Autonomous Navigation and Station-keeping of High-Altitude Balloon Using Extremum Seeking Control

Stratospheric high-altitude balloons are non-extensible, sealed flexible structures designed to operate in the earth's stratosphere for extended periods. These balloons do not have propulsion engines, and their dynamics are entirely based on prevailing atmospheric wind conditions, making them vulnerable to being carried by wind currents. Station-keeping involves maintaining the balloon within a specific region for an extended duration. The standard control strategy utilizes atmospheric wind velocity variability with altitude, allowing a controller to predict the altitude with favourable wind velocities. In recent years, reinforcement learning-based station-keeping controllers have gained popularity. These controllers require extensive, realistic historical atmospheric training datasets to perform effectively. In the absence of such datasets, we propose a data-driven control strategy based on dual-mode extremum-seeking control (ESC) with vanishing oscillation for the navigation and station-keeping of high-altitude balloon platforms. Through simulation studies using real wind data from the National Oceanic and Atmospheric Administration (NOAA), we demonstrated that our proposed real-time optimization algorithm can successfully steer the balloon from one location to another without explicit knowledge of the prevailing wind dynamics.