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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| Format: | Article |
| Language: | English |
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MDPI AG
2024-11-01
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| Series: | Aerospace |
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| Online Access: | https://www.mdpi.com/2226-4310/11/11/912 |
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| author | Carlo Ferro Matteo Cafaro Paolo Maggiore |
| author_facet | Carlo Ferro Matteo Cafaro Paolo Maggiore |
| author_sort | Carlo Ferro |
| collection | DOAJ |
| description | 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 machine learning (ML) to enhance the performance of the missile path. In the initial phase, a GA is employed to optimize the missile’s trajectory for efficient target acquisition, defining key launch parameters such as the ramp angle and lateral maneuver force to minimize positional errors and to ensure effective target engagement. Following trajectory optimization, the derived data are used to train an ML model that predicts setup parameters, significantly reducing computational costs and time. This close integration enables real-time adjustments for acquiring moving targets, thereby improving accuracy and minimizing maneuvering costs. This study also explores the application of fluidic thrust vectoring for small rockets, providing an innovative solution to enhance maneuverability and control, especially at low speeds. The proposed framework was validated using experimental launch data from the Icarus Team. The methodology offers a robust and cost-effective solution for precision targeting and improved maneuverability in aerospace and defense contexts. |
| format | Article |
| id | doaj-art-12527c6a173842d9b0a5e147b5a410bb |
| institution | Kabale University |
| issn | 2226-4310 |
| language | English |
| publishDate | 2024-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Aerospace |
| spelling | doaj-art-12527c6a173842d9b0a5e147b5a410bb2024-11-26T17:42:54ZengMDPI AGAerospace2226-43102024-11-01111191210.3390/aerospace11110912Optimizing Solid Rocket Missile Trajectories: A Hybrid Approach Using an Evolutionary Algorithm and Machine LearningCarlo Ferro0Matteo Cafaro1Paolo Maggiore2Department of Mechanical and Aerospace Engineering, Politecnico di Torino, 10129 Turin, ItalyDepartment of Mechanical and Aerospace Engineering, Politecnico di Torino, 10129 Turin, ItalyDepartment of Mechanical and Aerospace Engineering, Politecnico di Torino, 10129 Turin, ItalyThis 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 machine learning (ML) to enhance the performance of the missile path. In the initial phase, a GA is employed to optimize the missile’s trajectory for efficient target acquisition, defining key launch parameters such as the ramp angle and lateral maneuver force to minimize positional errors and to ensure effective target engagement. Following trajectory optimization, the derived data are used to train an ML model that predicts setup parameters, significantly reducing computational costs and time. This close integration enables real-time adjustments for acquiring moving targets, thereby improving accuracy and minimizing maneuvering costs. This study also explores the application of fluidic thrust vectoring for small rockets, providing an innovative solution to enhance maneuverability and control, especially at low speeds. The proposed framework was validated using experimental launch data from the Icarus Team. The methodology offers a robust and cost-effective solution for precision targeting and improved maneuverability in aerospace and defense contexts.https://www.mdpi.com/2226-4310/11/11/912fluidic thrust vectoringtrajectory optimizationevolutionary algorithm (EA)genetic algorithm (GA)machine learning applicationsmissile guidance systems |
| spellingShingle | Carlo Ferro Matteo Cafaro Paolo Maggiore Optimizing Solid Rocket Missile Trajectories: A Hybrid Approach Using an Evolutionary Algorithm and Machine Learning Aerospace fluidic thrust vectoring trajectory optimization evolutionary algorithm (EA) genetic algorithm (GA) machine learning applications missile guidance systems |
| title | Optimizing Solid Rocket Missile Trajectories: A Hybrid Approach Using an Evolutionary Algorithm and Machine Learning |
| title_full | Optimizing Solid Rocket Missile Trajectories: A Hybrid Approach Using an Evolutionary Algorithm and Machine Learning |
| title_fullStr | Optimizing Solid Rocket Missile Trajectories: A Hybrid Approach Using an Evolutionary Algorithm and Machine Learning |
| title_full_unstemmed | Optimizing Solid Rocket Missile Trajectories: A Hybrid Approach Using an Evolutionary Algorithm and Machine Learning |
| title_short | Optimizing Solid Rocket Missile Trajectories: A Hybrid Approach Using an Evolutionary Algorithm and Machine Learning |
| title_sort | optimizing solid rocket missile trajectories a hybrid approach using an evolutionary algorithm and machine learning |
| topic | fluidic thrust vectoring trajectory optimization evolutionary algorithm (EA) genetic algorithm (GA) machine learning applications missile guidance systems |
| url | https://www.mdpi.com/2226-4310/11/11/912 |
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