Deep learning methods have achieved great success in tasks like image classification, object detection and fault diagnosis. However, practical limitations often prevent gathering large amounts of data. Hence, there is a recent focus on algorithms for learning with small samples. This review aims to explain popular small sample learning methods and how they perform in real world applications. The review covers different approaches like metric models, memory models, parameter updating models and sample augmentation models, discussing their pros and cons. It also explores how these methods are applied in tasks like image classification, object detection, semantic segmentation and fault diagnosis. Lastly, it discusses the limitations of small sample learning methods and predicts future research trends focusing on less data dependency, more efficient algorithms and robust models.

This paper investigates the impulsive control of spacecraft for close proximity approach and maintenance along the sunlight. The concept of approach along the sunlight means that a spacecraft approaches a target, with its position kept on the line connecting the target and the Sun. Firstly, the close proximity relative dynamics is established between the spacecraft and the target. Then the geometrical definition of the sunlight corridor is presented. By transforming the path constraint to the position constraint, a nonlinear programming (NLP) model is developed to describe the optimal impulsive control for the approaching. The time constrained fuel optimal strategy is obtained through solving the NLP model. In addition, the long term variations of the line connecting the target and the Sun are analyzed in the target’s local vertical local horizontal (LVLH) coordinate frame. And an impulsive control strategy is proposed for position maintenance along the sunlight, which is based on the sampling of impulse epochs and positions as well as the dynamical fitting technique. Finally, numerical simulations are conducted to verify the proposed control strategies. The results indicate that the spacecraft can successfully conduct the approach and maintenance, with the sunlight corridor constraint satisfied.

Target detection related technology has been widely used in space target surveillance, satellite automatic orbit finding and other fields. It is also one of the most important and challenging research branches in the field of computer vision and has gradually become a hot research area in the military field at home and abroad. In the modern air space confrontation, the acquisition of near earth vehicle targets through satellite remote sensing images can quickly judge the effective strength of the enemy forces, which enables our troops to occupy a strategic advantage. Aiming at the problems such as too long satellite observation distance and complex background of remote sensing image, the small sample target detection algorithm is studied based on one stage light weighted network YOLOv8. The research work of this paper mainly includes three aspects. Firstly, the generalization performance of the model is improved by image enhancement methods such as image flipping, Mosaic data enhancement and mixup data enhancement. Secondly, the average accuracy of the model is improved by adjusting the optimization function, reducing the class loss gain and reducing the mask ratio. Thirdly, the computational efficiency of the model is improved via the preset parameter and loading the model derived from the original optimization function. The method proposed in this paper is verified on the public aircraft data set, and the verification indexes include precise recall (PR), average accuracy (mAP) and the number of frames per second (FPS). The results show that the improved network model proposed in this paper can meet the needs of fast target detection in satellite remote sensing images.

The large space structure in the assembly process has the characteristics of discrete increment, and its configuration and parameters are constantly change. Different from space structure with fixed configuration, there is currently no in depth research on how to reasonably configure actuators to suppress structural vibration during on orbit assembly process. Hence, the optimal configuration of the actuators in the assembly stage of the large space structure is different from that in the on orbit operation stage. Aiming at the problem, a distributed optimal configuration method of large space structure actuators in the assembly process is proposed in this paper. Considering the characteristics of large space structure which are assembled by modular substructures, the design concept for the actuator distributed optimal configuration in the assembly stage of large space structure is first given. Secondly, the dynamic model of truss structure for actuator placement optimization in assembly process is developed, and the objective function for optimal configuration is developed. The hybrid particle swarm optimization algorithm is improved by combing with the beetle antennae search algorithm; Finally, a numerical example of the position optimization of the actuators of the space smart truss structure in the assembly process is given. The results show that the distributed optimal configuration of actuators in the assembly process of space truss structures is realized by the improved hybrid particle swarm optimization algorithm.

To solve the problem of trajectory tracking control for free floating space manipulator with model uncertainties and external disturbances, a neural network based adaptive sliding mode controller is proposed. To address the model uncertainty, the bidirectional long short term memory neural network (Bi-LSTM) is employed to estimate the uncertainty of the space manipulator model through offline learning. The adaptive sliding mode control is adopted to handle the estimation errors of neural network and external disturbances. The stability of the proposed controller is analyzed via Lyapunov theory. Numerical simulations verify the effectiveness of the proposed control strategy. The results show that the proposed novel controller can effectively improve the control performance at low gains.

Event cameras, a novel type of neuromorphic visual sensor, offer the high temporal resolution and dynamic range, making them suitable for use in computer vision and robotics. This article addresses the challenges of high measurement delay and large data volume in traditional visual systems used in space manipulators by exploring perception and control methods based on event cameras. Asynchronous event stream data generated by event cameras differs from that of traditional cameras, necessitating new algorithms designed specifically for event data. In this paper, a fast circular feature detection and tracking algorithm using iterative reweighting fitting is designed. Building upon this algorithm, a visual servo method adapted to circular features for manipulators is proposed. Experimental results demonstrate that the proposed detection and tracking algorithm achieves a high success rate and significantly faster detection speeds than traditional algorithms. Meanwhile, the event camera’s highspeed feature feedback facilitates more precise servo motion of the manipulators.

