Spacecraft Simulator Research Articles

Space Docking Simulation Simulator Overview

Background: Docking mechanism ground simulation test technology has been a significant issue in the aerospace industry. Docking mechanisms must pass various conventional evaluation tests as a class of electromechanical space products and other space products. Due to the unique nature of the working object environment in space engineering, it is very expensive to simultaneously simulate the docking work under space conditions and conduct ground reproduction tests, so the test technology of the docking mechanism must be thoroughly investigated. Objective: This paper reviews patents on aircraft ground docking and space environment simulation systems to provide insights and references to scholars and researchers in spacecraft simulator manufacturing. Methods: The representative patents associated with each simulation equipment for space docking simulators, including mechanical docking dynamics equipment, space-integrated environment simulation experimental equipment, and thermal vacuum experimental equipment, are described by analyzing the structural functions of these simulators and elaborating on their operating principles and characteristics. Results: By describing various types of space simulators, the current direction of space docking simulators can be optimized, and their future development direction is summarized and analyzed. Conclusion: The comprehensive environmental reproduction of the space docking simulator facilitates the examination of actual problems and potential flaws in spacecraft and has a significant effect on the advancement of the space field.

Neural-Rendezvous: Provably Robust Guidance and Control to Encounter Interstellar Objects

Interstellar objects (ISOs) are likely representatives of primitive materials invaluable in understanding exoplanetary star systems. Due to their poorly constrained orbits with generally high inclinations and relative velocities, however, exploring ISOs with conventional human-in-the-loop approaches is significantly challenging. This paper presents Neural-Rendezvous—a deep-learning-based guidance and control framework for encountering fast-moving objects, including ISOs, robustly, accurately, and autonomously in real time. It uses pointwise minimum norm tracking control on top of a guidance policy modeled by a spectrally normalized deep neural network, where its hyperparameters are tuned with a loss function directly penalizing the model predictive control state trajectory tracking error. We show that Neural-Rendezvous provides a high probability exponential bound on the expected spacecraft delivery error, the proof of which leverages stochastic incremental stability analysis. In particular, it is used to construct a non-negative function with a supermartingale property, explicitly accounting for the ISO state uncertainty and the local nature of nonlinear state estimation guarantees. In numerical simulations, Neural-Rendezvous is demonstrated to satisfy the expected error bound for 100 ISO candidates. This performance is also empirically validated using our spacecraft simulator and in high-conflict and distributed unmanned aerial vehicle swarm reconfiguration with up to 20 unmanned aerial vehicles.

Reconfigurable Guidance Strategy for Compensating Actuator Faults in Spacecraft Formation Flying

The capacity to keep a desired topology with a requested accuracy plays a significant role in every spacecraft formation-flying operation. These missions can be terminated in case of an unexpected spacecraft fault, preventing the system from returning to its nominal configuration. This paper presents and tests a new recovery solution, called Reconfigurable Guidance Strategy (RGS), for the spacecraft formation-flying control problem subject to a look-in-place permanent thruster fault. The proposed method relies on autonomously and in real-time reconfiguring the guidance function to compensate for the loss of the spacecraft actuation system. The performance and cost of the RGS have been tested in a high-fidelity simulation scenario, the 42 spacecraft simulator developed by NASA Goddard Space Flight Center, taking into account orbital and rotational nonlinear coupled dynamics, high-order perturbation models, and actuator and sensor models. The numerical simulation results have demonstrated the proposed recovery strategy’s effectiveness, feasibility, and robustness.

AstroSLAM: Autonomous monocular navigation in the vicinity of a celestial small body—Theory and experiments

We propose AstroSLAM, a standalone vision-based solution for autonomous online navigation around an unknown celestial target small body. AstroSLAM is predicated on the formulation of the SLAM problem as an incrementally growing factor graph, facilitated by the use of the GTSAM library and the iSAM2 engine. By combining sensor fusion with orbital motion priors, we achieve improved performance over a baseline SLAM solution and outperform state-of-the-art methods predicated on pre-integrated inertial measurement unit factors. We incorporate orbital motion constraints into the factor graph by devising a novel relative dynamics—RelDyn—factor, which links the relative pose of the spacecraft to the problem of predicting trajectories stemming from the motion of the spacecraft in the vicinity of the small body. We demonstrate AstroSLAM’s performance and compare against the state-of-the-art methods using both real legacy mission imagery and trajectory data courtesy of NASA’s Planetary Data System, as well as real in-lab imagery data produced on a 3 degree-of-freedom spacecraft simulator test-bed.

