{"id":260513,"date":"2024-05-29T10:18:33","date_gmt":"2024-05-29T09:18:33","guid":{"rendered":"https:\/\/www.ctengineeringgroup.com\/ct-has-completed-the-iadgenol-project-on-automating-awe-system-trajectories-using-deep-learning\/"},"modified":"2025-04-14T14:56:13","modified_gmt":"2025-04-14T13:56:13","slug":"ct-concluye-el-proyecto-iadgenol","status":"publish","type":"post","link":"https:\/\/www.ctengineeringgroup.com\/es\/ct-concluye-el-proyecto-iadgenol\/","title":{"rendered":"CT concluye el proyecto IADGENOL sobre la automatizaci\u00f3n de las trayectorias de los sistemas AWE utilizando Deep Learning."},"content":{"rendered":"

[et_pb_section fb_built=\u00bb1″ _builder_version=\u00bb4.26.1″ custom_margin=\u00bb0px||||false|false\u00bb custom_padding=\u00bb0px||||false|false\u00bb da_disable_devices=\u00bboff|off|off\u00bb global_colors_info=\u00bb{}\u00bb theme_builder_area=\u00bbpost_content\u00bb da_is_popup=\u00bboff\u00bb da_exit_intent=\u00bboff\u00bb da_has_close=\u00bbon\u00bb da_alt_close=\u00bboff\u00bb da_dark_close=\u00bboff\u00bb da_not_modal=\u00bbon\u00bb da_is_singular=\u00bboff\u00bb da_with_loader=\u00bboff\u00bb da_has_shadow=\u00bbon\u00bb][et_pb_row _builder_version=\u00bb4.16″ background_size=\u00bbinitial\u00bb background_position=\u00bbtop_left\u00bb background_repeat=\u00bbrepeat\u00bb global_colors_info=\u00bb{}\u00bb theme_builder_area=\u00bbpost_content\u00bb][et_pb_column type=\u00bb4_4″ _builder_version=\u00bb4.16″ custom_padding=\u00bb|||\u00bb global_colors_info=\u00bb{}\u00bb custom_padding__hover=\u00bb|||\u00bb theme_builder_area=\u00bbpost_content\u00bb][et_pb_text _builder_version=\u00bb4.27.4″ background_size=\u00bbinitial\u00bb background_position=\u00bbtop_left\u00bb background_repeat=\u00bbrepeat\u00bb hover_enabled=\u00bb0″ global_colors_info=\u00bb{}\u00bb theme_builder_area=\u00bbpost_content\u00bb sticky_enabled=\u00bb0″]<\/p>\n

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El proyecto IADGENOL se enmarca dentro de los campos de tecnolog\u00edas AWES (Airborne Wind Energy Systems o energ\u00eda e\u00f3lica aerotransportada<\/em>) y tecnolog\u00edas de aprendizaje profundo e inteligencia artificial. En este proyecto se pretenden investigar y aplicar, desde el punto de vista del control autom\u00e1tico, tecnolog\u00edas de aprendizaje autom\u00e1tico para el control de sistemas AWES utilizando datos procedentes tanto de un entorno de simulaci\u00f3n como de una m\u00e1quina AWES en operaci\u00f3n.<\/p>\n

AWES representa un enfoque innovador para aprovechar el viento en las capas altas de la atm\u00f3sfera, pero enfrenta desaf\u00edos significativos, especialmente en cuanto a autonom\u00eda operativa. Actualmente, lograr un control aut\u00f3nomo robusto es uno de los principales obst\u00e1culos; los sistemas deben operar de forma completamente aut\u00f3noma y soportar condiciones clim\u00e1ticas variables durante largos per\u00edodos. Para mejorar la adaptabilidad de los sistemas de control tradicionales, se ha estudiado el uso del aprendizaje autom\u00e1tico y el aprendizaje por refuerzo.<\/p>\n

El objetivo principal de la iniciativa IADGENOL fue la creaci\u00f3n de un modelo de control basado en aprendizaje profundo para el control autom\u00e1tico de trayectorias de sistemas AWE, as\u00ed como el empleo de estos modelos para comprender y caracterizar los desaf\u00edos din\u00e1micos a los que se enfrentan dichos sistemas. Este proyecto ha sido llevado a cabo \u00edntegramente por CT utilizando sus propios recursos y la m\u00e1quina de pruebas AWES compartida, disponible a trav\u00e9s de un acuerdo de colaboraci\u00f3n con la Universidad Carlos III de Madrid para el desarrollo de tecnolog\u00edas AWES. Las tareas realizadas abarcan desde la investigaci\u00f3n preliminar sobre el estado del arte, la miner\u00eda de datos y su procesamiento para su posterior uso en el desarrollo de modelos, hasta el desarrollo mismo de los modelos basados en datos y del controlador.<\/p>\n

