Satellite Attitude Research Articles

Probabilistic Analysis of Orbital Characteristics of Rotary Systems with Centrally and Off-Center Mounted Unbalanced Disks

Rotor dynamics plays a crucial role in the performance and safety of rotating machinery, with disk position and unbalance significantly impacting system behavior. This study investigates the dynamic characteristics of two rotor configurations: a centrally mounted unbalanced disk (Rotor05un) and an off-center unbalanced disk (Rotor025un). Using numerical simulations and Monte Carlo analysis, we examined critical speeds and orbital patterns for both configurations. Probability distributions of shaft orbital positions revealed distinct patterns for each configuration. Quantile analysis revealed approximate linear trends for Rotor025un, suggesting higher system stiffness and more predictable behavior near critical speeds. Cross-sectional analyses of the orbits provided insights into the complex interactions between disk position, gyroscopic effects, and system natural frequencies. These findings provide valuable insights for rotor system design, particularly for applications with non-ideal mass distributions. The study goes beyond traditional critical speed analysis to examine orbital patterns and point on orbit occurrence from a probabilistic perspective. Based on the simulation of the orbits, an orbital is determined that allows the probability of the shaft occurring at the analyzed distance from the origin to be determined. The paper also offers insights into the complex interaction behavior of chosen rotor configurations and highlights the importance of considering disk position in predicting and optimizing rotor dynamic behavior, contributing to the development of more robust and efficient rotating machinery.

PDE-based anti-disturbance attitude and vibration control of flexible satellite under output constraints

In this paper, an anti-disturbance control strategy is exploited for the attitude reorientation and vibration damping of flexible satellite under disturbances and output constraints. The flexible satellite is formulated by partial differential equations (PDEs) and the exploited controller extends the existing results from the following two aspects. (1) The exploited controller is capable of handling the time-varying output constraints by incorporating the barrier Lyapunov function (BLF) with a coupling-based item through Lyapunov analysis. (2) The exploited controller possesses the excellent disturbance rejection property by introducing two disturbance observers to separately identify the disturbance torque and force. The asymptotic stability of the closed-loop system is strictly evaluated. The exploited controller can restrain the attitude reorientation error and the tip deformation always in the predefined output constraints. Lastly, comparative simulations validate and highlight the main results.

Orbital Precession in Janis–Newman–Winicour Spacetime

We have investigated the Janis–Newman–Winicour spacetime through three fundamental tests of theories of gravity, namely, gravitational lensing, perihelion shift, and redshift due to gravitational force. Focusing initially on the circular motion of a massive particle within the equatorial plane, the analysis disregards external scalar field interactions. The Janis–Newman–Winicour (JNW) spacetime’s unique parameters, mass (M) and the scalar parameter (n), are examined, revealing an intriguing relationship between the innermost stable circular orbit position of the test particle and the scalar field parameter. The study also explores photon motion around a gravitational object in JNW spacetime, revealing the expansion of the photon sphere alongside a diminishing shadow, influenced by the external scalar field. Despite these complexities, gravitational bending of light remains consistent with general relativity predictions. The investigation extends to perihelion precession, where the trajectory of a massive particle in JNW spacetime exhibits eccentricity-dependent shifts, distinguishing it from Schwarzschild spacetime. Finally, oscillatory motion of massive particles in JNW spacetime is explored, providing analytical expressions for epicyclic frequencies using perturbation methods. The study concludes with the application of MCMC analyses to constrain the JNW spacetime parameters based on observational data.