Online tree-based planning for active spacecraft fault estimation and collision avoidance.

Autonomous robots operating in uncertain or hazardous environments subject to state safety constraints must be able to identify and isolate faulty components in a time-optimal manner. When the underlying fault is ambiguous and intertwined with the robot's state estimation, motion plans that discriminate between simultaneous actuator and sensor faults are necessary. However, the coupled fault mode and physical state uncertainty creates a constrained optimization problem that is challenging to solve with existing methods. We combined belief-space tree search, marginalized filtering, and concentration inequalities in our method, safe fault estimation via active sensing tree search (s-FEAST), a planner that actively diagnoses system faults by selecting actions that give the most informative observations while simultaneously enforcing probabilistic state constraints. We justify this approach with theoretical analysis showing s-FEAST's convergence to optimal policies. Using our robotic spacecraft simulator, we experimentally validated s-FEAST by safely and successfully performing fault estimation while on a collision course with a model comet. These results were further validated through extensive numerical simulations demonstrating s-FEAST's performance.

Autonomous Spacecraft Motion Plan Characteristics Influence Perceived Path Appropriateness

Autonomous Rendezvous and Docking maneuvers, performed by two spacecraft joining together in space, are becoming increasingly automated. However, human supervision of the docking spacecraft remains necessary for system failure mitigation, which manifests in manual takeover of the docking spacecraft’s motion control. Previous analysis found that intrinsic characteristics of the motion plans taken by the spacecraft significantly influenced human supervisor manual-takeover performance. This work analyzes how the human supervisor’s observation of the spacecraft’s path traversal compared to the participant’s expectation of path traversal. In this study, participants ( N = 33) monitored a simulation of an autonomous spacecraft attempting to dock at one of two docking stations. Participants were asked about their interpretation of the appropriateness of the path taken by the spacecraft in each trial. Statistically significant relationships were identified between intrinsic characteristics of the motion plan, the human supervisor’s takeover performance, and the human’s interpretation of the path taken.

Reinforcement-Learning-Based Robust Guidance for Asteroid Approaching

This paper presents a reinforcement-learning (RL)-based robust low-thrust guidance method for asteroid approaching under process uncertainties. Markov decision processes with stochastic dynamics are formulated for RL. To overcome the problem of low terminal accuracy in RL-based transfer trajectory design, robust zero-effort-miss/zero-effort-velocity (R-ZEM/ZEV) guidance is proposed. Originally, an eigenvalue-related term is defined according to the stability conditions of the ZEM/ZEV feedback system and chosen as the learning parameter, which can significantly improve the robustness of the agent to process uncertainties under the low-thrust magnitude constraint. Moreover, the navigation performance of the asteroid approaching is modeled via the Fisher information matrix and incorporated in the reward function design, which enables optimizing the optical observation performance together with the propellant cost. Thereafter, the proximal policy optimization is adopted to train an RL agent that can efficiently deal with the uncertainties. The effectiveness and efficiency of the proposed method are validated through simulations of a low-thrust spacecraft approaching the asteroid Bennu. The promising results indicate that the proposed method can not only deal with various uncertainties efficiently and autonomously but can also guarantee high terminal accuracy.

Observer-based HOFA predictive cooperative control for networked multi-agent systems under time-variant communication constraints

This research focuses on a cooperative control problem for networked multi-agent systems (NMASs) under time-variant communication constraints (containing time-variant communication delays and time-variant data losses) in the forward and feedback channels. From the perspective of high-order fully actuated (HOFA) system theory, a HOFA system model is adopted to describe the NMAS, which is called the networked HOFA multi-agent system (NHOFAMAS). Because of complicated working scenarios over the network, the states of NMASs are immeasurable and the communication constraints are always present, such that an observer-based HOFA predictive control (OB-HOFAPC) method is designed to implement the cooperative control when existing the immeasurable states and time-variant communication constraints. In this method, a HOFA observer is established to estimate the immeasurable states for constructing a consensus control protocol. Then, an incremental prediction model (IPM) in a HOFA form is developed via a Diophantine equation to take the place of a reduced-order prediction model. Through this IPM, multi-step output ahead predictions are derived to optimize the cooperative control performance and compensate for time-variant communication constraints in real-time. The depth discussion gives a sufficient and necessary criterion to analyze the simultaneous consensus and stability for closed-loop NHOFAMASs. The capability and advantage of OB-HOFAPC method are illustrated via numerical simulation and experimental verification on a cooperative flying-around task of three air-bearing spacecraft simulators.