Robot learning algorithms have promoted the development of motion planning and control, and a key issue in robot learning is how to building a high performance robot physics engine. Due to the special environment of spacecrafts, characterized by limited sample data and costly experimental conditions, this paper presents a high fidelity simulation environment for spacecrafts with robot learning algorithms. Adhering to the standard Gym framework, the simulation environment supports a variety of mainstream robot learning algorithm libraries and Gym style control/learning algorithms. Utilizing experimental data from the microgravity simulation system, a data driven approach is employed to construct a spacecraft dynamics model for state updates within the simulation environment. As an illustrative example, the mainstream reinforcement learning algorithm Soft Actor Critic is trained and tested in the constructed simulation environment for the spacecraft stabilization task, demonstrating the feasibility of the simulation environment for robot learning algorithm.

In this paper, we improve the deep deterministic policy gradient algorithm and combine the graph attention network to propose a spacecraft fault diagnosis method. Based on the construction of spacecraft system level and component level knowledge graphs, a unique reward function, policy network and value network are set up according to the structure of spacecraft knowledge graphs and the semantic configuration of reinforcement learning environment. Based on the construction of spacecraft system level and component level knowledge graphs, unique reward functions, environments, policy networks and value networks are set according to the structure and semantics of spacecraft knowledge graphs. We use in orbit data for experimental validation, and the experimental results show that the method can combine systemlevel knowledge graph with component level knowledge graph for hierarchical, fast and accurate fault diagnosis.

Harmonic drive systems are widely used in joints of space manipulators because of their low relative quality, compact structure and low costs. However, dynamic characteristics, including kinematic error, stiffness, and friction of harmonic drive systems, hinder the performance improvements of space manipulators, such as pointing accuracy and stability. To overcome this problem, scholars research the experiments, analyses and models on the dynamic characteristics of harmonic drive systems. This paper reviews studies about kinematic error, stiffness and friction. Some research topics are presented.

Anomaly detection of spacecraft telemetry data is a key technology to identify the status of spacecraft and ensure the safe and reliable operation of spacecraft. However, anomaly detection of spacecraft telemetry data usually faces problems such as large dimensionality of time series data, unbalanced anomalies, and lack of labeled samples. In response to these problems, a deep anomaly detection model is proposed based on the idea of anomaly detection. Specifically, according to the strong temporal correlation of telemetry data, a long short term memory network with an attention mechanism is used to establish a telemetry data prediction model. At the same time, in order to overcome the problem of few abnormal labels and high data dimensions of spacecraft telemetry data, a fine tuning transfer learning method is used to optimize the prediction model, and a fully connected layer is used to unify the dimensions of different data sets, by which the accuracy of the transfer learning model and the capacity for anomaly detection are improved. Two spacecraft data sets released by NASA are taken as the experimental object, and the proposed anomaly detection method is used to identify the abnormal state of the data set. The results show that compared with the classic anomaly detection algorithm, the introduction of transfer learning can significantly improve the performance of the model. The experimental results are better than the current common anomaly detection models, which proves the effectiveness of the method.

With the development of information technology and sensor technology, data driven fault diagnosis technology is one of the key technologies to ensure the efficient and safe operation of large industrial equipment. Due to its powerful feature representation ability and the advantages of feature extraction based on big data, machine learning has become one of the most commonly used feature extraction methods in the field of fault diagnosis. However, the data collected by the monitoring equipment includes a large amount of unlabeled data, and the traditional deep neural network model does not make full use of it, resulting in the waste of some useful information. For unlabeled data, we adopt the idea of unsupervised learning, train a feature extraction model by maximizing mutual information, and on this basis, we design a fault diagnosis method for time series data, and verify it on the public dataset Case Western Reserve University bearing dataset, achieving higher diagnostic accuracy than previous traditional methods. Further verification on satellite monitoring data, our feature extraction model can distinguish different stages of failure and capture the data characteristics of different stages. The results show that the fault diagnosis method based on unsupervised learning proposed in this paper can effectively and fully utilize a large amount of unlabeled data and improve the fault diagnosis accuracy of time series data.

In order to reduce the number of jet unloading of satellites in orbit, save propellant and prolong the service life of satellites, the angular momentum management of zero momentum satellites and V-wheel bias momentum satellites in geostationary orbit is researched. According to the characteristics of the satellite angular momentum evolution, the angular momentum management methods for two types of satellites are presented, and the optimized jet unloading strategy is proposed. Based on the above research, theoretical analysis and simulation verification of abnormal unloading of in orbit satellites are carried out, and solutions are proposed. In orbit experiments show that the optimized jet unloading strategy can significantly reduce the number of satellite unloading, which verifies the correctness of the strategy.