Study on the detection of power line operation anomalies in the Hexi Corridor based on multi-scale filtering of BeiDou satellite signals

In the artificial intelligence detection of power line anomalies, the identification effect is determined by the positioning process of two-dimensional map, which is also a difficult point in this field Therefore, this paper proposes a method to detect the abnormal operation of power lines in Hexi Corridor based on multi-scale filtering of Beidou satellite signals. Multi-scale processing is carried out by wavelet transform to obtain multiple wavelet basis functions of different scales. On this basis, a two-dimensional time–frequency diagram is drawn according to the results of continuous wavelet transform. The adaptive multi-time frequency entropy method is selected to divide the two-dimensional time–frequency map into multiple layers and scales, and the adaptive multi-scale time–frequency entropy of the time–frequency plane is calculated. After smoothing the obtained power line position signal by S-G filtering method, the power line position signal of Hexi Corridor is obtained by GPS/ Beidou dual-mode satellite positioning system, and the positioning signal is optimized by using the positioning mechanism of multiple receiving stations. Realize the abnormal operation detection of power lines in Hexi Corridor. The experimental results show that this method can obtain more accurate positioning signals, and the geometric accuracy factor GDOP fluctuates in the range of 0 ∼ 2. The unsmooth signal can be effectively removed; The maximum error result of abnormal position detection is 1.22kn；; At the same time, the abnormal displacement section of the line can be determined.

Research on Satellite Navigation Control of Six‐Crawler Machinery Based on Fuzzy PID Algorithm

ABSTRACTThe six‐crawler driving mechanism plays a crucial role in the operation of large machines such as bucket‐wheel excavators, dumping machines, and mobile crushing stations, as it serves functions like bearing, movement and steering. The driving characteristics of this mechanism directly influence the safety and efficiency of these machinery systems. To enhance the design methodology for multi‐crawler machinery, improve path controllability, and achieve adaptive driving, a satellite navigation control system for six‐crawler machinery was developed based on the principles of real‐time kinematic (RTK) satellite positioning. This system utilizes the distance deviation and heading angle deviation between the actual path and the predetermined path of the six‐crawler machinery as inputs to a fuzzy proportion integration differentiation (fuzzy PID) controller. This controller regulates the deviation angle of the steering crawler and the driving speeds of each track, thereby ensuring precise path tracking control. To evaluate the path tracking control performance under both straight and curved driving conditions, a virtual prototype model of the six‐crawler mechanical system was established, and co‐simulation analysis was conducted. In addition, an experimental platform for path tracking control of six‐crawler machinery was established to validate the efficacy of the satellite navigation system. The actual tracking data obtained from various driving conditions and initial deviations demonstrated that the RTK satellite navigation path tracking control system exhibited excellent control performance.

Exploring a novel feature of ellis spacetime: Insights into scalar field dynamics

We have studied neutral and charged massive particles dynamics in Ellis spacetime in the presence of the external scalar field. Focusing on the circular motion of massive particles, the impact of an external scalar field on the Innermost Stable Circular Orbit (ISCO) position is analyzed, revealing a non-linear relationship with the scalar field parameter. Perturbation techniques are employed to investigate oscillatory motion near stable orbits in the Ellis spacetime, yielding analytical expressions for radial and angular oscillations. The throat of the wormhole has been constrained by comparing theoretical and observational results for fundamental frequencies of particles from quasars. Finally, scalar and gravitational perturbations in the Ellis spacetime have been studied. It is shown that the equation for the scalar profile function is fully independent from the tensor functions and the solution can be represented in terms of the confluent Heun function. However, it has been shown that equations for the tensor profile functions strongly depend on the scalar profile functions in the Ellis spacetime and they are reduced to the Regge–Wheeler–Zerilli equation. Finally, numerical solutions to the Regge–Wheeler–Zerilli equation for the radial functions in the Ellis have been presented.

Impact of proton radiation and annealing of CMOS image sensors on star sensor performance

Star sensors play a crucial role as high-precision optical attitude navigation devices in satellite attitude control systems. The CMOS image sensor is an important component of the imaging system of the star sensor and experiences performance degradation due to the total ionizing dose effect and displacement damage effect in the radiation environment of its long-term operational workspace. As a result, the detection capability and attitude positioning accuracy of the star sensor gradually deteriorate. The performance of CMOS image sensors can be partially restored after annealing but the impact on the performance of star sensors is unclear. This study conducted 100 MeV proton irradiation experiments with different fluences on CMOS image sensors and performed room temperature and high temperature annealing treatments. By testing and analyzing the performance of star sensors equipped with irradiated and different annealing treatments on CMOS image sensors, the impact mechanism of CMOS image sensor radiation damage and annealing recovery on the performance variation of star sensors was obtained. This study provides theoretical support for reliability of long-term in-orbit star sensors.

