Spacecraft Cluster Research Articles

Neural network-based control for the on-orbit assembly of heterogeneous spacecraft cluster based on Vicsek fractal

On-orbit assembly is a highly effective technique for building large space structures. This paper presents the dynamics and control of an on-orbit assembly of a large space structure based on a heterogenous spacecraft cluster comprising both rigid and flexible spacecraft. The topology of the large space structure is inspired by the Vicsek fractal. A distributed assembly strategy is proposed for the spacecraft team. Radial Basis Function neural networks (RBFNNs) are utilized to approximate the bounded uncertain terms in translational and attitude dynamics. To avoid collisions during the pre-assembly phase, a neural network (NN)-based controller with collision avoidance force is designed for relative position motion. During the assembly phase, only proportional-derivative (PD) law is employed for relative position control. In addition, an NN-based controller is designed for relative attitude control for both rigid and flexible spacecraft. To estimate the unmeasured flexible vibrations, modal coordinate observers are introduced for the flexible spacecraft. The stability of the closed-loop system is proved via Lyapunov functions. Moreover, numerical results are presented to validate the effectiveness of the proposed controllers and assembly strategy.

Cooperative autonomous obstacle avoidance control of spacecraft cluster via event-triggered reference governors

This paper conducts research on the design of a cooperative obstacle avoidance control mechanism for spacecraft cluster. It proposes an event-triggered reverse command regulator to address the current unidirectional obstacle avoidance mechanism, which only goes from the cluster reference command to the individual execution of spacecraft. The proposed mechanism enhances the survivability of spacecraft cluster in complex environments. By employing the designed event-triggered command regulator, the spacecraft can autonomously decide whether to execute a leader-following task based on whether the tracking error satisfies the performance requirements of the cluster closed-loop system. This allows the spacecraft to dynamically choose between task execution and temporarily “forgetting” the task to focus on collision avoidance, thereby increasing the collaborative flexibility of the spacecraft cluster. Furthermore, based on Lyapunov stability theory, the convergence of the proposed cooperative obstacle avoidance control algorithm is theoretically proven. Ultimately, simulation results demonstrate the real-time effectiveness of the designed cooperative obstacle avoidance solution.

Cooperative control method of multiple spacecraft formation based on graphtheory

Spacecraft cluster is a new way of multi-vehicle cooperation and a major problem in the current space distribution system. A large number of core technologies of spacecraft cluster technology need to be simulated, analyzed and verified on the ground. On this basis, this article proposes the construction of a spacecraft system model and a dynamic model, and designs a path planning method that combines the spacecraft system model with the a* algorithm. And use the Morphin search tree algorithm to improve the A-star algorithm, and then simulate and analyze the spacecraft formation control scheme. The simulation results of trajectory planning show that after running the model for 21 ms, the improved A* algorithm achieved the expected error. Its MAE value is 10 - 4, achieving good path planning effect. The research plan can effectively achieve control of spacecraft formation and meet the needs of collaborative tasks and navigation route planning.

Obstacle-Avoidance Distributed Reinforcement Learning Optimal Control for Spacecraft Cluster Flight

Obstacle-Avoidance Distributed Reinforcement Learning Optimal Control for Spacecraft Cluster Flight

Energetic Particle Injection During Short Isolated Bubble as Seen in RCM Simulation and Spacecraft Observations in the Flow Braking Region

AbstractAlthough energetic particle (EP) injections are commonly thought to be formed by the flow burst intrusion from the magnetotail, important details and quantitative aspects of their transport, acceleration and flow braking need further investigation and understanding. Motivated by frequent observations of short transient EP injections being not associated with substorms, we analyze high‐resolution Rice Convection Model simulations of a short (5‐min long) localized (∼3RE width) density depletion (evacuating 90% of flux tube content) initiated at the tailward simulation boundary (∼18RE) and allowed to evolve within an otherwise typical plasma sheet environment. We note that, driven by betatron‐like acceleration, the peak EP flux at fixed energy dramatically increases in a couple of minutes when the bubble head enters the inner magnetosphere at r < 8–10 RE giving rise to a localized injection of subsequently drifting EP clouds. Here the 50–200 keV electron flux reaches values as high as #105 (cm2 s sr keV)−1, and even higher energies (up to 1 MeV) may briefly appear. Surprisingly, at a later stage of bubble penetration, after termination of bubble jet from the tail, the injection boundary of high energy (HE) particles detaches from the bubble earthward boundary while the latter continues moving inward. Time History of Events and Macroscale Interactions during Substorms multi‐spacecraft mission observations of a short bubble‐like flow burst at the spacecraft cluster located near the flow stopping point, show much similarity with simulation results but also reveal important differences between responses of HE protons and electrons attributed to the finite gyroradius effect.

