In this paper, we begin by describing the Starlink constellation’s configuration plan, treating each satellite as a network node, naming and numbering the various nodes, and then classifying the laser interstellar links (LISLs) according to their orbital alignment and whether they are permanently visible. Whereupon, the method for calculating the respective theoretical interstellar distances required for establishing LISLs under two distinct orbital scenarios, co-orbital and hetero-orbital are analyzed, and the optimal phasing factors are proposed by solving an optimization algorithm for the shortest distance. The OneWeb and Starlink constellations, as well as the constellation states of various phasing factors are simulated, respectively. The Starlink constellation with F = 11 obtains the best coverage between 60° north and south latitudes predicated upon the analysis of the constellation N Asset Coverage. Following that, the first phase of the Starlink constellation deployment was modeled in order to analyze and count the number of permanent LISLs in orbit, adjacent to, and nearby. Subsequently, the characteristics of azimuthal, elevation, and range (AER) were subsequently analyzed to ascertain their variation law and to compile the number of permanent and temporary LISLs that could be established at various inter-distance ranges. Finally, predicated on the simulation results, the optimal LISLs connection strategy for the Starlink constellation is evaluated and a static topology for the constellation is constructed.

This paper is devoted to model-free attitude control of rigid spacecraft in the presence of control torque saturation and external disturbances. Specifically, a model-free deep reinforcement learning (DRL) controller is proposed, which can learn continuously according to the feedback of the environment and realize the high-precision attitude control of spacecraft without repeatedly adjusting the controller parameters. Considering the continuity of state space and action space, the Twin Delayed Deep Deterministic Policy Gradient (TD3) algorithm based on actor-critic architecture is adopted. Compared with the Deep Deterministic Policy Gradient (DDPG) algorithm, TD3 has better performance. TD3 obtains the optimal policy by interacting with the environment without using any prior knowledge, so the learning process is time-consuming. Aiming at this problem, the PID-Guide TD3 algorithm is proposed, which can speed up the training speed and improve the convergence precision of the TD3 algorithm. Aiming at the problem that reinforcement learning (RL) is difficult to deploy in the actual environment, the pretraining/fine-tuning method is proposed for deployment, which can not only save training time and computing resources but also achieve good results quickly. The experimental results show that DRL controller can realize high-precision attitude stabilization and attitude tracking control, with fast response speed and small overshoot. The proposed PID-Guide TD3 algorithm has faster training speed and higher stability than the TD3 algorithm.

The safe and dependable removal of large-scale space debris has been a long-standing challenge that is critical to the safety of spacecraft and astronauts. In the process of capturing and deorbiting space debris, the space manipulator must achieve extremely high control and precision. However, strong couplings, model uncertainties, and various inevitable unknown disturbances cause many difficulties in coordinated control of the space manipulator. To solve this challenge, this study examines the stabilization control of a space manipulator after capturing non-cooperative large-scale space debris and presents an adaptive integral sliding mode control (AISMC) scheme with time-delay estimation (TDE). The coupling term and lumped uncertainty are estimated by TDE technology, which eliminates the requirement of prior knowledge. Adaptive sliding mode control (ASMC) is used as desired injecting dynamics to compensate TDE errors, and a PID-type integral sliding mode surface is designed to reduce steady-state errors. The Lyapunov criterion is used to prove the global stability of the controller. Simulation results show that the controller has high tracking accuracy and strong robustness.

This paper investigates the application of particle swarm optimization (PSO) algorithm to plan joint trajectories of the space modular reconfigurable satellite (SMRS). SMRS changes its configuration by joint motions to complete various space missions; its movement stability is affected by joints motions because of the dynamic coupling effect in space. To improve the movement stability in reconfiguration progress, this paper establishes the optimization object equation to characterize the movement stability of SMRS in its reconfiguration process. The velocity-level and position-level kinematic models based on the proposed virtual joint coordinate system of SMRS are derived. The virtual joint coordinate system solves the problem of asymmetric joint coordinate system resulted by the asymmetric joint arrangement of SMRS. The six-order and seven-order polynomial curves are chosen to parameterize the joint trajectories and ensure the continuous position, velocity, and acceleration of joint motions. Finally, PSO algorithm is used to optimize the trajectory parameters in two cases. Consistent optimization results in terms of the six-order and seven-order polynomial in both cases prove the PSO algorithm can be effectively used for joint trajectory planning of SMRS.

The massive mega constellation of satellites will have a significant impact on global space safety. With Starlink as an example, this paper is aimed at assessing the risk of in-orbit collision, analyzing the probability of collision in orbit in its natural operating state, and forecasting the probability of secondary collision between the collision-generated short-term debris cloud and satellites in the same orbit. The mass, size, velocity, and direction of space debris in a particular orbit of Starlink satellite are calculated based on the MASTER-8 model, and the shape characteristics of the Starlink satellite are added to the model to determine the probability of a Starlink satellite colliding with space debris in that orbit. A modified spacecraft impact disintegration model then is used to calculate the collision results and estimate the collision threat level of the short-term debris cloud formed by the Starlink satellite after its destruction to satellites in the same orbit. The results indicate that the collision probability of Starlink satellite in orbit natural operation exceeds the red warning threshold 10^-4 that the satellite disintegration after the first collision will generate 14088 pieces of debris over 1cm, of which 4092 debris are potentially dangerous to other spacecraft, and that the collision probability to a satellite in the same orbit exceeds the red warning threshold of 10^-4 within 30 minutes, implying that collision avoidance needs to be improved.