Detection and Analysis of Anomalous Behavior in On-Orbit Satellites Using AI Algorithms

The increasing deployment of satellites for essential applications necessitates robust anomaly detection to maintain their operational integrity. Traditional methods, which depend on manual monitoring and predefined thresholds, often prove inadequate in the complex space environment. This paper investigates the application of Artificial Intelligence (AI) algorithms to improve the detection and analysis of anomalous behavior in on-orbit satellites. AI, especially through machine learning (ML) and deep learning (DL), provides advanced capabilities for processing extensive telemetry data and identifying intricate patterns. By leveraging historical data, AI systems can establish normal operational parameters and detect deviations indicating potential anomalies. Techniques such as supervised and unsupervised learning are employed to develop models with high predictive accuracy. Furthermore, AI facilitates root cause analysis by correlating anomalies with operational conditions or external factors, enabling effective corrective measures. The integration of AI also promotes autonomous satellite operations, which are crucial for deep-space missions. This advancement enhances satellite reliability and safety, supporting sustainable and progressive space exploration. In this research, machine learning algorithms were employed to develop the proposed anomaly detection system. The system aims to detect subtle failures in the spacecraft’s attitude dynamics system, particularly in the reaction wheel subsystem, by learning solely from the spacecraft's nominal behavioral data. The system was developed from a small satellite's attitude dynamics control system, which may exhibit bearing failures in the reaction wheels. Two types of anomaly detection systems were introduced: a two-sided learning anomaly detection system and a one-sided learning anomaly detection system. For this study, a two-sided learning anomaly detection system was developed using the Logistic Regression (LR) method. This provided a foundation for the training process using a machine learning approach. By learning from both nominal and failure behaviors of the satellite, the system was designed to detect small reaction wheel friction failures effectively.

The use of geodetic measurements in the assessment of the technical condition of road pavement – case study

Abstract Technical infrastructure is exposed to environmental impacts throughout its entire life cycle. For linear objects, such as roads, additional impacts resulting from their continuous use – vehicle traffic – are distinguished. Due to their communication function, these facilities are subject to ongoing condition monitoring. This procedure allows to identify any damage and indicate appropriate protective measures. Observations of the terrain as well as the road surface can be carried out using commonly used geodetic measurements. Cyclic monitoring applies especially to facilities in mining areas, where impacts in the form of continuous and discontinuous deformations significantly contribute to their damage. The research presents the results of selected geodetic measurements made on a deformed road surface affected by underground mining. The aim of the observations was to use both classic and modern measurement techniques to determine, among others, the shape of the surface or identify damage. The applied measurements included commonly used total station measurements and GNSS satellite positioning. Additionally, low-ceiling photogrammetry using an unmanned aerial vehicle and laser scanning were used to illustrate the shape of the road surface. Finally, a comparison was made of selected methods; their advantages and disadvantages allowed us to determine their usefulness in monitoring the condition of the pavement.

Development of a low-power Hall thruster with permanent magnets and a dual trigger electrode hollow cathode for the Qilu satellite constellation

A low-power Hall effect thruster (LpHet-100) has been installed on the Qilu-3 satellite and has been launched by the Long March 2D rocket in January 2023. The specific features of LpHet-100 are presented in this article. An innovative dual trigger electrode poison-resistant hollow cathode and an effective magnetic shielding configuration with permanent magnets, additive manufacturing gas distributor are designed and experimentally verified. The prominent characteristics of a shorter starting time, lower ignition shock, and self-healing have been shown in tests of the dual trigger electrode hollow cathode. Thrust performance tests proved that LpHet-100 can work stably in the power range of 50–230 W and generate a thrust of 2.9–10.8 mN, with the maximum specific impulse of 1594 s and maximum thrust efficiency of 38 %. Rigorous environmental tests were conducted to verify the thruster design scheme. In addition, Torque measurement of Hall thrusters is incorporated into the ground tests for the first time. A plasma torque of 5.65–9.88 μN·m is obtained when LpHet-100 operated in 132–228 W. It provides a critical guideline for the attitude and orbit control schemes of the Qilu-3 satellite, which is an essential part of space mission planning. Generally, this study proposes new design of the thruster body and hollow cathode, providing an exhaustive reference for the development process and application of low-power Hall thrusters.