Energy- and time-optimal reconfiguration of spacecraft clusters with collision avoidance

Spacecraft cluster reconfiguration is one of the enabling technologies to ensure non-traditional attributes of distributed space systems. This work treats energy- and time-optimal reconfiguration problems for both circular and eccentric reference orbits. Furthermore, typical local and coupling constraints have been taken into account. Particularly, focus is given to two coupling constraints: final configuration constraints and collision avoidance constraints. For final configuration constraints, a parameterization method is applied to ensure that the reconfiguration problem can be solved as only one optimization problem, rather than a large number of optimization problems resulting from the traditional discretization method. A generalized formulation is proposed for non-convex collision avoidance constraints, which are then convexified via linearization and convex restriction technology. This method provides the affine approximation as a special case. After incorporating above constraints, the reconfiguration problem is formulated as an open-loop optimal control problem, which is solved via the Gauss pseudospectral method (GPM). By virtue of elegant features of GPM, those solutions can serve as a counterpart and stepping stone for a distributed implementation of reconfiguration algorithms. Various simulations demonstrate that minimum-energy/time cluster reconfiguration problems with collision avoidance for circular and eccentric reference orbits can be solved effectively and efficiently using GPM.

Resilient Autonomous Mission Management for Decentralized Multispacecraft Missions

Autonomy will play a crucial role in achieving complex mission goals and reducing the burden for ground operations for upcoming ambitious aerospace missions. Standalone spacecraft can leverage autonomy concepts to optimize data collection and ensure robust operation. Autonomy can additionally provide a feasible method of ensuring coordination for spacecraft clusters through onboard peer-to-peer scheduling. The core of this framework, the schedule manager (SM), manages tasks by associating constraints with each task including time windows, task priority, conflict categories, and resource requirements, which assures that tasks will only run when capable. This increased control over individual tasks also improves the modularity of the overall mission plan and provides a built-in fail-safe in the event of unexpected task failure through the loading of predefined contingency schedules. Various other capabilities were added to the SM, including the SM data server to simplify commanding, distributed coordination, and integration with NASA Goddard Space Flight Center’s core Flight System (cFS) platform. Software validation was achieved with cFS unit tests, functional tests, and code analysis tools. Demonstrations were tested with a cluster of development boards in the loop as representative flight hardware.

Correction: Hua et al. Path Planning of Spacecraft Cluster Orbit Reconstruction Based on ALPIO. Remote Sens. 2022, 14, 4768

In the original article [...]

Robust relative orbit synchronization for spacecraft cluster: A distributed learning sliding mode control approach

This paper investigates the issue of robust relative orbit synchronization control for spacecraft cluster subjects to space perturbation. First, the relative orbit dynamics of the leader-follower spacecraft cluster is established. Combined with P-type learning algorithm and sliding mode control (SMC) strategy, a novel distributed learning SMC (LSMC) strategy with the consensus algorithm is explored such that all the follower spacecrafts converge to the target relative position and their velocities tend to the leader spacecraft. The distributed P-type learning algorithm is used to update and compensate space perturbation for all the follower spacecrafts. The proposed LSMC approach has the potential to achieve higher control accuracy than the traditional SMC one while it has the lower computation burden than the distributed adaptive SMC one. The stability analysis of the proposed distributed LSMC strategy is provided in detail. Finally, a numerical example is simulated to illustrate the effectiveness and superiority of the proposed distributed LSMC-based relative orbit synchronization control for spacecraft cluster.

