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Le projet IADGENOL s\u2019inscrit dans les domaines des technologies AWES\u00a0(Airborne Wind Energy Systems\u00a0: syst\u00e8mes d\u2019\u00e9nergie \u00e9olienne a\u00e9roport\u00e9e) et des technologies d\u2019apprentissage profond et d\u2019intelligence artificielle. Ce projet vise \u00e0 \u00e9tudier et \u00e0 appliquer, du point de vue du contr\u00f4le automatique, des technologies d\u2019apprentissage automatique pour le contr\u00f4le des syst\u00e8mes AWES en utilisant des donn\u00e9es provenant \u00e0 la fois d\u2019un environnement de simulation et d\u2019une machine AWES en fonctionnement.<\/p>\n
AWES repr\u00e9sente une approche innovante pour exploiter le vent dans la haute atmosph\u00e8re, mais elle est confront\u00e9e \u00e0 des d\u00e9fis importants, notamment en ce qui concerne l\u2019autonomie op\u00e9rationnelle. Actuellement, parvenir \u00e0 un contr\u00f4le autonome robuste constitue l\u2019un des principaux obstacles. Les syst\u00e8mes doivent fonctionner de mani\u00e8re totalement autonome et r\u00e9sister \u00e0 des conditions m\u00e9t\u00e9orologiques variables sur de longues p\u00e9riodes. Afin d\u2019am\u00e9liorer l\u2019adaptabilit\u00e9 des syst\u00e8mes de contr\u00f4le traditionnels, l\u2019utilisation de l\u2019apprentissage automatique et de l\u2019apprentissage par renforcement a \u00e9t\u00e9 \u00e9tudi\u00e9e.<\/p>\n
L\u2019objectif principal de l\u2019initiative IADGENOL \u00e9tait de d\u00e9velopper un mod\u00e8le de contr\u00f4le bas\u00e9 sur l\u2019apprentissage profond pour le contr\u00f4le automatique de la trajectoire des syst\u00e8mes AWE, et d\u2019utiliser ce type de mod\u00e8les pour comprendre et caract\u00e9riser les d\u00e9fis dynamiques auxquels sont confront\u00e9s ces syst\u00e8mes. Ce projet a \u00e9t\u00e9 enti\u00e8rement r\u00e9alis\u00e9 par CT en utilisant ses propres ressources et la machine de test AWES partag\u00e9e, disponible dans le cadre d\u2019un accord de collaboration avec l\u2019Universit\u00e9 Carlos III de Madrid pour le d\u00e9veloppement des technologies AWES. L\u2019ensemble du travail r\u00e9alis\u00e9 comprend la recherche pr\u00e9liminaire sur l\u2019\u00e9tat de l’art, l\u2019analyse et le traitement des donn\u00e9es pour une utilisation ult\u00e9rieure dans le d\u00e9veloppement de mod\u00e8les, et il s\u2019\u00e9tend m\u00eame jusqu\u2019au d\u00e9veloppement des mod\u00e8les fond\u00e9s sur les donn\u00e9es et du contr\u00f4leur.<\/p>\n
Des tests de validation du syst\u00e8me de contr\u00f4le en conditions r\u00e9elles ont \u00e9t\u00e9 effectu\u00e9s. Pr\u00e9alablement \u00e0 la r\u00e9alisation de ces tests, le contr\u00f4leur a \u00e9t\u00e9 entrain\u00e9 dans un environnement de simulation AWES d\u00e9velopp\u00e9 par CT. Les r\u00e9sultats de ce test, qui simule un \u00ab\u00a0apprentissage naturel\u00a0\u00bb, se sont av\u00e9r\u00e9s tr\u00e8s int\u00e9ressants puisqu\u2019ils ont permis de prouver que le cerf-volant est capable de s\u2019aligner avec le vent et d\u2019effectuer des huit, qui sont les trajectoires normalement utilis\u00e9es dans ce type de syst\u00e8me pour maximiser la production d\u2019\u00e9nergie.<\/p>\n
