MODELAGEM E ALGORITMOS DE CONTROLE ROBUSTO NEURO-ADAPTATIVO PARA FOGUETES DE COMBUSTÍVEL SÓLIDO ESTUDO DE CASO: CONTROLADOR BASEADO EM MODELO DE REFERÊNCIA COM RNA-RBF PARA CONTROLE DE ALTITUDE

• Ano de defesa: 2024
• Autor(a) principal: CARVALHO, Christian Danner Ramos de
• Orientador(a): FONSECA NETO, João Viana da
• Banca de defesa: FONSECA NETO, João Viana da , SOUZA, Francisco das Chagas de , BARRADAS FILHO, Alex Oliveira, SILVEIRA, Antonio da Silva
• Tipo de documento: Dissertação
• Tipo de acesso: Acesso aberto
• Idioma: por
• Instituição de defesa: Universidade Federal do Maranhão
• Programa de Pós-Graduação: PROGRAMA DE PÓS-GRADUAÇÃO EM ENGENHARIA AEROESPACIAL/CCET
• Departamento: DEPARTAMENTO DE ENGENHARIA DA ELETRICIDADE/CCET
• País: Brasil
• Palavras-chave em Português: Controle Robusto; Foguete; Controle Adaptativo de Referência de Modelo; Controle Neuro-Adaptativo de Referência de Modelo; Redes Neurais de Funções de Base Radial
• Palavras-chave em Inglês: Robust Control; Rocket; Model Reference Adaptive Control; Model Reference Neuro-Adaptive Control; Radial Basis Functions Neural Networks
• Área do conhecimento CNPq: Projeto de Estruturas Aeroespaciais
• Link de acesso: https://tedebc.ufma.br/jspui/handle/tede/5756

This dissertation presents the development of a methodology for designing robust neuro- adaptive 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 be able 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 MRAC (Model Reference Adaptive Control) techniques and an MRNAC (Model Reference Neuro-Adaptive Control) approach. The analysis, carried out using computer simulations, evaluates the behaviour 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.