Modelling and Neuro-Adaptive Robust Control Algorithms for Solid Fuel Rockets
This study presents the development of a methodology for designing neuro-adaptive robust controllers based on a reference model associated with an artificial neural network of radial basis functions (ANN-RBF) for solid fuel suborbital rockets. The modelling and neuro-adaptive robust control algorit...
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| Format: | Article |
| Language: | English |
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Instituto de Aeronáutica e Espaço (IAE)
2025-01-01
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| Series: | Journal of Aerospace Technology and Management |
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| Online Access: | https://jatm.com.br/jatm/article/view/1361 |
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| author | Christian Danner Ramos de Carvalho João Viana da Fonseca Neto |
| author_facet | Christian Danner Ramos de Carvalho João Viana da Fonseca Neto |
| author_sort | Christian Danner Ramos de Carvalho |
| collection | DOAJ |
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This study presents the development of a methodology for designing neuro-adaptive robust controllers based on a reference model associated with an artificial neural network of radial basis functions (ANN-RBF) for solid fuel suborbital rockets. The modelling and neuro-adaptive robust control algorithms for these rockets are presented. Initially, the methodology is evaluated for a robust controller based on a reference model with ANN-RBF for altitude control. The main objective of the control is to suppress the effect of non-linear uncertainties inherent in the process. The method involves mathematical and computational modelling, together with the design of adaptive controllers for stability and performance analysis. The controllers considered include model reference adaptive control (MRAC) techniques and a model reference neuro-adaptive control (MRNAC) approach. The analysis, carried out using computer simulations, evaluates the behavior of each controller in relation to system stability and performance. The final objective is to select the most suitable controller for the suborbital rocket, taking into account the system constraints, robust performance requirements, robust stability, and optimal adaptability. This research promotes the development of adaptive controllers for suborbital rockets, with possible applications in scientific research and commercial launches.
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| format | Article |
| id | doaj-art-08af102798f84ae9bc5e0a9ad81bdd34 |
| institution | Kabale University |
| issn | 2175-9146 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Instituto de Aeronáutica e Espaço (IAE) |
| record_format | Article |
| series | Journal of Aerospace Technology and Management |
| spelling | doaj-art-08af102798f84ae9bc5e0a9ad81bdd342025-01-29T02:01:52ZengInstituto de Aeronáutica e Espaço (IAE)Journal of Aerospace Technology and Management2175-91462025-01-0117Modelling and Neuro-Adaptive Robust Control Algorithms for Solid Fuel RocketsChristian Danner Ramos de Carvalho0João Viana da Fonseca Neto1Universidade Federal do Maranhão – Centro de Ciências Exatas e Tecnologia – Programa de Pós-Graduação em Engenharia Aeroespacial – São Luís/MA – Brazil.Universidade Federal do Maranhão – Centro de Ciências Exatas e Tecnologia – Programa de Pós-Graduação em Engenharia Aeroespacial and Programa de Pós-Graduação em Engenharia Elétrica – São Luís/MA – Brazil. This study presents the development of a methodology for designing neuro-adaptive robust controllers based on a reference model associated with an artificial neural network of radial basis functions (ANN-RBF) for solid fuel suborbital rockets. The modelling and neuro-adaptive robust control algorithms for these rockets are presented. Initially, the methodology is evaluated for a robust controller based on a reference model with ANN-RBF for altitude control. The main objective of the control is to suppress the effect of non-linear uncertainties inherent in the process. The method involves mathematical and computational modelling, together with the design of adaptive controllers for stability and performance analysis. The controllers considered include model reference adaptive control (MRAC) techniques and a model reference neuro-adaptive control (MRNAC) approach. The analysis, carried out using computer simulations, evaluates the behavior of each controller in relation to system stability and performance. The final objective is to select the most suitable controller for the suborbital rocket, taking into account the system constraints, robust performance requirements, robust stability, and optimal adaptability. This research promotes the development of adaptive controllers for suborbital rockets, with possible applications in scientific research and commercial launches. https://jatm.com.br/jatm/article/view/1361Robust control RocketModel reference adaptive controlModel reference neuro-adaptive controlArtificial neural networksRadial basis function neural networks |
| spellingShingle | Christian Danner Ramos de Carvalho João Viana da Fonseca Neto Modelling and Neuro-Adaptive Robust Control Algorithms for Solid Fuel Rockets Journal of Aerospace Technology and Management Robust control Rocket Model reference adaptive control Model reference neuro-adaptive control Artificial neural networks Radial basis function neural networks |
| title | Modelling and Neuro-Adaptive Robust Control Algorithms for Solid Fuel Rockets |
| title_full | Modelling and Neuro-Adaptive Robust Control Algorithms for Solid Fuel Rockets |
| title_fullStr | Modelling and Neuro-Adaptive Robust Control Algorithms for Solid Fuel Rockets |
| title_full_unstemmed | Modelling and Neuro-Adaptive Robust Control Algorithms for Solid Fuel Rockets |
| title_short | Modelling and Neuro-Adaptive Robust Control Algorithms for Solid Fuel Rockets |
| title_sort | modelling and neuro adaptive robust control algorithms for solid fuel rockets |
| topic | Robust control Rocket Model reference adaptive control Model reference neuro-adaptive control Artificial neural networks Radial basis function neural networks |
| url | https://jatm.com.br/jatm/article/view/1361 |
| work_keys_str_mv | AT christiandannerramosdecarvalho modellingandneuroadaptiverobustcontrolalgorithmsforsolidfuelrockets AT joaovianadafonsecaneto modellingandneuroadaptiverobustcontrolalgorithmsforsolidfuelrockets |