En revanche, la transition d\u2019un environnement simul\u00e9 \u00e0 une mise en \u0153uvre dans des conditions r\u00e9elles n\u2019a pas \u00e9t\u00e9 une franche r\u00e9ussite. Compte tenu des r\u00e9sultats encourageants obtenus lors des simulations, il semble n\u00e9anmoins que le probl\u00e8me pourrait \u00eatre r\u00e9solu en affinant les m\u00e9thodes de simulation actuelles, et en cr\u00e9ant des environnements contr\u00f4l\u00e9s en laboratoire permettant d\u2019entrainer ces algorithmes avec un niveau de risque minimal.<\/p>\n
Principaux r\u00e9sultats:<\/strong><\/p>\n Au cours de ce projet, trois axes de recherche parall\u00e8les ont \u00e9t\u00e9 d\u00e9velopp\u00e9s\u00a0: un contr\u00f4leur bas\u00e9 sur l\u2019apprentissage profond (Deep learning), un syst\u00e8me de caract\u00e9risation des lois physiques pour un syst\u00e8me AWES et un syst\u00e8me de pr\u00e9vision du vent.<\/p>\n Pour le contr\u00f4leur bas\u00e9 sur le\u00a0deep learning<\/strong><\/p>\n Un contr\u00f4leur a \u00e9t\u00e9 d\u00e9velopp\u00e9 en utilisant des algorithmes d\u2019apprentissage par renforcement profond (DRL), parmi lesquels celui bas\u00e9 sur TD3 a finalement \u00e9t\u00e9 choisi (Twin-Delayed Deep Deterministic Policy Gradient\u00a0: gradient de politique d\u00e9terministe profond \u00e0 double retard).<\/p>\n Le contr\u00f4leur a d\u00e9montr\u00e9 des performances stables lors du pilotage du cerf-volant effectuant des trajectoires en huit dans un environnement simul\u00e9 sans rafales de vent, bien que son efficacit\u00e9 ait \u00e9t\u00e9 limit\u00e9e en pr\u00e9sence de vents variables en raison des restrictions du simulateur. Par la suite, le contr\u00f4leur a \u00e9t\u00e9 int\u00e9gr\u00e9 au syst\u00e8me de contr\u00f4le de la machine AWES et test\u00e9 dans des conditions r\u00e9elles. Bien qu\u2019un contr\u00f4le efficace du cerf-volant n\u2019ait pas \u00e9t\u00e9 obtenu du fait d\u2019un entrainement insuffisant dans un environnement r\u00e9el et des difficult\u00e9s associ\u00e9es, l\u2019int\u00e9gration correcte du contr\u00f4leur avec la machine a pu \u00eatre valid\u00e9e.<\/p>\n Syst\u00e8me de caract\u00e9risation des lois physiques<\/strong><\/p>\n Des mod\u00e8les dynamiques bas\u00e9s sur des r\u00e9seaux neuronaux ont \u00e9t\u00e9 d\u00e9velopp\u00e9s pour pr\u00e9dire la dynamique de vol du syst\u00e8me. Ces mod\u00e8les se sont av\u00e9r\u00e9s efficaces pour pr\u00e9dire la dynamique sur des d\u00e9lais de 0,1\u00a0seconde. Cependant, la propagation des erreurs dans les pr\u00e9dictions a affect\u00e9 les performances du syst\u00e8me par rapport \u00e0 une trajectoire de r\u00e9f\u00e9rence.<\/p>\n Syst\u00e8me de pr\u00e9vision du vent<\/strong><\/p>\n Un syst\u00e8me a \u00e9t\u00e9 d\u00e9velopp\u00e9 pour pr\u00e9dire le vent \u00e0 diff\u00e9rentes altitudes en utilisant des donn\u00e9es m\u00e9t\u00e9orologiques au niveau du sol. Cependant, l\u2019\u00e9valuation de la pr\u00e9cision des pr\u00e9dictions a \u00e9t\u00e9 limit\u00e9e par le manque d\u2019\u00e9quipements de mesure adapt\u00e9s. Le syst\u00e8me a n\u00e9anmoins \u00e9t\u00e9 int\u00e9gr\u00e9 avec succ\u00e8s dans le contr\u00f4leur, ce qui a rendu possible la prise de d\u00e9cision en temps r\u00e9el.