SFSNet: An Inherent Feature Segmentation Method for Ground Testing of Spacecraft

In the final approach stage of rendezvous and docking of a spacecraft, the pose parameters of the target spacecraft need to meet docking or berthing capture conditions. Visible light visual measurement systems are increasingly employed in spacecraft ground tests to extract the geometric features of spacecraft to calculate and verify the accuracy of pose parameters. Most current feature-segmentation algorithms are unable to break through the scale transformation problem of spacecraft movement and the noise interference of multi-layer insulation materials in imaging. To overcome these challenges, we propose a novel feature segmentation algorithm based on the framework of deep convolutional neural networks. Firstly, a full convolution model of the encoding-decoding structure is constructed based on data for the ground test. A feature concatenation module is applied and combined with a network backbone to improve the segmentation performance. Then, a comprehensive loss function is presented and optimized by the pose characteristics of the spacecraft in the approach phase. Furthermore, a specific spacecraft simulation dataset to train and test our segmentation model is built through data augmentation. The experimental results verify that the proposed method achieves accurate segmentation of spacecraft of different scales, suppresses the interference caused by multilayer insulation material, and has strong robustness against motion ambiguity. The pixel accuracy of our proposed method reaches 96.5%, and the mean intersection over union is 93.0%.

ROEKF-MPC Estimator for Satellite Attitude and Gyroscope Bias Estimation

Micro-electro-mechanical system (MEMS) based gyroscopes are commonly used for satellite's attitude determination and control system in recent years. They are packaged in a small form factor and have lower power consumption. They provide a low-cost solution to the emerging NewSpace industry. MEMS gyroscopes exhibit time-zero null bias with variation over temperature. To overcome this problem, this paper proposes a technique for self-calibration of gyroscope bias based on reduced order Extended Kalman Filter (EKF) working alongside an estimator based on the model predictive control (MPC) approach. The proposed technique is referred as the ROEKF-MPC Estimator. Both simulation results and experimental verification using an in-house developed spacecraft simulator are presented. Unlike the EKF method, the proposed method can estimate the gyroscope bias instantly and it is robust against changes in temperature. In addition, the proposed method employing a reduced order EKF is 28.4% computationally more efficient than the typical higher order EKF method. Results show that the pointing performance is better than 0.35deg making the proposed method very attractive for most NewSpace applications. While the proposed method allows for self-calibration of gyroscope bias, its performance is affected by the gyroscope noise and requires the satellite's attitude to be nearly in steady state. This limitation will be addressed in detail.

Transverse curvature characterization of rectangular bistable CFRP laminates with a satellite capturing continuum robotic application

The geometry of a bistable unsymmetric cross-ply carbon fiber reinforced polymer (CFRP) laminate impacts the observed stable configuration curvature. Continuum robotic systems can leverage the multi-stable nature of bistable CFRPs to contort to resemble continuous-bodied appendages observed in nature. The compliance of continuum robots facilitates grasping and manipulating objects such as non-cooperative, tumbling targets encountered in an on-orbit servicing operation. This work investigates large-scale bistable CFRP rectangular laminates previously unexplored in the literature to demonstrate the feasibility of scaling the laminates to meet the needs of potential applications. A numerical parametric study explored the relationship between aspect ratio, thickness, and stable configuration curvature. Non-dimensional parameters enabled the creation of a bifurcation diagram to characterize the stable shapes and bistability. Polynomial regression and machine learning delivered curvature prediction tools. Experiments using an air-bearing zero gravity spacecraft simulator evaluated the feasibility of incorporating rectangular bistable CFRP laminates in an on-orbit servicing continuum robotic system. The initial system resembled a crab claw to grasp target boxes, and the orientation and size of the target significantly contributed to the capture success rate. Future investigations of bistable CFRPs, especially from an application perspective, can benefit from predictive tools like ML to quantify the bistable characteristics.

