Computational adaptive optimal control of spacecraft attitude dynamics with inertia matrix identification

The development of a computational adaptive optimal controller for regulation and tracking of spacecraft attitude with unknown moment of inertia is the subject of this paper. The control technique accomplishes this by sequentially prioritizing higher goals of stabilization, optimization and identification. The proposed controller assumes measurement of angular velocities, and control torques. Conditions are derived that ensure asymptotic stability of the states. The proposed methodology uses an integral reinforcement learning based approach to drive the system to a linear quadratic optimal control solution. A novel identification method is derived that follows from the optimal controller formulation. Simulations of a spinning satellite with unknown moment of inertia are considered which shows successful optimal regulation and tracking of angular velocities along with accurate identification of unknown inertia.