Attitude control and online estimation of unknown mass properties for captured debris with a flexible spacecraft

Abstract

With more spacecraft launched in Low Earth Orbit (LEO) for educational and research purposes, their mission completion or mission failure results in an increasing number of inactive orbiting space bodies. These uncooperative bodies can lead to collision with other space systems, creating the need to actively remove debris for collision avoidance and to increase the availability of orbital slots. Solutions currently investigated see the use of robust control in robot manipulators and tethered nets to maneuver a main spacecraft and the attached unknown debris. Another solution is to employ cameras and a combination of image processing technologies and output feedback to estimate the inertia tensor of the space debris to then employ it in a model-based controller architecture. However, robust control techniques and vision-based estimation suffer from practical disadvantages, like the added complexity in the Reaction Control System (RCS) with extra onboard fuel for the former, and the added complexity in computer resources and extra sensors for the latter. In this investigation, an adaptive attitude controller for a spacecraft debris collector is proposed. The adaptive control law uses Integral Concurrent Learning (ICL) to learn, onboard, the mechanical properties of an unknown spacecraft target using measurements of the states of the system already available onboard. During the collection of the system states and inputs, a classic adaptive controller is implemented to guarantee bounded stability of the system. Through numerical simulations of a test-case scenario, it is shown asymptotic convergence of both the attitude tracking error and the estimation error of the unknown parameters of the captured debirs and the flexible appendage. The results are compared to the system response of a classic adaptive controller that does not include ICL to show the improvements in performance ICL brings to the attitude maneuvering.