SAC-Based Intelligent Load Relief Attitude Control Method for Launch Vehicles - Zhou, Shou; Yang, Hao; Zhang, Shifeng; Bai, Xibin; Wang, Feng (Aerospace, 2025)
This paper proposes an intelligent control method based on Soft Actor-Critic (SAC) to address uncertainties faced by flight vehicles during flight. The method effectively reduces aerodynamic loads and enhances the reliability of structural strength under significant wind disturbances. A specific launch vehicle is taken as the research subject, and its dynamic model is established. A deep reinforcement learning (DRL) framework suitable for the attitude control problem is constructed, along with a corresponding training environment. A segmented reward function is designed: the initial stage emphasizes tracking accuracy, the middle stage, with a detrimental effect due to the high-altitude wind region, focuses on load relief, and the final stage gradually resumes following tracking accuracy on the basis of maintaining the effect of load relief. The reward function dynamically switches between stages using a time factor. The improved SAC algorithm is employed to train the agent over multiple epochs, ultimately resulting in an intelligent load relief attitude controller applicable to the launch vehicle. Simulation experiments demonstrate that this method effectively solves the attitude control problem under random wind disturbances, particularly reducing the aerodynamic loads of launch vehicles in the high-altitude wind region.