Cluster Observation of Ion Outflow in Middle Altitude LLBL/Cusp from Different Origins

The ionosphere is the ionized part of the upper atmosphere that is caused mainly by photoionization by solar extreme ultraviolet (EUV) emission and the atmospheric photochemistry process. The ionospheric ions escape from the ionosphere and populate the Earth’s magnetosphere. In this case study, ion outflows from two different origins were obtained by spacecraft Cluster C1 in the magnetospheric cusp region. One of the outflows was from the reflection of the dispersed solar wind particles. The other was the ionospheric outflow passing through the low latitude boundary layer of the cusp (LLBL/cusp), which was energized by downward Poynting flux. Similar to the reflected solar wind particles, outflowing ionospheric cold ions could also extend to the high-latitude region with magnetic field line convection, which mixed it up with solar wind particles. Based on the Cluster observation in the cusp region, two different origins of the outflowing particles were determined, and their unique mechanisms of formation were discussed. Results suggest that the strong electric field associated with solar wind particle precipitation may additionally accelerate the cold ionospheric ion flow in the LLBL/cusp.

Spacecraft cluster cloud high-frequency optimization computing theory

Spacecraft cluster cloud high-frequency optimization computing theory

Path Planning of Spacecraft Cluster Orbit Reconstruction Based on ALPIO

An adaptive learning pigeon-inspired optimization based on mutation disturbance (ALPIO) is proposed for solving the problems of fuel consumption and threat avoidance in spacecraft cluster orbit reconstruction. First, considering the constraints of maintaining a safe distance between adjacent spacecraft within the spacecraft cluster and of avoiding space debris, the optimal performance index for orbital reconfiguration is proposed based on the fuel consumption required for path planning. Second, ALPIO is proposed to solve the path planning. Compared with traditional pigeon-inspired optimization, ALPIO uses the initialization of chaotic and elite backward learning to increase the population diversity, using a nonlinear weighting factor and adjustment factor to control the speed and accuracy of prepopulation convergence. The Cauchy mutation was implemented in the map and compass operator to prevent the population from falling into local optima, and the Gaussian mutation and variation factor were utilized in the landmark operator to prevent the population from stagnating in the late evolution. Through simulation experiments using nine test functions, ALPIO is shown to significantly improve accuracy when obtaining the optimum compared with PSO, PIO, and CGAPIO, and orbital reconfiguration consumes less total fuel. The trajectory of path planning for ALPIO is smoother than those of other optimization methods, and its obstacle avoidance path is the most stable.

Features of the Formation of Temporary Clusters of Spacecraft Monitoring Radio Emission Sources on the Earth Surface

Features of the Formation of Temporary Clusters of Spacecraft Monitoring Radio Emission Sources on the Earth Surface

Proposal to provide power supply to a network of clusters of small spacecraft with a tandem spacecraft

Proposal to provide power supply to a network of clusters of small spacecraft with a tandem spacecraft

Theoretical aspects of the formation of a cluster of small spacecraft

Theoretical aspects of the formation of a cluster of small spacecraft

Research on dynamic path planning algorithm of spacecraft cluster based on cooperative particle swarm algorithm

In order to solve the problem of path planning for the spacecraft cluster to reach the dynamic target point under the premise of considering obstacle avoidance. In view of the fixed search radius, it will be difficult for the spacecraft to find a better value when it is close to the target point. This paper converts the orbital dynamics of each member spacecraft into an optimization problem considering constraints, and proposes an improved CPSO algorithm based on coordination. The path planning method of the traditional particle swarm optimization (CPSO): The dynamic radius search method that changes the search radius by changing the distance between them, and improves the CPSO algorithm based on this. The improved CPSO algorithm autonomously finds the optimal path of each member spacecraft at the current moment through the dynamic search radius, thereby obtaining the optimal solution for the dynamic path planning of the spacecraft cluster in three-dimensional space. The simulation results show that the use of the improved CPSO algorithm can not only obtain the optimal solution to the spacecraft cluster dynamic path planning problem, but also greatly reduce the fuel consumption in its path planning and improve the path stability of each member spacecraft.