Accurately predicting the remaining useful life (RUL) of lithium batteries plays an important role in understanding their health and managing spare parts resources. Most of the existing lithium battery remaining life prediction methods are limited to the prediction results based on the number of cycles. It is essentially a method oriented to a single time scale, ignoring the practical problem that the health state of lithium batteries is affected by the dual time scales of cycle times and working time. In view of this, this article proposes a lithium battery RUL prediction model based on Transformer under the dual time scales. This method selects the capacity as a key index to characterize its performance degradation. The battery capacity data is processed to obtain training sets and test sets through Kalman filtering and sliding time window. The life information contained in the dual time scales, and fully consider the interrelationship between the life information of different time scale, further, establish a mapping relationship between the capacity and the dual time scales, so as to realize the accurate prediction of the RUL of the lithium battery at the dual time scale. Finally, the effectiveness and potential application value of the proposed method are verified by lithium battery examples.

The dynamics of a linear tethered system with three satellites in nontypical plane with orthogonal control force is studied in this paper. An approximate but useful model for a high dimensional nonlinear system is established in the non inertial frame, where three satellites and two space tethers are deemed to be particles and massless springs, respectively. The analytical relationship between the initial state and dynamics and its influence on the critical state of dynamic behavior are derived. A three dimensional dynamic parameter domain is proposed to demonstrate the dynamics in any orbital plane, and verified by numerical simulation. The results show that the analytical calculation is completely consistent with the simulation.

The increasing number of space debris has led to frequent collisions among orbiting spacecrafts, posing a huge obstacle to human space exploration. The current distribution of space debris is analyzed with the aim of fewer collision events occurring in areas with dense distribution of space debris. The Density Based Spatial Clustering of Applications with Noise (DBSCAN) clustering algorithm is used to cluster debris based on the fuel consumption required between debris as feature information, and the core objects of debris are determined by the number of neighboring debris that meet the fuel consumption requirements. Satellites that release space vehicles to the vicinity of each debris are selected based on the fuel consumption required, which is equal to the number of clusters. The Hungarian algorithm is used to assign satellite debris cluster clearing tasks, and spacecraft can implement one to many core object clearing tasks in specific clusters.

With the development of space activities in various countries, the number of on orbit failed satellites keeps increasing. To restore the fail satellites as soon as possible, it is necessary to analyze the motion evolution of failed satellites and plan rescue and maintenance tasks both ground based and space based reasonably. However, satellites often carry various types of flexible attachments, and the elastic vibrations of attachments are coupled with the satellite’s attitude and orbital motion. Satellite’s mechanical energy is continuously dissipated since the attachments’ vibration, leading to unique motion evolution characteristics of these satellites from rigid ones. The attitude vibration coupling dynamic model of failed satellites carrying flexible attachments is established to analyze the long term motion evolution convincingly. And the relationship between the energy dissipation and the motion evolution trends is also analyzed. The results show that the initial angular velocity of the satellite, the installation state of flexible attachments, and the structural and material characteristics have a significant impact on the energy dissipation rate, while the initial vibration state of flexible attachments has a small impact on the energy dissipation rate.

The increasing tension in space exposes high orbit satellites to threats from space debris and other spacecrafts. The establishment of a high orbit space based situational awareness system, which enables observation and cataloguing of high orbiting targets, is of great significance to guarantee the normal operation of high orbiting satellites. Optical observation has the advantages of passive passivity, low energy consumption and suitable for long term operation compared with infrared observation. In this paper, the configuration design and coverage analysis of the high orbit optical observation constellation are carried out. A visibility constraint model is established based on the imaging characteristics of the optical load. Target coverage with finite time is established as the optimization index. The configuration optimization design problem is transformed into multiple single satellite optimization problems by decomposition optimization strategy. The differential evolution (DE) algorithm is used for constellation configuration optimal design. The simulation results show that the decomposition optimization strategy has better global optimization characteristics. The relationship between the number of observation satellites and the target coverage is analyzed and the marginal effect of non repetitive target coverage is discussed.

Low altitude airspace drones are characterized by small size and flexible flight, which brings difficulties to visual detection of trespassing drones. The low altitude drone object segmentation algorithm incorporating an attention mechanism named Rep YOLACT (re-parameterization you only look at coefficients network) is proposed, which is first used with RepVGG (re-parameterization visual geometry group) networks to improve ResNet (residual network) backbone in YOLACT and enhance the feature extraction capability of the network. Meanwhile, CBAM (convolutional block attention module) is added after the three feature layers output from the backbone feature extraction network, so as to further utilize the information of the feature layers efficiently. Experiments are conducted on FL-drones (flying drones dataset) and MUD (multiscale unmanned aerial vehicle dataset), respectively. The results show that the proposed Rep YOLACT algorithm improves mask AP (average precision) and mask AR (average recall) by 0.3% and 11.7%, respectively, compared with YOLACT algorithm on FL-drones. The proposed Rep YOLACT algorithm improves 2.3% and 5% on mask AP and prediction frame AR compared to YOLACT algorithm, which can perform the drone segmentation task well and its segmentation accuracy is higher than other mainstream segmentation algorithms.

