{"id":19627,"date":"2025-05-12T13:16:19","date_gmt":"2025-05-12T13:16:19","guid":{"rendered":"https:\/\/aerospacerepository.org\/?p=19627"},"modified":"2025-09-10T10:29:47","modified_gmt":"2025-09-10T10:29:47","slug":"stall-flutter-suppression-with-active-camber-morphing-based-on-reinforcement-learning","status":"publish","type":"post","link":"https:\/\/aerospacerepository.org\/index.php\/2025\/05\/12\/stall-flutter-suppression-with-active-camber-morphing-based-on-reinforcement-learning\/","title":{"rendered":"Stall flutter suppression with active camber morphing based on reinforcement learning"},"content":{"rendered":"\n<p><strong>Jinying Li, Yuting Dai, Chao Yang<\/strong><\/p>\n\n\n\n<p><strong>DOI Number: https:\/\/doi.org\/10.82439\/ceas-ifasd-2024-032<\/strong><\/p>\n\n\n\n<p><strong>Conference number: IFASD-2024-032<\/strong><\/p>\n\n\n\n<p>This study investigates the adaptation of reinforcement learning into stall flutter suppression. The geometric model is a NACA0012 airfoil with active trailing edge morphing. Firstly, an offline, rapid responsive stall flutter environment is constructed with differential equations, where the aerodynamic force is predicted with reduced-order models. A double-Q-network (DQN) algorithm is adapted to train the controlling agent with the proposed offline environment. The agent has 5 optional actions with different amplitudes and directions of morphing. The reward function is designed with a linear combined punishment of pitching angle and angular velocity, a large bonus reward on complete suppression, and a large punishment on over-limit morphing. The trained agent shows a rapid and complete stall flutter suppression performance in offline environment simulation, where different sets of observations and scores are discussed.<\/p>\n\n\n\n<p><a href=\"https:\/\/aerospacerepository.org\/wp-content\/uploads\/2025\/05\/32.pdf\">Read the full paper here<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"<p><b>Jinying Li, Yuting Dai, Chao Yang<\/b><\/p>\n<p>DOI Number: https:\/\/doi.org\/10.82439\/ceas-ifasd-2024-032<\/p>\n<p>Conference number: IFASD-2024-032<\/p>\n","protected":false},"author":2,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[993,3023,3022],"tags":[2958,3311,1969],"class_list":["post-19627","post","type-post","status-publish","format-standard","hentry","category-events","category-aeroelastic-analysis-frameworks-1-ifasd-2024","category-ifasd-2024","tag-flexible-wing","tag-reinforcement-learning","tag-stall-flutter","category-993","category-3023","category-3022","description-off"],"_links":{"self":[{"href":"https:\/\/aerospacerepository.org\/index.php\/wp-json\/wp\/v2\/posts\/19627","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/aerospacerepository.org\/index.php\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/aerospacerepository.org\/index.php\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/aerospacerepository.org\/index.php\/wp-json\/wp\/v2\/users\/2"}],"replies":[{"embeddable":true,"href":"https:\/\/aerospacerepository.org\/index.php\/wp-json\/wp\/v2\/comments?post=19627"}],"version-history":[{"count":3,"href":"https:\/\/aerospacerepository.org\/index.php\/wp-json\/wp\/v2\/posts\/19627\/revisions"}],"predecessor-version":[{"id":20411,"href":"https:\/\/aerospacerepository.org\/index.php\/wp-json\/wp\/v2\/posts\/19627\/revisions\/20411"}],"wp:attachment":[{"href":"https:\/\/aerospacerepository.org\/index.php\/wp-json\/wp\/v2\/media?parent=19627"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/aerospacerepository.org\/index.php\/wp-json\/wp\/v2\/categories?post=19627"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/aerospacerepository.org\/index.php\/wp-json\/wp\/v2\/tags?post=19627"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}