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Modal-Constrained Co-optimization Approach of Thermomechanical Performance and Weight for Straight-Ribbed Brake Discs Using Deep Learning2026-01-5061To be published on 07/22/2026
In this study, an efficient method for concurrent thermomechanical performance and weight optimization under modal constraints is proposed to address the coupled design challenges of thermomechanical characteristics (thermal capacity, thermal deformation, and modal) and structural weight in straight-ribbed brake discs. Based on high-fidelity computer-aided engineering (CAE) simulations of brake disc thermomechanical behavior, a neural network (NN)-based surrogate model and a ResNet-guided geometric feature recognition (RGFG) model for automatic modality recognition were developed, and integrated with a particle swarm optimization (PSO) framework for optimal solution exploration. When applied to a passenger vehicle brake disc case study, the surrogate model of NN demonstrates remarkable accuracy: it shows more than 95% agreement with the CAE results in thermal capacity prediction, the prediction accuracy of thermal deformation exceeds 90% compared to CAE results and 83.4% compared to test result, thereby validating the method’s effectiveness. Compared with conventional CAE approaches, the surrogate model of NN achieves a subsecond prediction speed, significantly reducing computational costs. The surrogate model of RGFG achieves a test accuracy exceeding 95%. Furthermore, the proposed optimization framework offers valuable insights for the inverse design of brake discs.
Han, Simiao, Jiang, Daxin, Han, Chao, Wang, Jinda, Sui, Qinghai
Research on Deep Learning Navigation Algorithms for Helicopter Airborne Navigation2026-99-18647/17/2026
It is very hard to position helicopters in complex environments, and this severely limits their ability to navigate on their own. This paper proposes a navigation algorithm that uses a combination of different sensors and deep learning. It uses a special type of deep learning called ResNet50 and a special type of machine learning called LSTM. This algorithm extracts features of the environment and uses a Kalman filter to estimate the state of the system. The system is made more robust by merging information from multiple levels. The algorithm’s ability to maintain stable navigation in the face of faulty sensors is noteworthy, as is its use of an adaptive inference strategy that dynamically adjusts computational load. This strategy strikes a balance between performance and resource consumption. Experiments show that the plan works well in places where GPS is not available. This makes it much better for the helicopter to fly by itself, and it can be used in places like the army, for looking at places from the sky, and for helping people in danger.
Yang, Ming
Few-Shot Semantic Segmentation Method for Crater Detection on Small Bodies2026-99-18527/17/2026
Craters are the primary landmarks used for visual navigation in missions exploring small celestial bodies. However, obtaining high-quality, annotated crater data is often challenging due to limited imaging conditions and strict mission constraints. Conventional semantic segmentation models struggle with limited data and are challenging to train effectively. To overcome this limitation, this study introduces a few-shot segmentation approach for crater detection on small celestial bodies. Our method includes a prototype representation module that constructs class-level prototypes to quickly associate crater regions with their semantic features. This paper also designs an iterative learning module that gradually improves the segmentation output, helping the model better capture detailed edges and structures. Tests on a simulated few-shot dataset demonstrate that our method provides reliable and accurate crater segmentation, achieving a mean intersection-over-union (mIoU) of 88.7, outperforming traditional fully supervised methods.
Li, Shuai, Zhu, Shengying
Greedy Satellite Scheduling Method with Minimal Switching Based on Interacting Multiple Model2026-99-18207/17/2026
This paper investigates the tracking of highly maneuverable targets during flight and the corresponding satellite scheduling problem in a space-based observation system. Based on dual-satellite measurements, a nonlinear observation equation was formulated. The J2 perturbation model and the current statistical model were utilized within an Interacting Multiple Model filtering framework to achieve adaptive estimation of the target states of the boost-glide vehicles. Building upon this framework, a greedy satellite scheduling algorithm based on IMM model probabilities is proposed. This method dynamically selects the optimal measurement set within a given prediction window to maximize observation performance. The proposed strategy is compared against rolling-horizon scheduling and fast-slow timescale scheduling approaches. Simulation results demonstrate that the proposed method effectively adjusts model weights in response to target maneuvers, enhancing adaptability during highly maneuverable phases. Meanwhile, it reduces the number of satellite switches while maintaining estimation accuracy, significantly improving scheduling efficiency and tracking continuity.