Accurate prediction of the Remaining Useful Life (RUL) of rolling bearings is of paramount importance for ensuring the safe, stable, and reliable operation of engineering equipment. Existing deep learning prediction methods often directly establish a mapping relationship between vibration monitoring data and RUL, typically overlooking the differential states of rolling bearing performance degradation and neglecting the diversity of features extracted by deep learning models, leading to significant bias in RUL prediction results. In light of this, a novel method for dividing the degradation state of rolling bearings and predicting RUL is proposed. Features of bearing vibration signals are extracted, and the Mann Kendall test is employed to judge trends, determining the starting point of the degradation period. The endpoint of the slow degradation period is identified through the trend of normalized singular value correlation coefficients. A rolling bearing RUL prediction model based on a bidirectional long short term memory network with attention (Bi-LSTM-Att) is constructed, and the slow degradation period data and corresponding RUL labels are used to train the prediction model to achieve RUL prediction. The accuracy and effectiveness of the proposed method for bearing RUL prediction are validated through a public bearing dataset.

A satellite fault diagnosis approach based on federated learning is proposed to address issues such as single satellite under configuration and incomplete measurement information. Firstly, a fault model for satellites is established, and fault data is generated via the unity simulation environment. Then, a Bidirectional Coordination Network (BicNet) is used to construct local training models, which considers neighboring satellite fault information for decision making. The diagnostic network does not need to be retrained when the number of formation satellites changes, enabling plug and play. Finally, a federated learning framework is used for distributed training, integrating fault features of the entire satellite group without increasing communication pressure. Each satellite uploads local model parameters for collaborative modeling, improving the fault diagnosis capability for different fault types of satellite group and completing the fault diagnosis. Simulation results demonstrate high accuracy of 99% on the test set, indicating the effectivenessw of proposed method.

Aiming at the characteristics of wide sources, diverse forms, large differences and small quantities of spacecraft fault knowledge, and the defects that the fault knowledge contained in telemetry data during spacecraft operation is difficult to be effectively utilized, this paper proposes an ontology entity bidirectional constrained knowledge graph construction method, which adopts a combination of top down and bottom up approaches to construct knowledge graphs and implement graphical fusion of multi source heterogeneous fault knowledge of spacecraft including telemetry data. At the ontology layer, an improved IDEF5 method is proposed to construct the fault knowledge ontology. At the entity layer, three different knowledge extraction methods are proposed to extract knowledge from (semi)structured data (FMEA analysis table, expert rules), unstructured data (fault texts) and telemetry data, and fuse the knowledge in the entity layer, according to the sources of fault data and the degree of structure. The construction of the fault knowledge graph is realized through the bidirectional constraints and collaborative optimization between the ontology layer and the entity layer. In this paper, taking the spacecraft control moment gyroscope as an example, the fault knowledge graph is constructed and displayed visually by using the above method. The feasibility and effectiveness of the method are verified by a case study.

Timely and effective detection of abnormal changes in spacecraft power subsystem (SPS) is an important guarantee for the safe and stable operation of spacecraft. However, due to the complex working environment of SPS and the closed loop structure inside SPS, the telemetry signal characterizing its working state contains noise and cannot reflect the fault information in time. Therefore, in view of the noise and label free problems of SPS telemetry signals, a SPS fault detection method is proposed based on multiresolution singular value decomposition (MSVD) and auto encoder (AE). Firstly, MSVD is applied to the field of wave signal denoising to reduce the influence of noise on telemetry signal. Secondly, aiming at the problem of lack of fault labels in SPS telemetry data, an unsupervised auto encoder algorithm is used to detect the abnormal data after noise reduction. Finally, the proposed algorithm is applied to SPS, and the denoising effects of MSVD, wavelet transform and empirical mode analysis are compared. Then, the SPS is detected by AE. The results show that the proposed algorithm has lower misjudgment rate and higher detection rate.

Aiming at the problem of mechanical resonance caused by weak damping flexible mode of sandwich system and the influence of backlash nonlinear factors, an auto coupling PID control method independent of the controlled object is used in this paper. This method treats all uncertain factors such as nonlinear dead zone in the sandwich system as a total disturbance, establishes a controlled error system with the total disturbance as the excitation, and then designs an auto coupled PID control system based on speed factor, and fully proves the robust stability and antidisturbance robustness of the autocoupled PID control system in theory. The simulation results show the effectiveness of the autocoupling PID control effect. The control method not only has a good response speed, but also has good robustness. Meanwhile, the method has a good application prospect in the field of sandwich structure flexible transmission system control.

The inertial measurement unit (IMU) deployed on the reusable spacecraft not only meets the needs of long term space segment, but also needs to have high dynamic performance to meet the needs of high precision navigation during reentry. According to the needs of IMU deployed on the reusable spacecraft， the methods of dynamic accuracy design and verification in the development of reusable spacecraft IMU are summarized, and a six degree of freedom vibration IMU accuracy test method is introduced. The method can simulate the reentry mechanical environment more realistically, and can be triggered by system requirements to comprehensively evaluate the dynamic accuracy of IMU. It can also provide reference for other types of reusable spacecraft.

