Gain-Based Feedback for Shape Control of Antenna Reflectors Using Deep Q-Network Algorithm

An intelligent shape control method for antenna reflectors is proposed using gain feedback instead of displacement feedback, considering the limitations of high-precision deformation measurement on orbit. Moreover, the finite element method is used to establish an analysis model of a grid reflector with embedded piezoelectric actuators, and the antenna gain formula is derived. To solve the multidimensional control laws from a single gain dimension, an active shape control model training framework is proposed based on the deep Q-network algorithm, including the state space, action space, and reward function. Numerical simulations, with errors stemming from two typical thermal loads as the initial errors, are conducted to illustrate the effect of the proposed control method. The influence of the voltage step size on the shape control precision is analyzed, and further online training is discussed for implementing control. The results indicate that the proposed method reduces the root-mean-square (RMS) error by more than 50% The control effect of the trained control model on other error conditions does not perform as desired. However, online training can effectively solve this problem, achieving faster convergence and a significant reduction of the RMS error.