Deep Koopman-operator-based model predictive control for free-floating space robots with disturbance observer

This paper investigates the optimal tracking control problem of free-floating space robots in the presence of non-ideal factors, such as model uncertainties and external disturbances. To address this issue, we first leverage the deep Koopman operator, facilitated with a deep neural network, to establish an offline formulation of a global linearization model for a micro-nano free-floating space robot. Based on the estimated linearization model, we then employ the model predictive control method for online optimization control, achieving a significantly reduced computational burden. Additionally, a decay factor is integrated into the model predictive control optimization objective function to balance the precision of joint angles tracking with the suppression of base satellite attitude disturbance. To further address the modeling inaccuracies and external disturbances, we incorporate a disturbance observer based on a radial basis function neural network for online compensation within the model predictive control framework. This augmentation enhances tracking precision and robustness. Several groups of simulation results are carried out to demonstrate the effectiveness of the proposed method, showing its capability of energy consumption, disturbance rejection and enhanced robustness. These highlight the potential of the proposed method to improve the control performance of free-floating space robots, even in the absence of a precise dynamic model.

Optimal Synthesis of a Satellite Attitude Control System under Constraints on Control Torques and Velocities of Reaction Wheels

The problem of optimal synthesis of a nonlinear system’s control law parameters of satellite attitude control under constraints is considered. The maximum stability degree of a system is considered as an optimality criterion. The reaction wheels’ control moments and angular velocities achievable within the drives’ physical characteristics are considered as constraints. A linear model of the system converted from the original nonlinear model was used to solve the problem. The decomposition method, based on the transition from real time to relative time, is used to separate the tasks of optimality criterion maximization and constraints consideration. A method of synthesizing the optimal form of system transient process according to the maximum stability degree criterion is developed. An analytical method for determining the optimal values of control law parameters is proposed, based on the structural features of the linear differential equations system. A method that takes into account constraints on control moments and angular velocities of reaction wheels, based on transition scale from relative time to real time and its calculation algorithm, ensuring that conditions of all constraints are met, is developed. An example of solving the problem of optimal system synthesis is considered. The results demonstrate the effectiveness and simplicity of the proposed methods and algorithm for targeted selection of a system’s technical characteristics, taking into account constraints on reaction wheels’ maximum angular velocities and control moments values.

Lignin regulating the active sites and peroxymonosulfate activation capacities of iron incorporated 1D carbon nanofiber for efficient organic pollutant degradation

In this study, 1D iron incorporated carbon nanofiber composite catalysts (PLCF-Fe) were prepared by pyrolyzing iron nitrate embedded polyacrylonitrile/lignin electrospun nanofiber. PLCF-Fe exhibited prominent peroxymonosulfate (PMS) activation capability for organic pollutant degradation, in which 1D structured carbon nanofiber effectively inhibited the aggregation of iron moieties. The introduction of lignin could modulate the oxidation capacities of the prepared PLCF-Fe through regulating both the graphitic degree as well as the oxygen-containing group distributions of the carbon nanofiber, and the crystalline phase composition of the iron compounds in PLCF-Fe. The existence of multiple active sites could concertedly trigger radical and non-radical PMS activation for high-efficient pollutant degradations, during which process negligible iron ions were leached. It was also found that pollutant degradation rate in PLCF-Fe/PMS system was correlated well with their highest occupied molecular orbital positions. Additionally, PLCF-Fe/PMS system could maintain conspicuous catalytic performance in a variety of water matrices with wide pH application range. This study provides an applicable structure and active sites regulating approach to prepare high-efficiency heterogeneous catalyst for PMS based Fenton-like organic decontamination processes.

