Submitted by Automa??o e Estat?stica (sst@bczm.ufrn.br) on 2016-05-03T22:56:48Z No. of bitstreams: 1 LeonardoWaylandTorresSilva_TESE.pdf: 3255016 bytes, checksum: 1c9f68f3968c7c1bdbdc4119bde6f919 (MD5) / Approved for entry into archive by Arlan Eloi Leite Silva (eloihistoriador@yahoo.com.br) on 2016-05-05T20:16:14Z (GMT) No. of bitstreams: 1 LeonardoWaylandTorresSilva_TESE.pdf: 3255016 bytes, checksum: 1c9f68f3968c7c1bdbdc4119bde6f919 (MD5) / Made available in DSpace on 2016-05-05T20:16:14Z (GMT). No. of bitstreams: 1 LeonardoWaylandTorresSilva_TESE.pdf: 3255016 bytes, checksum: 1c9f68f3968c7c1bdbdc4119bde6f919 (MD5) Previous issue date: 2015-06-19 / Os centros de lan?amento e rastreio t?m por finalidade realizar atividades cient?ficas e comerciais com ve?culos aeroespaciais. Os Sistemas de Rastreio de Foguetes (SRF) integram a infraestrutura desses centros, sendo respons?veis pela coleta e processamento dos dados da trajet?ria dos ve?culos. Os sensores dos SRFs normalmente s?o Radares com Refletores Parab?licos (RRPs), mas tamb?m ? poss?vel usar radares com arranjos de antenas, chamados de Arranjos de Varredura (AVs), originando os Radares com Arranjos de Varredura (RAVs). Nos AVs, o sinal de alimenta??o de cada elemento radiante do arranjo pode ser ajustado para fazer o controle eletr?nico do diagrama de radia??o, a fim de aumentar as funcionalidades e reduzir as manuten??es do sistema. Com isso, nos projetos de implanta??o e reutiliza??o de RAVs, a modelagem est? sujeita a v?rias combina??es de sinais de alimenta??o, produzindo um problema de otimiza??o complexo, devido ao grande n?mero de solu??es dispon?veis. Para solucionar tal problema, ? poss?vel usar m?todos de otimiza??o off-line, tais como Algoritmos Gen?ticos (AGs), cujas solu??es calculadas s?o armazenadas para aplica??es on-line. Nesse contexto, o m?todo do Algoritmo Gen?tico com Crossover M?ximo-M?nimo (Genetic Algorithm with Maximum-Minimum Crossover - GAMMC) foi usado para desenvolver o algoritmo GAMMC-P, que otimiza a etapa de modelagem do controle do diagrama de radia??o de AVs planares. Comparado a um AG com recombina??o convencional, o GAMMC tem uma abordagem diferente, pois realiza a recombina??o de indiv?duos mais aptos com indiv?duos menos aptos, para aumentar a diversidade gen?tica da popula??o, evitando a converg?ncia prematura, aumentando o fitness e reduzindo o tempo de processamento. Assim, o GAMMC-P utiliza um algoritmo reconfigur?vel, com m?ltiplos objetivos, codifica??o real diferenciada e o operador gen?tico MMC, tendo atingido com sucesso os requisitos propostos para diferentes condi??es de opera??o de um RAV planar. / Launching centers are designed for scientific and commercial activities with aerospace vehicles. Rockets Tracking Systems (RTS) are part of the infrastructure of these centers and they are responsible for collecting and processing the data trajectory of vehicles. Generally, Parabolic Reflector Radars (PRRs) are used in RTS. However, it is possible to use radars with antenna arrays, or Phased Arrays (PAs), so called Phased Arrays Radars (PARs). Thus, the excitation signal of each radiating element of the array can be adjusted to perform electronic control of the radiation pattern in order to improve functionality and maintenance of the system. Therefore, in the implementation and reuse projects of PARs, modeling is subject to various combinations of excitation signals, producing a complex optimization problem due to the large number of available solutions. In this case, it is possible to use offline optimization methods, such as Genetic Algorithms (GAs), to calculate the problem solutions, which are stored for online applications. Hence, the Genetic Algorithm with Maximum-Minimum Crossover (GAMMC) optimization method was used to develop the GAMMC-P algorithm that optimizes the modeling step of radiation pattern control from planar PAs. Compared with a conventional crossover GA, the GAMMC has a different approach from the conventional one, because it performs the crossover of the fittest individuals with the least fit individuals in order to enhance the genetic diversity. Thus, the GAMMC prevents premature convergence, increases population fitness and reduces the processing time. Therefore, the GAMMC-P uses a reconfigurable algorithm with multiple objectives, different coding and genetic operator MMC. The test results show that GAMMC-P reached the proposed requirements for different operating conditions of a planar RAV.