Cette thèse porte sur le développement d'une loi de guidage autonome par platitude pour les véhicules de rentrée atmosphérique. La problématique associée au développement d'une loi de guidage autonome porte sur l'organisation globale, l'intégration et la gestion de l'information pertinente jusqu'à la maîtrise du système spatial durant la phase de rentrée. La loi de guidage autonome proposée dans ce mémoire s'appuie sur le concept de platitude, afin d'effectuer un traitement des informations à bord, dans le but double d'attribuer un niveau de responsabilité et d'autonomie au véhicule, déchargeant ainsi le segment sol de tâches opérationnelles "bas niveau", pour lui permettre de mieux assumer son rôle de coordination globale. La première partie de ce mémoire traite de la caractérisation formelle de sorties plates pour les systèmes non linéaires régis par des équations différentielles ordinaires, ainsi que pour les systèmes linéaires à retards. Des algorithmes constructifs sont proposés afin de calculer des sorties plates candidates sous un environnement de calcul formel standard. Dans la seconde partie, une méthodologie complète et générique de replanification de trajectoires de rentrée atmosphérique est proposée, afin de doter la loi de guidage d'un certain niveau de tolérance à des pannes actionneur simple/multiples pouvant survenir lors des phases critiques d'une mission de rentrée atmosphérique. En outre, une méthodologie d'annexation superellipsoidale est proposée afin de convexifier le problème de commande optimale décrit dans l'espace des sorties plates. La loi de guidage proposée est ensuite appliquée étape par étape à une mission de rentrée atmosphérique pour la navette spatiale américaine STS-1. / This thesis deals with the design of an autonomous guidance law based on flatness approach for atmospheric reentry vehicles. The problematic involved by the design of an autonomous guidance law relates to the global organization, the integration and the management of relevant data up to the mastering of the spacecraft during the re-entry mission. The autonomous guidance law proposed in this dissertation is based on flatness concept, in order to perform onboard processing so as to locally assign autonomy and responsibility to the vehicle, thus exempting the ground segment from "low level" operational tasks, so that it can ensure more efficiently its mission of global coordination. The first part of the manuscript deals with the formal characterization of flat outputs for nonlinear systems governed by ordinary differential equations, as well as for linear time-delay systems. Constructive algorithms are proposed in order to compute candidate flat outputs within a standard formal computing environment. In the second part of the manuscript, a global and generic reentry trajectory replanning methodology is proposed in order to provide a fault-tolerance capability to the guidance law, when facing single/multiple control surface failures that could occur during the critical phases of an atmospheric reentry mission. In addition, a superellipsoidal annexion method is proposed so as to convexify the optimal control problem described in the flat outputs space. The proposed guidance law is then applied step by step to an atmospheric reentry mission for the US Space Shuttle orbiter STS-1.

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No. of bitstreams: 1 DissSATM.pdf: 3079104 bytes, checksum: 51b46ffeb4387370e30fb92e31771606 (MD5) Previous issue date: 2015-10-16 / Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES) / Latterly, the development of data mining techniques has emerged in many applications’ fields with aim at analyzing large volumes of data which may be simple and / or complex. The logistics of transport, the railway setor in particular, is a sector with such a characteristic in that the data available in are of varied natures (classic variables such as top speed or type of train, symbolic variables such as the set of routes traveled by train, degree of tack, etc.). As part of this dissertation, one addresses the problem of classification and prediction of heterogeneous data; it is proposed to study through two main approaches. First, an automatic classification approach was implemented based on classification tree technique, which also allows new data to be efficiently integrated into partitions initialized beforehand. The second contribution of this work concerns the analysis of sequence data. It has been proposed to combine the above classification method with Markov models for obtaining a time series (temporal sequences) partition in homogeneous and significant groups based on probabilities. The resulting model offers good interpretation of classes built and allows us to estimate the evolution of the sequences of a particular vehicle. Both approaches were then applied onto real data from the a Brazilian railway information system company in the spirit of supporting the strategic management of planning and coherent prediction. This work is to initially provide a thinner type of planning to solve the problems associated with the existing classification in homogeneous circulations groups. Second, it sought to define a typology of train paths (sucession traffic of the same train) in order to provide or predict the next movement of statistical characteristics of a train carrying the same route. The general methodology provides a supportive environment for decision-making to monitor and control the planning organization. Thereby, a formula with two variants was proposed to calculate the adhesion degree between the track effectively carried out or being carried out with the planned one. / Nos últimos anos aflorou o desenvolvimento de técnicas de mineração de dados em muitos domínios de aplicação com finalidade de analisar grandes volumes de dados, os quais podendo ser simples e/ou complexos. A logística de transporte, o setor ferroviário em particular, é uma área com tal característica em que os dados disponíveis são muitos e de variadas naturezas (variáveis clássicas como velocidade máxima ou tipo de trem, variáveis simbólicas como o conjunto de vias percorridas pelo trem, etc). Como parte desta dissertação, aborda-se o problema de classificação e previsão de dados heterogêneos, propõe-se estudar através de duas abordagens principais. Primeiramente, foi utilizada uma abordagem de classificação automática com base na técnica por ´arvore de classificação, a qual também permite que novos dados sejam eficientemente integradas nas partições inicial. A segunda contribuição deste trabalho diz respeito à análise de dados sequenciais. Propôs-se a combinar o método de classificação anterior com modelos de Markov para obter uma participação de sequências temporais em grupos homogêneos e significativos com base nas probabilidades. O modelo resultante oferece uma boa interpretação das classes construídas e permite estimar a evolução das sequências de um determinado veículo. Ambas as abordagens foram então aplicadas nos dados do sistema de informação ferroviário, no espírito de dar apoio à gestão estratégica de planejamentos e previsões aderentes. Este trabalho consiste em fornecer inicialmente uma tipologia mais fina de planejamento para resolver os problemas associados com a classificação existente em grupos de circulações homogêneos. Em segundo lugar, buscou-se definir uma tipologia de trajetórias de trens (sucessão de circulações de um mesmo trem) para assim fornecer ou prever características estatísticas da próxima circulação mais provável de um trem realizando o mesmo percurso. A metodologia geral proporciona um ambiente de apoio à decisão para o monitoramento e controle da organização de planejamento. Deste fato, uma fórmula com duas variantes foi proposta para calcular o grau de aderência entre a trajetória efetivamente realizada ou em curso de realização com o planejado.