MIT AeroAstro | Boeing Undergraduate Research and Innovation Scholar
Machine Learning in Autonomous Control for Airborne Wind Energy System
Among the novel renewable energy technologies, a new class of wind energy convertors, Airborne Wind Energy Systems (AWES), is rapidly growing. This new generation of technology employs aircrafts to capture high altitude winds that are inaccessible to traditional wind turbines. This project aims to apply the techniques of machine learning to the task of autonomously controlling the aircraft in order to optimize its trajectory and maximize the system’ s energy efficiency. An airborne wind energy system prototype with a fixed ground station and a non-powered soft kite is manufactured and is used as the testing platform in real world experiments.
SuperUROP offers a great opportunity to immerse myself in research as an undergraduate. It will allow me to explore cutting-edge research areas in depth and will help me figure out whether I’m interested in pursuing a career in academia. I’m really excited to combine my knowledge of computer science and aerospace engineering and apply them onto a real-world application that could potentially make an impact on how we think about renewable energy.