Novel ground microgravity experiment system for a spacecraft-manipulator system based on suspension and air-bearing

With the development of aerospace technology, the on-orbit activities of large spacecraft are increasing day by day, and accordingly the amount of space debris is increasing. Therefore, a large spacecraft-manipulator system is needed to perform on-orbit service, such as, assembling large spacecraft and removing space debris. Before launching, the trajectory optimization and control algorithm of the spacecraft-manipulator system have to be verified on the ground microgravity experiment system since the complex systems are prone to failure. In this study, a ground microgravity experiment system based on suspension and air-bearing is proposed to provide a long-term and stable microgravity environment for the large spacecraft-manipulator system. A suspension gravity compensation system with multiple suspensions is designed for the redundant space manipulator that moves in three dimensions, and a suspension tracking smooth sliding mode control algorithm based on hierarchical control is presented. Then, the coupling-reduction strategy based on equivalent spring-mass is presented to reduce the electromechanical coupling of the suspension gravity compensation system and the space manipulator. For the roll and pitch motion of the large-mass/inertia spacecraft, the target spacecraft simulator with a 2-degree-of-freedom rotating mechanism is designed. Some air-bearings are installed on the base of the chaser spacecraft simulator and target spacecraft simulator, which produce an air gap to enable the frictionless plane motion of the spacecraft. Finally, the target spacecraft simulator transposition experiment was carried out. The results show that the residual torque of the space manipulator is very small, and the ground microgravity experiment system compensates the gravity of the spacecraft-manipulator system with high precision.

Layperson Physiological Tolerance and Operational Performance in Centrifuge-Simulated Spaceflight.

INTRODUCTION: Prior study has indicated that individuals of varied age, medical history, and limited-to-no experience tolerate spaceflight conditions. We sought to expand upon the understanding of layperson response to hypergravity conditions expected in commercial spaceflight by exposing subjects, following minimal training, to centrifuge-simulated, high-fidelity commercial spaceflight profiles. We further explored how these individuals perform in simulated operational activities during and following hypergravity.METHODS: Volunteer subjects participated in up to five centrifuge runs (maximum +4.0 Gz, +4.5 Gx, 6.1 G resultant; onset rate <0.5 Gz · s-1, ≤1 Gx · s-1). Profiles included two winged spacecraft simulations with sequential and combined +Gx/+Gz and two capsule simulations representing nominal +Gx launch and reentry. The final profile simulated a capsule launch abort, with a more dynamic cycling of +Gx exposures and oscillatory multi-axis exposures simulating parachutes and water motion. Touchscreen tablets were used to administer pattern-replication tasks during and after profiles.RESULTS: A total of 46 subjects participated, including 4 diabetics and 9 with cardiac disease. There was increased frequency of motion sickness, subjectively associated with capsule-type profiles, and increased termination of participation compared to prior studies. There was no association between medical history, age, sex, or motion sickness history and tolerance or noncompletion. Tablet test errors were common; accuracy and time to completion were associated with age. There was no association between any time metric or accuracy and sex.DISCUSSION: This study improves understanding of layperson tolerance in commercial spaceflight analog conditions, and the capsular profiles broaden the applicability of the findings. The frequency of task errors highlights the potential for mistakes in operational activities when performed by laypersons.Blue RS, Ong KM, Ray K, Menon A, Mateus J, Auñón-Chancellor S, Shah R, Powers W. Layperson physiological tolerance and operational performance in centrifuge-simulated spaceflight. Aerosp Med Hum Perform. 2023; 94(8):584-595.

Adaptive control for excitation and parameter identification of a three-axis spacecraft simulator with full-state constraints

Adaptive control for excitation and parameter identification of a three-axis spacecraft simulator with full-state constraints

Applied Optimal Control of Spacecraft Simulator Subject to Failures of Reaction Wheels

Applied Optimal Control of Spacecraft Simulator Subject to Failures of Reaction Wheels

Distributed sliding-mode cloud predictive formation control of networked multi-agent systems with application to air-bearing spacecraft simulators