\u00a0Se han llegado a cabo pruebas de validaci\u00f3n del sistema de control en la operaci\u00f3n real. Como paso previo a estas pruebas el controlador ha sido entrenado en un entorno de simulaci\u00f3n AWES desarrollado por CT. Los resultados de esta prueba, simulando un \u201caprendizaje natural\u201d han resultado muy interesantes pues se ha comprobado que la cometa es capaz de alinearse con el viento y realizar figuras de ocho, que son las trayectorias normalmente utilizadas en este tipo de sistemas para maximizar la generaci\u00f3n de energ\u00eda.<\/p>\n

Por otro lado, el paso de un entorno simulado a la implementaci\u00f3n en condiciones reales no ha resultado completamente exitoso. Sin embargo, debido a los resultados alentadores obtenidos durante las simulaciones, se considera que el problema podr\u00eda resolverse afinando los m\u00e9todos de simulaci\u00f3n actuales y creando entornos controlados de laboratorio que permitan entrenar estos algoritmos con un nivel de riesgo m\u00ednimo.<\/p>\n

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Resultados principales:<\/strong><\/p>\n

Durante la ejecuci\u00f3n del proyecto se han desarrollado tres l\u00edneas de investigaci\u00f3n paralelas: un controlador basado en aprendizaje profundo (Deep Learning<\/em>), un sistema de caracterizaci\u00f3n de la f\u00edsica de un sistema AWES y un sistema de predicci\u00f3n de viento.<\/p>\n

\u00a0Para el controlador basado en deep learning<\/em><\/strong><\/p>\n

Se desarroll\u00f3 un controlador utilizando algoritmos de aprendizaje por refuerzo profundo (DRL), de los que finalmente se escogi\u00f3 el basado en TD3 (Twin-Delayed Deep Deterministic Policy Gradient<\/em>).<\/p>\n

El controlador demostr\u00f3 un rendimiento estable al dirigir la cometa en trayectorias de ocho en un entorno simulado sin r\u00e1fagas de viento, aunque su efectividad se vio limitada por los vientos variables, debido a las restricciones del simulador. M\u00e1s tarde, el controlador fue integrado con el sistema de control de la m\u00e1quina AWES real y probado en condiciones reales. Aunque no se alcanz\u00f3 un control eficiente de la cometa, debido a la falta de entrenamiento en un entorno real y las dificultades asociadas, se verific\u00f3 la correcta integraci\u00f3n del controlador con la m\u00e1quina.<\/p>\n

Sistema de caracterizaci\u00f3n de la f\u00edsica<\/strong><\/p>\n

Se desarrollaron modelos din\u00e1micos basados en redes neuronales para predecir la din\u00e1mica de vuelo del sistema. Estos modelos demostraron ser efectivos al predecir la din\u00e1mica en un horizonte temporal de 0.1 segundos. Sin embargo, la propagaci\u00f3n de errores en las predicciones afect\u00f3 al rendimiento del sistema en comparaci\u00f3n con una trayectoria de referencia.<\/p>\n

Sistema de predicci\u00f3n de viento<\/strong><\/p>\n

Se desarroll\u00f3 un sistema para predecir el viento a diferentes alturas utilizando datos meteorol\u00f3gicos de la superficie. Sin embargo, la evaluaci\u00f3n de la precisi\u00f3n de las predicciones fue limitada debido a la falta de equipos de medici\u00f3n adecuados. A pesar de esto, el sistema fue integrado exitosamente en el controlador, lo que permiti\u00f3 la toma de decisiones en tiempo real.<\/p>\n