Designing intelligent operating methods is a key for constructing autonomous operating abilities of core devices such as spacecraft. Benefiting from the development of machine learning techniques, current intelligent operating methods driven by data have shown significant improvements on the ability of autonomous operation. However, viewing the trend of spacecraft clusters, traditional methods are challenged by two key requirements, distributed learning and privacy protection. A feasible solution is based on federated learning whose major concerns are how to learn efficiently in a distributed way with privacy performance guarantee. Core devices like spacecraft usually work in extreme environments and are very limited on the resources of computation and communication, and different devices show significant heterogeneous characteristics on data distributions, computation resources and so on. The heterogeneous characteristics can reduce the performance of general federated learning methods. Therefore, in this paper, based on the idea of grouping models, a federated learning algorithm for constructing intelligent operation methods is proposed, which is designed with consideration of the heterogeneous characteristics. The proposed method can reduce the waiting costs among different heterogeneous devices, adjust the timing of local learning of different devices, provide different models for devices with significantly different data distributions, and achieve the goal of improving the performance of operation models obtained by federated learning. Experimental results are conducted to show the the effectiveness of the proposed method

Efficient and accurate anomaly detection of power equipment is essential for aerospace safety. Scientific detection and maintenance can promptly identify potential faults and ensure the safety and reliability of the system. The data collected by sensors from power equipment contains valuable information. Feature extraction is usually required for processing these data. Although deep learning methods historically obtain excellent results, there is always a trade off between fine tuning existing networks or designing models from scratch for sensor data processing. To address this issue, we propose a temporal feature extraction network for time series data based on self supervised learning. First, we use self supervised learning methods to pre train the network. Then we devise a novel network model structure that can effectively extract the representation of time series data. Finally, we evaluate the proposed method on relevant datasets, and the experimental results demonstrate the effectiveness of the proposed method.

This paper proposes a dynamic scene visual odometry method based on deep learning. The C3Ghost module is built using the lightweight Ghost module combined with the target detection network YOLOv5s, and the CA (coordinate attention mechanism) is introduced to improve the network detection speed while ensuring detection accuracy. It is combined with the motion consistency algorithm to eliminate dynamic feature points and only use static feature points for pose estimation. Experimental results show that compared with the traditional ORB SLAM3 (orient FAST and rotated BRIEF simultaneous localization and mapping 3) algorithm, the ATE (absolute trajectory error) and RPE (relative pose error) on the TUM (technical university of Munich) RGB-D (RGB depth) high dynamic data set has improved by more than 90% on average. Compared with the advanced SLAM algorithm, it is also relatively improved. Therefore, this algorithm effectively improves the stability and robustness of visual SLAM in dynamic environments.

Health state prediction is a key technology to ensure the safe and stable operation of spacecraft in orbit from a system level. This paper proposes a method for predicting the health status of spacecraft based on unsupervised clustering and long short term memory (LSTM) networks, in response to the characteristic of performance degradation in key components of mechatronics. This method first extracts high dimensional time domain features of multi dimensional parameters of a single component of spacecraft, and fuses them into health factors that reflect the operational status of components through PCA method. Then, it combines unsupervised clustering algorithm to identify different evolution stages of faults. Finally, LSTM network is used to construct a health state evolution prediction model for each degradation stage, achieving health state prediction of spacecraft component. This article takes the key component of the control system, the Control Moment Gyroscope (CMG), as an example to experimentally verify the effectiveness of the above algorithm.

In order to construct a longer term constraint in the process of visual positioning, and facilitate the establishment of globally consistent trajectory estimation for aircraft equipped with visual positioning equipment, a lightweight weakly supervised simultaneous localization and mapping (SLAM) loop closure detection algorithm is proposed based on contrastive learning. By establishing consistent global descriptors for images and using similarity measures to judge loops in trajectories, long term timing constraints are established. Considering the application on resource constrained platforms, an EfficientNet (efficient neural network) is used to achieve more efficient feature extraction, and the NSE (need squeeze and excitation) attention module is combined to improve the screening of effective data in the process of data dimensionality reduction. Global descriptor is integrated through a complete VLAD (vector of locally aggregated descriptors) layer. The network model can still have efficient recognition ability in environmental changes such as lighting conditions, viewing angles, and seasons. The experimental results show that, while maintaining a 2% difference in the TOP 5 recall index compared to the baseline model, the proposed method can effectively reduce model volume by 57%, training time by 35%, and improve execution efficiency by 48%, which is beneficial for deployment on resource constrained embedded platforms such as small drones

The strictly regressive orbit is designed under high precise orbit dynamic with high order non spherical gravitational field of earth. The strictly regressive orbit is one kind of sun synchronous repeat track orbits, of which the beginning and ending space trajectories over repeat period in WGS84 coordinates are highly coincided. The on borne tube navigation algorithm sets a strictly regressive orbit designed before launch as reference orbit, based on which a virtual formation is formed with the real satellite. Since the effect of air drag is not considered in the design process of strictly regressive orbit, the satellite in orbit can calculate the orbit decay situation and orbit decay rate caused by air drag. To reuse the reference orbit, the effects of third body perturbation of sun and moon are also not considered in the design process of strictly regressive orbit. Therefore the long period motion characteristics of orbit inclination are determined during in orbit flying. Based on the telemetering data of LT1 satellite, the orbital characteristics mentioned above are analyzed in this paper.

