Multi-Thread Learning and Adaptation for Spacecraft Attitude Control

In this paper, a new multi-thread, quasi-attracting manifold controller for attitude tracking of a spacecraft with an unknown inertia matrix is introduced. The controller guarantees closed-loop stability for any given reference trajectory while adapting many estimates of unknown parameters such that any one particular estimate almost never moves further away from the truth than its previous estimate. While maintaining this “no-regret” learning feature for each individual thread, all of the threads are mixed into a single composite estimate using a judi-ciously designed weighting scheme that rewards both past and present performance of each individual thread. This provides faster error convergence and superior transient response performance compared with traditional single-thread based adaptive control implementations. The need for rapid convergence of poorly determined inertia properties is particularly relevant when recovering non-cooperative or uncontrolled space objects such as space debris or other on-orbit servicing applications. The beneficial features of the proposed multi-thread learning scheme is demonstrated via numerical simulations.