This paper investigates the application of distributed sliding-mode cloud predictive control scheme in networked multi-agent control systems (NMACS). A sliding-mode cloud predictive control (SMCPC) scheme based on the cloud control system is designed. Combining the advantages of variable structure control (VSC) of sliding-mode control(SMC) and the advantages of cloud predictive control (CPC) in handling network delays, the network delay is actively compensated while ensuring the stability and output consistency of the NMACS. Also taking advantage of the small online computation of SMC, reduces the burden of CPC in processing big data and increases the computation rate. In addition, the scheme includes multi-step state prediction and output prediction as well as coordinated optimization among multiple agents. By optimizing the cost function design, the SMCPC scheme is guaranteed to achieve the desired control performance in multi-agent formation control. And the feasibility of the control scheme is verified by stability and consistency analysis. The applicability of the control scheme in practical applications is verified by air-bearing spacecraft simulator (ABS) formation control experiments.

Constrained Cooperative Control for High-Order Fully Actuated Multiagent Systems With Application to Air-Bearing Spacecraft Simulators

This article is concerned with the constrained cooperative control for high-order fully actuated multiagent systems (HOFAMASs) subject to input saturation and prescribed performance. A predictive control scheme is given to achieve constrained cooperation among HOFAMASs. In this scheme, an incremental HOFA (IHOFA) prediction model is constructed by using a Diophantine equation to replace a reduced-order one. A cost function, investigating the cooperative performance and the constraints on input saturation and prescribed performance, is optimized by multistep output predictions so that the optimal constrained cooperative controllers are developed to satisfy the consensus requirement and given constraints. The further discussion gives a condition of stability and consensus, which is convenient to check and extend in practice. Simulated and experimental results of formation control for air-bearing spacecraft simulators are shown to illustrate the effectiveness of the provided scheme.

An output formation consensus control for leader–follower networked multi‐agent systems under communication constraints and switching topologies

AbstractIn this paper, the formation consensus problem for a class of leader–follower networked multi‐agent systems under communication constraints and switching topologies is investigated. A networked predictive control scheme is proposed to achieve stability and output formation consensus with the switching topology, capable of compensating for data loss and time delays in the network. By equating the whole closed‐loop networked multi‐agent system with the proposed control scheme to the corresponding switched system, the sufficient and necessary condition of output formation consensus and stability for agents is given. Finally, using three‐degree‐of‐freedom air‐bearing spacecraft simulators as the control objects, the proposed scheme is demonstrated to be able to actively compensate for the communication constraints through numerical simulations, and it is also verified to have a good control performance by further realizing the formation task of the simulators through practical experiments.

Positioning accuracy estimation of spacecraft simulator based on Monte Carlo and Anderson-Darling test

In aerospace ground test system, positioning accuracy estimation of spacecraft simulator has an important influence on the trajectory planning and obstacle avoidance optimization. In this study, the fusion strategy of multi-laser-ranging-sensors is used to analyse the principle of laser ranging in the positioning of spacecraft simulator. On this basis, effect of measurement error on the positioning accuracy of spacecraft simulator is analysed, and theoretical derivation are performed. In order to accurately estimate the positioning accuracy, the algorithm based on Monte Carlo and Anderson-Darling test are introduced. Finally, simulations and experiments were conducted for the above algorithm. The result shows that the fusion strategy of multi-laser ranging sensors is reasonable. Compared with traditional positioning and accuracy estimation methods of spacecraft simulator, the proposed method is more precise for the accuracy estimate and is more convenient for engineers to use.

Predictive control of discrete-time high-order fully actuated systems with application to air-bearing spacecraft simulator

This paper addresses an output tracking problem for discrete-time high-order fully actuated (DHOFA) systems and its application in the control of air-bearing spacecraft (ABS) simulator. A HOFA system model, as a novel system representation, is applied to establish the dynamics of discrete-time control systems. Accordingly, a HOFA predictive control scheme is presented to deal with this problem, which is composed of a HOFA feedback for stabilization and a HOFA predictive control for tracking. In this scheme, a Diophantine equation is exploited to construct an incremental HOFA (IHOFA) prediction model to substitute a reduced-order prediction model, and then a cost function involving tracking performance is minimized by using multi-step output predictions. A sufficient and necessary condition is proposed to discuss the stability and tracking performance of the closed-loop DHOFA systems, it is simple to utilize in system analysis and extend in practice. Two experiments of the control of ABS simulator are shown to illustrate the feasibility of the presented HOFA predictive control scheme.