Aiming at the problem of efficient collaborative decision-making among multiple satellites in multi-satellite orbital interception mission, a multi-satellite cooperative orbital interception strategy based on attention mechanism and deep reinforcement learning is proposed. Firstly, considering the orbital dynamics, maneuverability, mission duration, and collision avoidance constraints faced in orbital interception mission, a Markov decision process is designed, and a multi-satellite game strategy solution framework based on the actor–critic network is built; then, a guided reward function is designed to effectively guide the pursuit satellite to approach and intercept the escaping satellite to accelerate the convergence speed of the algorithm; finally, the attention mechanism is used to capture the potential relationship between satellites and generate coded information with biased attention effect, which helps satellites form an efficient collaborative interception strategy. The simulation experiments show that the trained satellites can conduct autonomous learning and decision-making in a dynamic and uncertain environment. In orbital interception mission, the pursuit satellite can adopt an effective collaborative strategy to use the advantage of quantity to make up for the disadvantage of speed, maintain a high mission success rate, and a series of intelligent game behaviors emerge.

In this paper, we investigate analytical numerical iterative strategies for the pursuit-evasion game involving spacecraft with leader–follower information. In the proposed problem, the interplay between two spacecraft gives rise to a dynamic and real-time game, complicated further by the presence of perturbation. The primary challenge lies in crafting control strategies that are both efficient and applicable to real-time game problems within a nonlinear system. To overcome this challenge, we introduce the model prediction and iterative correction technique proposed in model predictive static programming, enabling the generation of strategies in analytical iterative form for nonlinear systems. Subsequently, we proceed by integrating this model predictive framework into a simplified Stackelberg equilibrium formulation, tailored to address the practical complexities of leader–follower pursuit-evasion scenarios. Simulation results validate the effectiveness and exceptional efficiency of the proposed solution within a receding horizon framework.

This paper presents Part Ⅱ of a review on DFACS, which specifically focuses on the modeling and analysis of disturbances and noises in DFACSs. In Part Ⅰ, the system composition and dynamics model of the DFACS were presented. In this paper, we discuss the effects of disturbance forces and noises on the system, and summarize various analysis and modeling methods for these interferences, including the integral method, frequency domain analysis method, and magnitude evaluation method. By analyzing the impact of disturbances and noises on the system, the paper also summarizes the system’s performance under slight interferences. Additionally, we highlight current research difficulties in the field of DFACS noise analysis. Overall, this paper provides valuable insights into the modeling and analysis of disturbances and noises in DFACSs, and identifies key areas for future research.

The Drag-Free and Attitude Control System (DFACS) is a critical platform for various space missions, including high precision satellite navigation, geoscience and gravity field measurement, and space scientific experiments. This paper presents a comprehensive review of over sixty years of research on the design and dynamics model of DFACS. Firstly, we examine the open literature on DFACS and its applications in Drag-Free missions, providing readers with necessary background information on the field. Secondly, we analyze the system configurations and main characteristics of different DFACSs, paying particular attention to the coupling mechanism between the system configuration and dynamics model. Thirdly, we summarize the dynamics modeling methods and main dynamics models of DFACS from multiple perspectives, including common fundamentals and specific applications. Lastly, we identify current challenges and technological difficulties in the system design and dynamics modeling of DFACS, while suggesting potential avenues for future research. This paper aims to provide readers with a comprehensive understanding of the state-of-the-art in DFACS research, as well as the future prospects and challenges in this field.