Powered Landing Control of Reusable Rockets Based on Softmax Double DDPG

Abstract

Multi-stage launch vehicles are currently the primary tool for humans to reach extraterrestrial space. The technology of recovering and reusing rockets can effectively shorten rocket launch cycles and reduce space launch costs. With the development of deep representation learning, reinforcement learning (RL) has become a robust learning framework capable of learning complex policies in high-dimensional environments. In this paper, a deep reinforcement learning-based reusable rocket landing control method is proposed. The mathematical process of reusable rocket landing is modelled by considering the aerodynamic drag, thrust, gravitational force, and Earth’s rotation during the landing process. A reward function is designed according to the rewards and penalties derived from mission accomplishment, terminal constraints, and landing performance. Based on this, the Softmax double deep deterministic policy gradient (SD3) deep RL method is applied to build a robust reusable rocket landing control method. In the constructed simulation environment, the proposed method can achieve convergent and robust control results, proving the effectiveness of the proposed method.