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Tras dos a\u00f1os de investigaci\u00f3n, CT ha desarrollado con \u00e9xito un modelo de control basado en Deep Learning que aborda los desaf\u00edos din\u00e1micos de las operaciones aut\u00f3nomas de los sistemas AWE.
\nLos resultados de las \u00faltimas pruebas de validaci\u00f3n en un entorno de simulaci\u00f3n para el sistema de control AWES, han sido presentados en el Congreso Europeo AWES, AWEC2024, en Madrid, demostrando una precisa alineaci\u00f3n con el viento y trayectorias \u00f3ptimas de generaci\u00f3n de energ\u00eda.<\/p>\n","protected":false},"author":1,"featured_media":255310,"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>

CT takes a further step towards Industry 4.0 with the successful outcome of the Plug&Play R&D project.<\/h1>

[\/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>

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Revolutionizing connectivity with our cost-effective antenna for enhanced satellite communications.<\/strong><\/p>

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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>

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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>

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Tools used<\/strong>:<\/p>

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    • CST Studio Suite<\/li><\/ul><\/li><\/ul>

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        • MATLAB<\/li><\/ul><\/li><\/ul>

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            • Simulink<\/li><\/ul><\/li><\/ul>

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              Design and simulation<\/strong><\/p>

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              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>

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                • Substrate Integrated Waveguide (SIW) technology <\/strong>to design rectangular waveguides was employed using printed technology, a cost-effective alternative. It employs a structure consisting of two parallel transverse metallic plates and two parallel arrays of metallic vias on a dielectric substrate. These vias simulate the walls of a conventional rectangular waveguide. The significant advantage of SIW over traditional designs is its considerably lower manufacturing costs. Furthermore, this technology has emerged as an alternative to planar technology for achieving low losses in very high frequency bands (SATCOM).<\/li><\/ul><\/li><\/ul>

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                    • H-Plane horn antenna.<\/strong> Its main characteristic is their constant height while modifying the width in the H-plane, resulting in cylindrical wavefronts. Key components in microwave systems, known for their ability to efficiently transmit signals with high gain and low losses. They work by flaring the signal at the end of a waveguide, ensuring effective communication with receiving antennas.<\/li><\/ul><\/li><\/ul>

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                        • Dielectric lens. <\/strong>Horn antennas face challenges in evenly distributing signals across their aperture, which affects negatively their performance. To tackle this issue, a solution involves adding a dielectric lens at the antenna's opening. This lens, placed in front of the aperture, modifies the way electromagnetic waves pass through, thereby improving the antenna's radiation pattern. The benefits include increased signal strength, controlled beam direction, reduced unwanted radiation, and the ability to operate in specific frequency bands.<\/li><\/ul><\/li><\/ul>

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                            • Coaxial feeding. <\/strong>It employs a connector with two conductors separated by Teflon insulation, allowing smooth signal transmission to the antenna.<\/li><\/ul><\/li><\/ul>

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                                • Design, optimization, and simulation of the unitary element. <\/strong>Results show good reflection coefficients and radiation patterns. The dimensions of the SIW H-plane horn has been optimized, considering factors like directivity and aperture efficiency. The addition of a dielectric lens improves phase uniformity and radiation efficiency. Overall, the design process aims to optimize antenna performance through careful integration and optimization of the components.<\/li><\/ul><\/li><\/ul>

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                                  The development of a new antenna enhances global satellite communications with promising features and opens avenues for further advancements in technology.<\/strong><\/p>

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                                  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>

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                                  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>

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                                  [\/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>

                                  Related Insights<\/h2><\/div>
                                  \u00a0<\/div>

                                  [\/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":[36,19],"tags":[71,279],"class_list":["post-260513","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-news","category-noticias","tag-awes","tag-awes-es"],"yoast_head":"\nCT concluye el proyecto IADGENOL sobre la automatizaci\u00f3n de las trayectorias de los sistemas AWE utilizando Deep Learning. - The CT Engineering Group<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/www.ctengineeringgroup.com\/ct-has-completed-the-iadgenol-project-on-automating-awe-system-trajectories-using-deep-learning\/\" \/>\n<meta property=\"og:locale\" content=\"es_ES\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"CT concluye el proyecto IADGENOL sobre la automatizaci\u00f3n de las trayectorias de los sistemas AWE utilizando Deep Learning. - The CT Engineering Group\" \/>\n<meta property=\"og:description\" content=\"Tras dos a\u00f1os de investigaci\u00f3n, CT ha desarrollado con \u00e9xito un modelo de control basado en Deep Learning que aborda los desaf\u00edos din\u00e1micos de las operaciones aut\u00f3nomas de los sistemas AWE. 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