МНОГОУРОВНЕВАЯ БАЛЛИСТИЧЕСКАЯ СТРУКТУРА КЛАСТЕРНОЙ ОРБИТАЛЬНОЙ ГРУППИРОВКИ ДИСТАНЦИОННОГО ЗОНДИРОВАНИЯ ЗЕМЛИ

The features of the application and justification of the orbital groupings of remote sensing of the Earth, consisting of clusters of small spacecraft, are considered. A review and analysis of the ballistic justification, construction and features of the use of orbital groups of remote sensing of the Earth is carried out. Modern approaches to the ballistic design of periodic review orbital groupings are considered. The article considers a new integrated approach to the ballistic construction of promising cluster orbital groupings, which will allow providing the spatio-temporal and accuracy characteristics required by the consumer, due to the optimal multi-level ballistic structure. The fundamental principles of constructing a cluster orbital grouping with a multi-level ballistic structure are formulated. The stages of the formation of a multi-level ballistic structure are considered in detail, from the standpoint of a systematic approach. A mathematical formulation of the problem and a hierarchy of performance indicators are proposed. For a meaningful description of the simulated system, a conceptual model for substantiating multi-level ballistic structures of a cluster orbital grouping of remote sensing of the Earth under the influence of the external environment has been developed. The model shows the interrelationships of the main elements of the substantiation of the ballistic structure of the cluster orbital grouping of remote sensing of the Earth and the sequence of formation of particular problems. The results of modeling both the ballistic structure of the cluster and the ballistic structure of the Earth remote sensing orbital grouping, which provides a set of tactical and technical, spatio-temporal and structurally stable consumer requirements, are presented. The effect of the application of the developed conceptual model will be the optimal strategy for the use of cluster orbital groupings of remote sensing of the Earth, which will provide the required value of its effectiveness under the influence of the external environment.

Групповое управление многоспутниковой орбитальной группировкой на основе концепции режимов совместного функционирования

The problem of control of multisatellite orbital groupings based on the concept of operating modes in conditions of limited control resources is considered. The control task is formulated, its solution is proposed. The content of the newly introduced operating modes of the entire space system is considered. The formalization of the grouping’s state description in the form of an almanac of the system, which allows automating the decision-making process for control, is given. It is concluded that it is necessary to develop practical methods of group control by creating a flight cluster of small spacecraft.

Long term and safe relative orbit design for heterogeneous spacecraft clusters

The problem of obtaining long term relative orbit configurations for spacecraft clusters with realistic operational considerations such as safety, station keeping and inter-spacecraft distances is addressed. Two different approaches are developed for station keeping and safety objectives. In the first approach, relative orbit configurations, or relative TLEs, are found minimizing deviations from reference mean orbit which would maximize the station-keeping objective. In second one, relative configurations are found from a reference initial condition by minimizing probability of collision, hence maximizing the safety objective, between the spacecraft in the cluster which are propagated numerically through a high precision orbit propagator. For the design optimization, a derivative free algorithm is proposed. Effectiveness of the approaches is demonstrated through simulations. Using this design framework, several configurations can be found by exploring the limits of the clusters in terms of spacecraft number, distance bounds and probabilities of collision for long time intervals.

Spacecraft cluster orbit planning method based on adaptive population mutated pigeon group optimization

The orbit planning problem of spacecraft clusters under complex conditions is a hotspot and a difficult goal in the current aerospace field. This paper studies the orbital optimal programming problem of distributed cluster spacecraft in the process of formation transformation and proposes the population evolution algorithm adaptive population variation pigeon-inspired optimization (APVPIO). Based on the core evolutionary algorithm and evolutionary stagnation, there is a high tendency to fall into the local optimal solution in the classical PIO algorithm. The fitness function of the classical PIO algorithm is studied and improved with the orbit planning problem. Finally, the simulation based on the adaptive population variation algorithm is performed. The results reveal that the APVPIO algorithm has better planning results, deeper population evolution depth, and a faster convergence speed in comparison to the classical PIO algorithm and particle swarm algorithm (PSO) algorithm. Hence, it has the potential to meet the complexity requirement of spacecraft clusters and orbital planning problems.