Reinforcement Learning Compensatory-Based Fully Actuated Control Method for Risley Prisms

Beam pointing control based on Risley prisms is of great significance in wide-angle, high-precision application scenarios, such as laser communication, but its inherent nonlinear system characteristics seriously restrict the performance of beam pointing control, such as accuracy. For this reason, this paper combines the theory of fully actuated control with reinforcement learning methods and designs a fully actuated control method based on reinforcement learning compensation: suppressing the influence of system nonlinearity through fully actuated control, using reinforcement learning to estimate system perturbations and nonlinearities, and then outputting a compensated control quantity using the low-dimensional output of fully actuated control as the reference input of reinforcement learning reduces the complexity of learning and realises the end-to-end uncertainty estimation. Finally, the stability of the method is theoretically analyzed, and the effectiveness of the method is verified by experimental analysis, which can further improve the beam pointing accuracy of the Risley prism system.