<\/p>\n <\/p>\n \u00c0 propos d\u2019IADGENOL\u00a0<\/p>\n Ce projet s\u2019inscrit dans le cadre de l\u2019appel \u00e0 subventions de 2021 concernant les projets de recherche et d\u00e9veloppement en intelligence artificielle et autres technologies num\u00e9riques, ainsi que leur int\u00e9gration dans la cha\u00eene de valeur. Il b\u00e9n\u00e9ficie d\u2019un financement de 510\u00a0000\u00a0\u20ac provenant des fonds Next Generation de l\u2019Union europ\u00e9enne, du Plan de relance, de transformation et de r\u00e9silience, et du Secr\u00e9tariat d\u2019\u00c9tat \u00e0 la num\u00e9risation et \u00e0 l\u2019intelligence artificielle. R\u00e9f\u00e9rence du projet\u00a0: 2021\/C005\/00148256.<\/p>\n [\/et_pb_text][et_pb_button button_url=\u00a0\u00bbhttps:\/\/www.ctengineeringgroup.com\/wp-content\/uploads\/2025\/07\/CT-conclut-le-projet-IADGENOL-sur-lautomatisation-des-trajectoires-des-systemes-AWE-a-laide-du-deep-learning.pdf\u00a0\u00bb button_text=\u00a0\u00bbT\u00e9l\u00e9chargez les informations du projet\u00a0\u00bb button_alignment=\u00a0\u00bbcenter\u00a0\u00bb _builder_version=\u00a0\u00bb4.27.4″ _module_preset=\u00a0\u00bbdefault\u00a0\u00bb custom_button=\u00a0\u00bbon\u00a0\u00bb button_text_color=\u00a0\u00bb#707372″ button_bg_color=\u00a0\u00bb#f7f7f7″ button_border_width=\u00a0\u00bb0px\u00a0\u00bb button_icon=\u00a0\u00bb||divi||400″ button_icon_color=\u00a0\u00bb#707372″ button_icon_placement=\u00a0\u00bbleft\u00a0\u00bb button_on_hover=\u00a0\u00bboff\u00a0\u00bb hover_enabled=\u00a0\u00bb0″ global_colors_info=\u00a0\u00bb{}\u00a0\u00bb theme_builder_area=\u00a0\u00bbpost_content\u00a0\u00bb sticky_enabled=\u00a0\u00bb0″][\/et_pb_button][\/et_pb_column][\/et_pb_row][et_pb_row column_structure=\u00a0\u00bb1_3,1_3,1_3″ _builder_version=\u00a0\u00bb4.26.1″ _module_preset=\u00a0\u00bbdefault\u00a0\u00bb global_colors_info=\u00a0\u00bb{}\u00a0\u00bb theme_builder_area=\u00a0\u00bbpost_content\u00a0\u00bb][et_pb_column type=\u00a0\u00bb1_3″ _builder_version=\u00a0\u00bb4.26.1″ _module_preset=\u00a0\u00bbdefault\u00a0\u00bb global_colors_info=\u00a0\u00bb{}\u00a0\u00bb theme_builder_area=\u00a0\u00bbpost_content\u00a0\u00bb][et_pb_image src=\u00a0\u00bbhttps:\/\/www.ctengineeringgroup.com\/wp-content\/uploads\/2024\/06\/CONBANDERA_REDES_0.jpeg\u00a0\u00bb title_text=\u00a0\u00bbCONBANDERA_REDES_0″ align=\u00a0\u00bbcenter\u00a0\u00bb _builder_version=\u00a0\u00bb4.26.1″ _module_preset=\u00a0\u00bbdefault\u00a0\u00bb global_colors_info=\u00a0\u00bb{}\u00a0\u00bb theme_builder_area=\u00a0\u00bbpost_content\u00a0\u00bb][\/et_pb_image][\/et_pb_column][et_pb_column type=\u00a0\u00bb1_3″ _builder_version=\u00a0\u00bb4.26.1″ _module_preset=\u00a0\u00bbdefault\u00a0\u00bb global_colors_info=\u00a0\u00bb{}\u00a0\u00bb theme_builder_area=\u00a0\u00bbpost_content\u00a0\u00bb][et_pb_image src=\u00a0\u00bbhttps:\/\/www.ctengineeringgroup.com\/wp-content\/uploads\/2024\/06\/redes_footer_0.png\u00a0\u00bb title_text=\u00a0\u00bbredes_footer_0″ align=\u00a0\u00bbcenter\u00a0\u00bb _builder_version=\u00a0\u00bb4.26.1″ _module_preset=\u00a0\u00bbdefault\u00a0\u00bb global_colors_info=\u00a0\u00bb{}\u00a0\u00bb theme_builder_area=\u00a0\u00bbpost_content\u00a0\u00bb][\/et_pb_image][\/et_pb_column][et_pb_column