Orbital Soft Tissue Position after Globe-Sparing Maxillectomy and Inferior Orbital Rim Reconstruction: A Pilot Study.

Orbital Soft Tissue Position after Globe-Sparing Maxillectomy and Inferior Orbital Rim Reconstruction: A Pilot Study.

Reinforcement learning-based satellite formation attitude control under multi-constraint

As the complexity of space missions increases, the constraints on satellite attitude control become more stringent, particularly for satellites working in orbit formation. This paper introduces a novel method, based on the categorization and modeling of different constraints, for attitude control of satellite formations under multiple constraints. The method employs the Phased Priority Reinforcement Learning (PPRL) approach, which utilizes Deep Deterministic Policy Gradient (DDPG) technology. Considering the complexity of constraints and the challenge posed by the high control dimensionality due to multi-satellite coordination, the method addresses these challenges through a two-step training strategy. The first step addresses the multi-constraint issue for individual satellites and increases the priority of single-satellite training experience data in the experience replay buffer of the second step to enhance data utilization efficiency. To address the issue of reward sparsity in complex high-dimensional constraint models, a detailed reward mechanism is proposed, incorporating both local and global constraints into the reward function, thereby achieving both efficient and effective attitude control. This approach not only meets dynamic, state, and performance constraints but also demonstrates adaptability and robustness through numerical simulations. Compared to traditional methods, this approach achieves significant improvements in control performance and constraint satisfaction, offering a novel solution pathway for high-dimensional control problems in multi-constraint satellite formations.

Characterization and verification of the optimal feedback gain of a satellite magnetorquer-based attitude control system

In spacecraft mission planning and operation, the attitude determination and control subsystem (ADCS) of a satellite provides information about the orientation of the satellite in the inertial reference frame. Furthermore, this subsystem produces the control actions required to adjust the orientation of the satellite, especially in the low-Earth orbit (LEO) regime. This paper focuses on the satellite’s three-axis attitude control problem within the context of active and passive control, which includes detumbling control, pointing control, magnetic control, and attitude stabilization after solar panel wing deployment using magnetorquers as the primary actuators. The objective is to stabilize and reduce the angular rate while orienting the satellite to the desired attitude. The proposed satellite attitude control system (ACS) strategies are designed, developed, characterized, and verified. These strategies encompass the B-dot control algorithm for detumbling control along with pointing control and attitude stabilization after solar panel wing deployment. hardware-in-the-loop simulation (HiLs) tests are conducted to assess the performance of the satellite magnetorquer-based ACS in the presence of noise. These tests involve a relative Earth’s magnetic field (EMF) generator in conjunction with SGP-4-based satellite orbital propagator high-level control software. Additionally, cascade proportional-integral-derivative (PID) and state-dependent Riccati equation (SDRE) controllers are implemented to generate sufficient torque using three-axis magnetorquers on a frictionless air-bearing platform. The platform is balanced to closely simulate the dynamic motion of a spacecraft in space. The testing includes a single initial condition and three inertia conditions for stabilization after solar panel wing deployment. Finally, the effectiveness of the cosimulation as a primary experiment through an integrated HiLs process is validated. This comprehensive approach confirms the control system’s performance and its ability to meet mission requirements.

A Novel Method of 3D Lyapunov Guidance Vector Field to Avoid Intercepting Satellite Based on Reinforcement Learning

This paper proposes a new 3D Lyapunov guidance vector field(3D-LGV) avoidance strategy based on reinforcement learning for the satellite evasion and interception problem. Combining it with the interfered fluid dynamical system (IFDS) enables the satellite to evade and smoothly enter orbit according to the state of the intercepting satellite in real time. 3D-LGV provides an initial flow field approaching an elliptical orbit, while IFDS provides a perturbed flow field based on the intercepting satellite position. The combined potential field of the initial flow field and the disturbed flow field is the planned velocity direction of the satellite. As a decision-making layer, the proximal policy optimization (PPO) dynamically adjusts the perturbed flow field in the IFDS to increase the avoidance success rate in different scenarios. The experimental results show that, compared with the particle swarm optimization with rolling horizon control algorithm, the algorithm proposed in this paper has a shorter decision time and a higher avoidance success rate. At the same time, Monte Carlo simulation shows that the evasion success rate of the proposed algorithm reaches 98%.

