11; s. 912
Hlavní autori:	Ferro, Carlo, Cafaro, Matteo, Maggiore, Paolo
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Basel MDPI AG 01.11.2024
Predmet:	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
On-line prístup:	Získať plný text
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