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<dc:title xml:lang="en">Stochastic optimization by evolutionary methods applied to autonomous aircraft flight control</dc:title>
<dcterms:alternative xml:lang="fr">Optimisation stochastique par évolution artificielle appliquée à la conduite autonome d’engins aériens</dcterms:alternative>
<dc:subject xml:lang="fr">Automatique</dc:subject>
<dc:subject xml:lang="fr">Contrôle</dc:subject>
<dc:subject xml:lang="fr">Commande</dc:subject>
<dc:subject xml:lang="fr">Pilotage</dc:subject>
<dc:subject xml:lang="fr">Planification de trajectoire</dc:subject>
<dc:subject xml:lang="fr">Identification</dc:subject>
<dc:subject xml:lang="fr">Navigation</dc:subject>
<dc:subject xml:lang="fr">Intelligence artificielle</dc:subject>
<dc:subject xml:lang="fr">Évolution artificielle</dc:subject>
<dc:subject xml:lang="fr">Programmation génétique</dc:subject>
<dc:subject xml:lang="fr">Algorithmes évolutionnaires</dc:subject>
<dc:subject xml:lang="en">Automatics</dc:subject>
<dc:subject xml:lang="en">Control</dc:subject>
<dc:subject xml:lang="en">Path planning</dc:subject>
<dc:subject xml:lang="en">Identification</dc:subject>
<dc:subject xml:lang="en">Navigation</dc:subject>
<dc:subject xml:lang="en">Artificial intelligence</dc:subject>
<dc:subject xml:lang="en">Artificial evolution</dc:subject>
<dc:subject xml:lang="en">Evolutionary algorithms</dc:subject>
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<tef:elementdEntree autoriteExterne="16767465X" autoriteSource="Sudoc">Algorithmes évolutionnaires</tef:elementdEntree>
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<dcterms:abstract xml:lang="fr">Le but de ce doctorat est de déterminer dans quelle mesure les algorithmes issus de l’intelligence artificielle, principalement les Algorithmes Evolutionnaires et la Programmation Génétique, pourraient aider les algorithmes de l’automatique classique afin de permettre aux engins autonomes de disposer de capacités bien supérieures, et ce dans les domaines de l’identification, de la planification de trajectoire, du pilotage et de la navigation.De nouveaux algorithmes ont été développés, dans les domaines de l’identification, de la planification de trajectoire, de la navigation et du contrôle, et ont été testés sur des systèmes de simulation et des aéronefs du monde réel (Oktokopter du ST2I, Bebop.Drone de la société Parrot, Twin Otter et F-16 de la NASA) de manière à évaluer les apports de ces nouvelles approches par rapport à l’état de l’art.La plupart de ces nouvelles approches ont permis d’obtenir de très bons résultats comparés à l’état de l’art, notamment dans le domaine de l’identification et de la commande, et un approfondissement des travaux devraient être engagé afin de développer le potentiel applicatifs de certains algorithmes.</dcterms:abstract>
<dcterms:abstract xml:lang="en">The object of this PhD has consisted in elaborating evolutionary computing algorithms to find interesting solutions to important problems in several domains of automation science, applied to aircrafts mission conduction and to understand what could be the advantages of using such approaches, compared to the state-of-the-art, in terms of efficiency, robustness, and effort of implementation.New algorithms have been developed, in Identification, Path planning, Navigation and Control and have been tested on simulation and on real world platforms (AR.Drone 3.0 UAV (Parrot), Oktokopter UAV, Twin Otter and military fighter F-16 (NASA LaRC)), to assess the performances improvements, given by the new proposed approaches.Most of these new approaches provide very interesting results; and research work (on control by evolutionary algorithms, identification by genetic programming and relative navigation) should be engaged to plan potential applications in different real world technologies.</dcterms:abstract>
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