Deep learning based fuzzy-MPC controller for satellite combined energy and attitude control system

Combined Energy and Attitude Control System (CEACS) reduces the size and mass budgets of typical satellites and consequently, increases their payload capacity. CEACS uses flywheels for a dual purpose, i.e., as both energy storage and attitude control device. This maiden work attempts to introduce a novel Deep-Learning capability of the fuzzy-Model Predictive Control (FMPC) controller for CEACS. The design approach for the fuzzy-MPC controller uses the Takagi-Sugeno (T-S) fuzzy model of satellite attitudes and computes the control torque through a parallel distribution compensation (PDC) approach. However, the MPC controller offers a high computational burden, and it becomes a significant problem for smaller satellites having limited computational power. Therefore, in this research work, a novel Deep-Learning-based fuzzy-MPC controller (D-FMPC) is designed for the CEACS attitude regulation subject to higher initial angles, actuator constraints, parametric uncertainties, and external disturbance torques. Here, the deep-layer neural network is trained offline with the MPC controller data to replicate the FMPC controller, thus ensuring its controllability. Numerical results validate that the D-FMPC controller successfully mimics the FMPC controller and produces the desired pointing accuracy effectively with smooth transient response and without violating the attitude control actuator constraints. The results also validate that the D-FMPC controller offers significantly reduced computational burden than the FMPC controller. Therefore, the novel Deep-Learning solution provides a feasible platform for applying more complicated and sophisticated attitude control techniques for the CEACS attitude regulation in small satellites as an example.

Observations of Five Major Uranian Satellites during 2013–2020 Based on Gaia DR3

A total of 10,339 positions of the five major Uranian satellites (Ariel, Umbriel, Titania, Oberon and Miranda) were collected at the Yunnan Observatories from 2013 to 2020 with the 1.0 m astrometric reflector. A median filtering method was applied to reduce the influence of the bright Uranus halo. All CCD observational data was analyzed by the newly developed CCD image-processing software with Gaia DR3 as the reference catalog. The impact of different exposure times on satellite positioning accuracy was evaluated. The observational positions of satellites were compared with theoretical positions from Institut de Mécanique Céleste et de Calcul des Éphémérides (DE441 and Lainey et al. 2015) and the mean offsets are from −0.″115 to 0.″103 in both R.A. and decl. for the five satellites. The standard deviation of the offsets are from 0.″008 to 0.″086 for the four greatest Uranian satellites and from 0.″037 to 0.″132 for Miranda in each direction. These precise observations of the five major Uranian satellites over an 8 yr period will be very useful for improving the orbital parameters of both Uranus and its five major satellites.

Adaptive satellite attitude control for varying masses using deep reinforcement learning.

Traditional spacecraft attitude control often relies heavily on the dimension and mass information of the spacecraft. In active debris removal scenarios, these characteristics cannot be known beforehand because the debris can take any shape or mass. Additionally, it is not possible to measure the mass of the combined system of satellite and debris object in orbit. Therefore, it is crucial to develop an adaptive satellite attitude control that can extract mass information about the satellite system from other measurements. The authors propose using deep reinforcement learning (DRL) algorithms, employing stacked observations to handle widely varying masses. The satellite is simulated in Basilisk software, and the control performance is assessed using Monte Carlo simulations. The results demonstrate the benefits of DRL with stacked observations compared to a classical proportional-integral-derivative (PID) controller for the spacecraft attitude control. The algorithm is able to adapt, especially in scenarios with changing physical properties.