In order to achieve efficient and high precision torque calculation, a plasma flow detumbling model based on neural network is established. To address the limitations of traditional guidance laws in handling complex tumbling targets, the principle of nutation stabilization is defined, and an optimal guidance law is designed for plasma flow direction. Simulation results indicate that the rapid dynamics model based on neural network can significantly reduce the torque calculation time while maintaining the same precision. The optimal guidance law can rapidly stabilize complex nutation target. This study significantly improves detumbling efficiency from both onorbit calculation and mission strategy, laying the theoretical foundation for onorbit service based on plasma flow.

The remaining useful life (RUL) of the aero engine is important for the safe operation of the engine equipment and the development of maintenance plans. At present, the existing methods are difficult to effectively extract the degradation features of equipment under complex operating conditions and complex faults. To solve this problem, an engine RUL prediction method based on multi scale temporal convolutional network (MTCN) is proposed. In this method, time convolutional networks are used to extract temporal information. Moreover, the degradation features of equipment under complex operating conditions are extracted by multi scale convolution kernel. As a result, it is better to predict the RUL of equipment under extreme conditions. To verify the validity of the proposed method, abundant experiments are carried out on the C MAPSS dataset. The results show that the proposed method can effectively improve the accuracy of RUL prediction under complex conditions.

In order to ensure safe and accurate landing on celestial bodies, it is necessary to measure the speed of space probe, providing important information for guidance, navigation and control system. A real time visual velocity measurement method only based on optical camera is proposed in this paper. For terrain image sequences of celestial bodies, we use recurrent all pairs field transforms to extract the optical flow field between adjacent frames. Then we extract the eigenvectors corresponding to the optical flow field via the convolution layer and pooling layer in deep neural networks. To reduce the influence on measurement accuracy caused by visual perspective during the landing process, a long short term memory network for video sequences is constructed to match the eigenvectors up with velocities, thus a real time landing speed estimation is achieved for space probes. Simulation results demonstrate that our technique decreases the mean absolute percentage error by 11.98% and has higher measurement accuracy in comparison to the forward propagation network.

Combining the characteristics of redundant configuration of attitude sensors and unknown inertia parameters of the on orbit assembled satellites, in order to suppress the influence of sensor measurement errors and parameter uncertainty disturbance on satellite attitude, firstly a gyroscope/GPS/star sensor multisensor federal filtering algorithm is established and the residual chi square test method is used to achieve the detection, isolation and reconstruction of sensor faults, by which the attitude determination precision and result reliability are improved. On this basis, the parameter identification algorithm is used to estimate the assembly satellite inertia parameters, and the adaptive fixed time sliding mode control algorithm is proposed to improve the control precision of the sliding mode control under unknown disturbance and reduce the parameter selection range to attenuate the chattering phenomenon. Finally, the algorithm effectiveness is verified by numerical simulation, and the attitude precision and attitude stability of the assembled satellite are within 0.000 2° and 0.000 3(°)/s, respectively, indicating that the proposed algorithm can achieve high precision and high stability attitude control of the assembled satellites.

A high performance anomaly detection model has been constructed to address the problem of sparse anomalous image data in the real world. A two stage framework anomaly detection model is built using only normal training data and a small amount of synthetic anomaly sample. First， a ResNet 18 encoder model is trained to extract representation by the pretext of classifying normal data and synthetic anomaly data. Then， a single classifier for anomaly images is built through modelling the distribution of normal data representations using Gaussian density estimation. GradCAM is applied to extend the model， enabling the anomaly detection model to locate anomaly regions without labels. Finally， experiments are conducted on a simulated anomaly detection dataset using real world images， demonstrating that the proposed algorithm can detect anomaly and provide location results in remote sensing images that are even difficult to recognize with human eyes.

The fault detection for quadrotor unmanned aerial vehicle (UAV) is studied in this paper. Considering the UAV model is nonlinear and strongly coupled, a performance driven fault detection method is proposed based on neural network. However, the established fault detection system cannot be applied when the UAV enters a new gravitational field. To deal with solve this problem, a fault detection method is proposed based on transfer learning. By means of subspace transfer method and Bregman divergence measurement method, the source domain and target domain are aligned, and the parameter transfer and threshold setting of neural network are realized. Finally, we verify the effectiveness of the proposed method in a four rotor UAV system.

A satellite residual fuel estimation and correction method based on input parameter deviation analysis is proposed for solving the problem of large error in calculating residual fuel using PVT method, which affects satellite mission planning. Based on the derivation of the error propagation equation for residual fuel estimation and the comparison of the calculated deviations between the bookkeeping method and the PVT method, the error sources and input deviations of the PVT method for calculating residual fuel are analyzed to achieve satellite residual fuel correction. Under the premise of only considering residual fuel, the on orbit fuel consumption demand of GEO (geostationary orbit) satellites is analyzed to predict the remaining life of the satellite. The practical engineering application shows that this method can correct the calculation deviation when there is a large deviation in the satellite residual fuel calculation, and provide effective reference and technical support for satellite mission planning.

