Reinforcement-Learning-Based Appointed-Time Prescribed Performance Attitude Control for Rigid Spacecraft

This paper addresses a geometric control algorithm for the attitude tracking problem of the rigid spacecraft modeled on SO(3). Considering the topological and geometric properties of SO(3), we introduced a smooth positive attitude error function to convert the attitude tracking issue on SO(3) into the stabilization counterpart on its Lie algebra. The error transformation technique was further utilized to ensure the assigned transient and steady state performance of the attitude tracking error with the aid of a well- designed assigned-time performance function. Then, using the actor-critic (AC) neural architecture, an adaptive reinforcement learning approximator was constructed, in which the actor neural network (NN) was utilized to approximate the unknown nonlinearity online. A critic function was introduced to tune the next phase of the actor neural network operation for performance improvement via supervising the system performance. A rigorous stability analysis was presented to show that the assigned system performance can be achieved. Finally, the effectiveness and feasibility of the constructed control strategy was verified by the numerical simulation.