{"id":570809,"date":"2024-08-26T16:00:57","date_gmt":"2024-08-26T20:00:57","guid":{"rendered":"https:\/\/engineering.jhu.edu\/ece\/?post_type=news&p=570809"},"modified":"2024-08-27T15:19:47","modified_gmt":"2024-08-27T19:19:47","slug":"enhancing-robot-reliability-with-ai-and-mathematics","status":"publish","type":"news","link":"https:\/\/engineering.jhu.edu\/ece\/news\/enhancing-robot-reliability-with-ai-and-mathematics\/","title":{"rendered":"Enhancing Robot Reliability with AI and Mathematics"},"content":{"rendered":"

Ensuring the safety and reliability of advanced algorithms that control many complex systems such as self-driving cars or robots is an ongoing challenge, especially as these systems are deployed in increasingly diverse environments.<\/p>\n

To address this challenge, engineers at Johns Hopkins University have developed a novel method that combines artificial intelligence\u2014specifically neural networks\u2014with advanced mathematical techniques to create more reliable control systems for complex machines. Their results appear in IEEE Control Systems and Letters<\/em><\/a>.<\/p>\n

\u201cBy using advanced AI techniques with a principled mathematical framework, we\u2019ve significantly improved the stability and reliability of automated systems. This advancement could lead to the development of more dependable robots and autonomous vehicles that can safely operate under a broad range of conditions,\u201d said Jiarui Wang, MS \u201923, who worked with Mahyar Fazlyab<\/a>, an assistant professor of electrical and computer engineering and instructor in the Whiting School of Engineering\u2019s Engineering for Professionals\u2019 ECE program<\/a>, on the study.<\/p>\n

Wang said one key goal was to expand what’s known as the “region of attraction” (RoA): The range of states from which the control system is guaranteed to be able to stabilize the machine.<\/p>\n

To this end, Wang and Fazlyab combined neural networks with a mathematical tool called Zubov’s Partial Differential Equation (PDE). This approach allows for precise mapping of the RoA\u2019s boundaries, providing a clearer understanding of where a control system is effective and under what conditions it might fail.<\/p>\n

\u201cOur approach combines the Zubov PDE with actor-critic framework in deep reinforcement learning which contains two neural networks,\u201d said Wang. \u201cOne neural network \u2013 the actor \u2013 learns to control the robot while the other \u2013 the critic \u2013 is trained with the Zubov PDE to predict how far the machine is from stabilization. This collaboration allows us to improve and evaluate the controller jointly.\u201d<\/p>\n

The researchers say their approach thus ensures that the neural networks not only keep the machine stable but also maximize the area where this stability holds true.<\/p>\n

\u201cWhat set this approach apart is that it combines the Zubov PDE from control theory, physics-informed neural networks\u2014which is a modern method to numerically solve PDEs\u2014and the actor-critic framework from modern reinforcement learning,\u201d said Fazlyab. \u201cThis means we can accurately characterize the RoA and effectively learn the controller at the same time\u201d<\/p>\n

In practical tests across different scenarios, their method consistently increased the size of the RoA compared to traditional methods. This means the control systems could maintain stability over a larger range of conditions, making them more reliable and robust in real-world applications, they say.<\/p>\n

Wang and Fazlyab say they plan to apply their method to even more complex systems, ensuring that these control systems remain resilient against unexpected changes or errors in real-world dynamics. They believe this could lead to safer and more efficient technologies across industries, from robotics to autonomous vehicles.<\/p>\n","protected":false},"template":"","class_list":["post-570809","news","type-news","status-publish","hentry","news_categories-department-news","news_categories-research"],"acf":[],"yoast_head":"\nEnhancing Robot Reliability with AI and Mathematics - Department of Electrical and Computer Engineering<\/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:\/\/engineering.jhu.edu\/ece\/news\/enhancing-robot-reliability-with-ai-and-mathematics\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Enhancing Robot Reliability with AI and Mathematics - Department of Electrical and Computer Engineering\" \/>\n<meta property=\"og:description\" content=\"Ensuring the safety and reliability of advanced algorithms that control many complex systems such as self-driving cars or robots is an ongoing challenge, especially as these systems are deployed in…\" \/>\n<meta property=\"og:url\" content=\"https:\/\/engineering.jhu.edu\/ece\/news\/enhancing-robot-reliability-with-ai-and-mathematics\/\" \/>\n<meta property=\"og:site_name\" content=\"Department of Electrical and Computer Engineering\" \/>\n<meta property=\"article:modified_time\" content=\"2024-08-27T19:19:47+00:00\" \/>\n<meta name=\"twitter:card\" content=\"summary_large_image\" \/>\n<meta name=\"twitter:label1\" content=\"Est. reading time\" \/>\n\t<meta name=\"twitter:data1\" content=\"3 minutes\" \/>\n<!-- \/ Yoast SEO plugin. -->","yoast_head_json":{"title":"Enhancing Robot Reliability with AI and Mathematics - 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