Space membrane structures, prized for their lightness, affordability, and ability
to fold and unfold, hold immense promise for missions like deep space
exploration, high-definition Earth observation, and advanced satellite
communications. However, these structures are typically large-size,
ultra-lightweight, highly flexible, and have very low damping. This leads to
complex dynamic behavior featuring significant geometric nonlinearity, strong
coupling between rigid and flexible motions, and uncertain parameters.
Consequently, creating accurate yet simple mathematical models for designing
controllers is exceptionally difficult, and traditional control methods that
rely on such models often fall short. To tackle this problem, this study focuses
on a membrane antenna spacecraft equipped with cable actuators, aiming to
suppress its vibrations during attitude maneuvers. Using the Deep Deterministic
Policy Gradient (DDPG) algorithm, an intelligent vibration controller based on
the Actor-Critic architecture is designed. This approach is data-driven, meaning
it learns effective control strategies directly through interaction with the
system’s environment, bypassing the need for a precise pre-defined mathematical
model. Simulation results show that the controller demonstrated superior
vibration suppression and strong adaptability across several key scenarios.