In laser systems, beam pointing usually drifts as a consequence of various disturbances, e.g.,
inherent drift, airflow, transmission medium variation, mechanical vibration, and elastic deformation.
In this paper, we develop a laser beam pointing control system with Fast Steering Mirrors (FSMs)
and Position Sensitive Devices (PSDs), which is capable of stabilizing both the position and angle
of a laser beam. Specifically, using the ABCD matrix, we analyze the kinematic model governing
the relationship between the rotation angles of two FSMs and the four degree-of-freedom (DOF)
beam vector. Then, we design a Jacobian matrix feedback controller, which can be conveniently
calibrated. Since disturbances vary significantly in terms of inconsistent physical characteristics and
temporal patterns, great challenges are imposed to control strategies. In order to improve beam
pointing control performance under a variety of disturbances, we propose a data-driven disturbance
classification method by using a Recurrent Neural Network (RNN). The trained RNN model can
classify the disturbance type in real time, and the corresponding type can be subsequently used to
select suitable control parameters. This approach can realize the universality of the beam stabilization
pointing system under various disturbances. Experiments on beam pointing control under several
typical external disturbances are carried out to verify the effectiveness of the proposed control system.