type=\u00a0\u00bb1_3″ _builder_version=\u00a0\u00bb4.26.1″ _module_preset=\u00a0\u00bbdefault\u00a0\u00bb global_colors_info=\u00a0\u00bb{}\u00a0\u00bb theme_builder_area=\u00a0\u00bbpost_content\u00a0\u00bb][et_pb_image src=\u00a0\u00bbhttps:\/\/www.ctengineeringgroup.com\/wp-content\/uploads\/2024\/06\/EN_Funded_by_the_European_Union_RGB_POS.png\u00a0\u00bb title_text=\u00a0\u00bbEN_Funded_by_the_European_Union_RGB_POS\u00a0\u00bb _builder_version=\u00a0\u00bb4.26.1″ _module_preset=\u00a0\u00bbdefault\u00a0\u00bb global_colors_info=\u00a0\u00bb{}\u00a0\u00bb theme_builder_area=\u00a0\u00bbpost_content\u00a0\u00bb][\/et_pb_image][\/et_pb_column][\/et_pb_row][et_pb_row column_structure=\u00a0\u00bb1_3,1_3,1_3″ _builder_version=\u00a0\u00bb4.26.1″ _module_preset=\u00a0\u00bbdefault\u00a0\u00bb global_colors_info=\u00a0\u00bb{}\u00a0\u00bb theme_builder_area=\u00a0\u00bbpost_content\u00a0\u00bb][et_pb_column type=\u00a0\u00bb1_3″ _builder_version=\u00a0\u00bb4.26.1″ _module_preset=\u00a0\u00bbdefault\u00a0\u00bb global_colors_info=\u00a0\u00bb{}\u00a0\u00bb theme_builder_area=\u00a0\u00bbpost_content\u00a0\u00bb][et_pb_image src=\u00a0\u00bbhttps:\/\/www.ctengineeringgroup.com\/wp-content\/uploads\/2024\/06\/Logo-PRTR-tres-lineas_COLOR.png\u00a0\u00bb title_text=\u00a0\u00bbLogo-PRTR-tres-li\u0301neas_COLOR\u00a0\u00bb align=\u00a0\u00bbcenter\u00a0\u00bb _builder_version=\u00a0\u00bb4.26.1″ _module_preset=\u00a0\u00bbdefault\u00a0\u00bb global_colors_info=\u00a0\u00bb{}\u00a0\u00bb theme_builder_area=\u00a0\u00bbpost_content\u00a0\u00bb][\/et_pb_image][\/et_pb_column][et_pb_column type=\u00a0\u00bb1_3″ _builder_version=\u00a0\u00bb4.26.1″ _module_preset=\u00a0\u00bbdefault\u00a0\u00bb global_colors_info=\u00a0\u00bb{}\u00a0\u00bb theme_builder_area=\u00a0\u00bbpost_content\u00a0\u00bb][\/et_pb_column][et_pb_column type=\u00a0\u00bb1_3″ _builder_version=\u00a0\u00bb4.26.1″ _module_preset=\u00a0\u00bbdefault\u00a0\u00bb global_colors_info=\u00a0\u00bb{}\u00a0\u00bb theme_builder_area=\u00a0\u00bbpost_content\u00a0\u00bb][\/et_pb_column][\/et_pb_row][\/et_pb_section]<\/p>\n","protected":false},"excerpt":{"rendered":" After two years of research, CT has successfully developed a Deep Learning-based control model that addresses the dynamic challenges of autonomous AWE system operations. In collaboration with Carlos III University of Madrid, CT showcased the results of the latest validation tests in a simulation environment for the AWES control system at the European AWES Congress, AWEC2024, in Madrid, demonstrating precise wind alignment and optimal energy generation trajectories.<\/p>\n","protected":false},"author":1,"featured_media":255308,"comment_status":"closed","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_et_pb_use_builder":"on","_et_pb_old_content":" [et_pb_section fb_built=\"1\" _builder_version=\"4.25.1\" _module_preset=\"default\" background_color=\"#f7f7f7\" background_image=\"https:\/\/www.ctengineeringgroup.com\/wp-content\/uploads\/2024\/06\/blanco_pequeno.png\" background_size=\"custom\" background_image_width=\"0%\" background_position=\"top_left\" background_horizontal_offset=\"-10%\" background_vertical_offset=\"-100%\" global_colors_info=\"{}\" theme_builder_area=\"post_content\"][et_pb_row _builder_version=\"4.25.1\" _module_preset=\"default\" global_colors_info=\"{}\" theme_builder_area=\"post_content\"][et_pb_column type=\"4_4\" _builder_version=\"4.25.1\" _module_preset=\"default\" global_colors_info=\"{}\" theme_builder_area=\"post_content\"][et_pb_text _builder_version=\"4.25.1\" _module_preset=\"default\" text_font=\"--et_global_body_font||||||||\" text_text_color=\"#01426a\" custom_padding=\"|10%||10%|false|true\" global_colors_info=\"{}\" theme_builder_area=\"post_content\"]<\/p> [\/et_pb_text][\/et_pb_column][\/et_pb_row][\/et_pb_section][et_pb_section fb_built=\"1\" theme_builder_area=\"post_content\" _builder_version=\"4.25.1\" _module_preset=\"default\" title_text=\"AdobeStock_818513091-scaled-e1716393061652\" background_image=\"https:\/\/www.ctengineeringgroup.com\/wp-content\/uploads\/2024\/05\/AdobeStock_818513091-scaled-e1716393061652-1.jpeg\" hover_enabled=\"0\" sticky_enabled=\"0\" min_height=\"500px\"][\/et_pb_section][et_pb_section fb_built=\"1\" _builder_version=\"4.16\" global_colors_info=\"{}\"][et_pb_row _builder_version=\"4.16\" background_size=\"initial\" background_position=\"top_left\" background_repeat=\"repeat\" global_colors_info=\"{}\"][et_pb_column type=\"4_4\" _builder_version=\"4.16\" custom_padding=\"|||\" global_colors_info=\"{}\" custom_padding__hover=\"|||\"][et_pb_text _builder_version=\"4.16\" background_size=\"initial\" background_position=\"top_left\" background_repeat=\"repeat\" global_colors_info=\"{}\"]<\/p> Communication is the linchpin of today's fast-paced global society, facilitating seamless interaction and the flow of information at unprecedented speeds. At CT, we recognize the crucial role of satellite communications in shaping our world. From global connectivity to emergency response and beyond, these technologies permeate every aspect of our daily lives. By leveraging cost-effective technologies, we aim to democratize access to reliable communication tools, ensuring that everyone can benefit from the boundless possibilities of satellite-based connectivity.<\/p> <\/p> <\/p> Revolutionizing connectivity with our cost-effective antenna for enhanced satellite communications.<\/strong><\/p> <\/p> <\/p> As technology evolves, the optimization of existing technologies becomes paramount to enhance their performance, reduce costs, and broaden accessibility. Our innovative approach to satellite communications focuses on the design of a cost-effective H-plane horn antenna with a dielectric lens using Substrate Integrated Waveguide (SIW) technology. By harnessing affordable technologies, we strive to ensure that everyone can benefit from the limitless possibilities of satellite-based communication.<\/p> <\/p> <\/p> This antenna has been designed for SATCOM applications, operating in Ka-band, within the 26.5 GHz to 40 GHz frequency range. It enables high-speed data, voice, and video transmission, catering to a wide range of applications, from broadband communications and video streaming to mobile communications in remote areas and government\/military services. Furthermore, the antenna's compatibility with satellite navigation systems, earth observation, and meteorology applications underscores its significance in scientific research and environmental monitoring.