For the satellites equipped with multiple star sensors, there will be many problems such as too many filtering constant gain matrices and complex system when using conventional CGKF (constant gain Kalman filtering) method to determine the attitude. In order to simplify the filtering system and unify the constant gain matrix, a satellite attitude determination method based on virtual star sensor is proposed in this paper. Firstly, with small calculation amount, an algorithm of time lag compensation and relative reference calibration of star sensor is presented, which is suitable for onorbit realtime calculation of onboard computer. The output data of star sensor is unified to the current star time and the measuring reference of star sensor is unified. Secondly, the output data of virtual star sensor(the installation matrix is a unit matrix) is constructed based on the output data of single star sensor/dual star sensors, and a unified constant gain matrix is designed to determine the attitude. The simulation results show that the attitude determination accuracy of the proposed method is equivalent to that of the conventional CGKF method, therefore the effectiveness of the proposed method is verified. The proposed method unifies the constant gain matrix, saves a lot of onboard computer resources, and has important engineering application value.

This paper investigates the multi-sensor allocation strategy problem for linear system identification in a networked environment. A binary quantization mechanism is introduced to save communication bandwidth. For addressing the redundancy and channel congestion issues during data transmission, a differential event driven communication mechanism is proposed, and its communication rate is provided. It is shown that such mechanism has the capability of complete information recovery. Based on the available data at the receiving end, identification algorithms for various finite impulse response systems are constructed, and their convergence performance is analyzed. Furthermore, the multi-sensor allocation problem under constrained communication resources is modeled as a constrained optimization problem, and an improved genetic algorithm is designed to give the optimal solution. Finally, a numerical example is used to verify the correctness and effectiveness of theoretical results.

In order to address the path planning problem of intelligent agents in complex environments, this paper proposes an online off policy deep reinforcement learning algorithm model based on an improved prioritized experience replay method. Firstly, the model utilizes a flexible action evaluation algorithm to achieve collision free path planning for the intelligent agent by designing the state space, action space, and reward function. Secondly, by calculating the sample mixing priority using the sample priority and TD error, a measure of sample diversity is obtained, and an improved prioritized experience replay method based on the flexible action evaluation algorithm is proposed to enhance the learning efficiency of the model. The simulation experimental results validate the effectiveness of the proposed improved flexible action evaluation algorithm under various parameter combinations and the superiority of the improved prioritized experience replay method in model learning efficiency for continuous control tasks

The Ju Mang Satellite is mainly used for carbon monitoring of terrestrial ecosystems, investigation and monitoring of terrestrial ecology and resources, and monitoring and evaluation of national major ecological projects. The Ju Mang control subsystem adopts a high precision attitude determination, high stable attitude control and hybrid trajectory planning attitude maneuver algorithm. According to the demand of the load for the monthly calibration, an inertial scanning mode is designed, which can pass through any space position at any time. According to the requirements of satellite autonomous mission planning, a surface attribute prediction method based on the surface model map is designed. For the first fire detection sensor, a high precision calculation of the geographical longitude and latitude of the fire point is designed, and relative surface linear velocity and the auxiliary cloud judgment information such as the solar zenith angle, satellite zenith angle and relative azimuth angle are provided for the sensor to dynamic fire point prediction. In orbit, the highly reliable fire pointing calculation and false fire point are realized. Based on on orbit data of Ju Mang satellite, the implementation of specific indexes is proposed. Furthermore, the scheme of control system is tested on orbit.

Single pixel imaging is an imaging technique that reconstructs a complete image using only nonresolving bucket detectors combined with spatial light modulation information. It features nonlocal imaging and high sensitivity, making it suitable for ultra long distance imaging and detection of noncooperative targets in outer space. However, it requires multiple spatial light modulations for detection, resulting in low signal to noise ratio in the reconstructed images. A global attention mechanism based image enhancement method for low sampling rates is presented in this paper. A novel SUNet(swin transformer unet)network is built via Transformer architecture to address the issues of translational invariance and limited global receptive field in traditional convolutional neural networks. Improved differential ghost imaging algorithm based on CC(cake cutting)sequence is employed to reconstruct low quality images under low sampling conditions, which are then enhanced by SUNet. Experimental results show that, compared to the GIDC(ghost imaging using deep neural network constraint)method proposed in 2022, this approach achieves 3.29 dB improvement in peak signaltonoise ratio and 8% increase in structural similarity at 0.1 sampling rate, providing a new technological avenue for spatial detection in singlepixel imaging.

The impeller blades of the engine are a key component of the propulsion system of a space spacecraft and play an important role in the success and efficiency of space missions. In order to solve the above problems, this paper proposes a defect detection algorithm (EF CenterNet) that integrates multi-scale features and attention mechanism, and uses the lightweight EPSANet network as the backbone of the CenterNet algorithm to effectively integrate the PSA segmentation attention mechanism, pay attention to more important defect features, and enhance the feature extraction ability of the network. At the same time, the FPN structure is added after the feature layer output by the backbone feature extraction network to further integrate multi scale information, that is, low resolution high level semantic information and high resolution low level feature information, so as to improve the defect detection accuracy of the algorithm. Experimental results show that the proposed EF CenterNet algorithm achieves an average accuracy of 96.74% in the self made dataset, which is 1.81% higher than that of the baseline CenterNet algorithm, and an average accuracy of 77.37% in the public dataset.