<\/p> <\/p> <\/p> Tools used<\/strong>:<\/p> <\/p> <\/p> <\/p> <\/p> <\/p> <\/p> <\/p> <\/p> <\/p> Design and simulation<\/strong><\/p> <\/p> <\/p> The antenna design process involves integrating conventional and SIW rectangular waveguides, sectoral horn antennas, and dielectric lenses on a single substrate using SIW technology. The following paragraphs provide details regarding the phases of the design process.<\/p> <\/p> <\/p> <\/p> <\/p> <\/p> <\/p> <\/p> <\/p> <\/p> <\/p> <\/p> <\/p> <\/p> <\/p> <\/p> The development of a new antenna enhances global satellite communications with promising features and opens avenues for further advancements in technology.<\/strong><\/p> <\/p> <\/p> In the intricate landscape of satellite communications, the development of this antenna offers a cost-effective and innovative solution for enhancing global connectivity. The results of this prototype have been impressive, providing high directivity and improved field uniformity, making it suitable for various applications.<\/p> <\/p> <\/p> Future efforts could focus on refining the feeding mechanism, potentially transitioning to waveguide technology for better performance at higher frequencies. Additionally, manufacturing a functional prototype and exploring the feasibility of multi-horn arrays opens exciting avenues for further research and development in SATCOM systems.<\/p> <\/p> <\/p> <\/p> <\/p> [\/et_pb_text][et_pb_button button_url=\"https:\/\/www.ctengineeringgroup.com\/wp-content\/uploads\/2024\/07\/Empowering-global-connectivity-2.pdf\" button_text=\"Download the full article\" button_alignment=\"center\" _builder_version=\"4.25.1\" _module_preset=\"default\" custom_button=\"on\" button_text_color=\"#707372\" button_bg_color=\"#f7f7f7\" button_border_width=\"0px\" button_icon=\"\ue092||divi||400\" button_icon_color=\"#707372\" button_icon_placement=\"left\" button_on_hover=\"off\" hover_enabled=\"0\" global_colors_info=\"{}\" theme_builder_area=\"post_content\" sticky_enabled=\"0\"][\/et_pb_button][\/et_pb_column][\/et_pb_row][et_pb_row _builder_version=\"4.25.1\" _module_preset=\"default\"][et_pb_column _builder_version=\"4.25.1\" _module_preset=\"default\" type=\"4_4\"][et_pb_text _builder_version=\"4.25.1\" _module_preset=\"default\" text_font=\"CTgroup Bold||||||||\" text_text_color=\"#f2a900\" text_font_size=\"24px\" custom_margin=\"||-1px||false|false\" hover_enabled=\"0\" global_colors_info=\"{}\" theme_builder_area=\"post_content\" sticky_enabled=\"0\" custom_padding=\"||50px||false|false\"]<\/p> [\/et_pb_text][et_pb_blog _builder_version=\"4.25.1\" _module_preset=\"default\" include_categories=\"current\" posts_number=\"3\" fullwidth=\"off\" hover_enabled=\"0\" sticky_enabled=\"0\"][\/et_pb_blog][\/et_pb_column][\/et_pb_row][\/et_pb_section]<\/p>","_et_gb_content_width":"","footnotes":""},"categories":[173,36],"tags":[71,287],"class_list":["post-260898","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-actualites","category-news","tag-awes","tag-awes-fr"],"yoast_head":"\nCT takes a further step towards Industry 4.0 with the successful outcome of the Plug&Play R&D project.<\/h1>
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