In order to establish an accurate model of the all electric propulsion satellite, design an attitude orbit control method and improve the satellite control accuracy, the dynamics modeling of the all electric propulsion satellite with deployable thruster point assembly mechanism (TPAM) is studied. The model of the four joint deployable TPAM is established, and the electric thrust and torque models based on the configuration of TPAM are derived. On this basis, the attitude orbit dynamics equation of the satellite is established. The model can be used to describe the attitude motion state and orbit change of the all electric propulsion satellite, which can accurately reflect the motion performance of the satellite. The model has been applied in the development of the APSTAR 6E satellite. The simulation results show that the dynamic simulation results of all electric propulsion satellite via the model accord with the test and analysis results, and the control effect of the APSTAR 6E in orbit is consistent with the expectation, which verifies the validity of the model.

In the problem of two spacecraft rendezvous, the multi impulse strategy can often achieve less fuel consumption. In this paper, the artificial bee colony algorithm is used to optimize the fixed time multi impulse rendezvous of spacecraft, and an improved artificial bee colony algorithm is proposed. This method can simplify the processing of time constraints and obtain solutions that meet the constraints of earth radius. This method is easy to program and robust, and can be applied to different perturbation models. Simulation results show that the algorithm is not easy to fall into local optima, and has higher accuracy than particle swarm optimization algorithm and traditional artificial bee colony algorithm.

To solve the control problem of nonminimum phase underactuated vertical taking off and landing (VTOL) aircraft, an ACPID(autocoupling proportional integral differrential) control method is proposed. Firstly, coordinate transformation is used to map the center of mass of VTOL aircraft to Huygens vibration center, which can not only realize decoupling of control input of the new system, but also avoid zero dynamic instability of nonminimum phase VTOL aircraft. Then, Huygens vibration center is designed with ACPID controller in vertical and horizontal directions respectively, and the bottom thrust and the virtual instruction of roll attitude angle for VTOL aircraft are obtained respectively, and then the ACPID controller of the roll attitude angle is designed to form the roll torque, so as to realize the position tracking control of VTOL aircraft system. Finally, the robust stability and antidisturbance robustness of the closed loop control system are proved by the complex frequency domain analysis theory. Theoretical analysis and simulation results show the effectiveness of the proposed method, which has important scientific significance and wide application prospects in the field of nonminimum phase underactuated control system.

With the increase of spacecraft software quantity and complexity, the demand for software reuse is becoming more and more urgent. In this paper, for the electric propulsion software of multiple platforms at the present stage, a structured analysis method is used to carry out software requirement analysis, extract common requirements and variable requirements, and identify reusable software components. After that, an event driven model based on adaptation improvement is proposed to assemble software components, and an electric propulsion software architecture framework is established. Finally, the public service operation，the form of encapsulation of hardware drivers are described, and the way that the software architecture can be applied in specific projects is given. The electric propulsion architecture framework proposed in this paper and the designed and implemented software components have been applied in several projects and achieved good results.

Aiming at the problem of anomaly monitoring due to the insufficient sensors and low confidence level of the rocket power system, an improved support vector machine based anomaly monitoring method for the rocket power system is proposed. Firstly, a closed loop of the rocket control system is built, and the flight state dataset is constructed by selecting suitable measurable parameters. Secondly, the LSTM Auto encoder algorithm is used to reconstruct the flight state data to obtain the residual data. Then, the support vector machine model is constructed, and the artificial bee colony algorithm is used to find the optimal classification parameters for the support vector machine parameters. The residual dataset is input to the support vector machine model. Finally, the effectiveness and feasibility of the algorithm are verified via the closed loop simulation data.

Due to its characteristics of being thin walled and flexible, the dynamics performance parameters of flat solar sails for deployable thin film structures are rather complex. In order to comprehensively and accurately understand the impact of the large flexible solar sail dynamics on the attitude control performance of new hundred kilogram class low Earth orbit satellites, the ANCF (absolute node coordinate formulation) beam model and ANCF membrane elements are used to construct the primary flexible appendages. A dynamic model of large space deployable structures is established based on the predeformation of the beams. With the established dynamic model, complex space environmental disturbances including solar radiation pressure, aerodynamic drag, gravity gradient torque and geomagnetic torque are comprehensively considered. The CMIF (complex modal indicator function) method is employed to identify the modal parameters of the flat solar sail in orbit. Comparison between the identification results and simulation analysis results show that the CMIF method can effectively identify the loworder natural modes of deployable flat solar sails, laying a theoretical foundation for their engineering implementation.

For the fast analysis of dynamic stray light of the flexible solar array vibration measurement camera in the space station, the relative angles between three environmental light sources(the sun, the moon and the earth) and optical axis of the camera in the orbital period are computed, meanwhile the dynamic vibration displacements of a large flexible array are simulated, a dynamic stray light discrimination algorithm that significantly reduces the amount of computation is given, based on the analysis of environmental light sources characteristics (point source and area source) and two types of stray light interference ways (direct incidence and incidence after primary reflection). The problem of stray light trajectory changing dynamically with the solar array is solved. The simulation results show that the realtime and accuracy of this algorithm can meet the stray light analysis requirements of large flexible solar array vibration measurement cameras.