Deng, Sirui, Liu, Chengzhe, Wang, Yandong
Load Analysis of Flap and Slat Actuators in Aircraft High-Lift Systems2026-99-18027/17/2026
This study develops an end-to-end load analysis scheme for flap and slat actuators, which comprise the aircraft’s high-lift system, and the analysis results are directly integrated into hardware optimization. Because they shoulder heavy responsibilities during the takeoff and landing phases, whether they can remain rock-solid under complex aerodynamic conditions or even remain unmoved in emergencies is directly related to their overall safety performance. This work process is closely linked and includes three major links. First of all, according to the CCAR-25.301 standard, the load envelope under normal working conditions is sorted out, and the limit cases of abnormal faults are exhausted. Subsequently, ANSYS Workbench pulled silk and peeled off the cocoons to capture the peak stress at the engagement between the output shaft and the gear. In the end, the closed-loop verification of the customized test bench made the theoretical calculations and the hardware-measured data exactly the same. The entire package provides designers with hardcore data support, and always uses airworthiness, not convenience, as the criterion when improving actuator performance.
Xu, Yuanze
Digital Twin Simulation and Verification System Design and Implementation for Satellite Production Lines2026-99-18047/17/2026
In this paper, we focus on satellite production lines and design and implement a digital twin simulation and verification system for them. This is to improve manual documentation efficiency and provide sufficient process controllability in the small satellites’ batch production and assembly testing. We built a layered architecture. This allows the system to dynamically interact with AIT data management systems, structured process systems, and equipment data by fusing multi-source data. We also develop functional modules that combine lightweight 3D model visualization, dynamic simulation engines, and hybrid scheduling optimization algorithms. These modules can perform twin simulation, execute processes, intelligently schedule production, manage work reporting, conduct intelligent analysis, trigger anomaly alarms, and perform system management. We also dynamically simulate complex workflows like satellite transfer and automated assembly. These workflows are then verified using 3D virtual scene modeling and physical engines. We use time-series analysis to improve scheduling accuracy and multidimensional dynamic monitoring and hierarchical response to enhance production stability. In practice, the system can provide visualized control over the full process of satellite production. This greatly improves assembly efficiency and process controllability. It can also be an extensible digital way for aerospace manufacturing. The use of hierarchical architecture design and multimodal data fusion can be further applied in the complex equipment intelligent manufacturing.
Zhao, Fenghua
Study on Resistance and Surface Pressure Characteristics of UUVs Navigating in the Pycnocline2026-99-18077/17/2026
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Zhang, Yin, Xue, Leilei, Guo, Liqiang, Fu, Xiao, Zhang, Xiaofang, Liu, Zhihao, Han, Guoxin
On-orbit Computing Framework for LEO Satellites Based on MADDPG and Lyapunov Optimization2026-99-18117/17/2026
This paper solves the problem of resource and energy constraints on orbit computing for LEO satellites. By combining MADDPG reinforcement learning and Lyapunov optimization, the paper proposes a computing framework and implements an adaptive task offloading model for space flight using a multi-agent deep actor critic algorithm, MADDPG. The joint optimization mechanism is implemented by multi-agent dynamic task offloading. Through the transformation from the state with long-term constraints into optimization of the status of queue stability, the load scheduling under threshold energy in accordance with the characteristics of energy constraints was realized by introducing Lyapunov virtual queues into the process of policy evaluation of deep reinforcement learning. The experimental results show that the proposed framework enables a lightweight preliminary calculation, balanced energy consumption to reduce resource allocation, and realizes the stable queues through adaptability of tasks under energy balance conditions, which can provide high-efficiency computing assistance and support for space orbit tasks such as monitoring remote sensing of Earth.
Yan, Ming, Zhao, Liang, Xu, Lexi, Zhou, Xiaofei, Hawbani, Ammar, Sun, Yunhe
Research on Clustering Data-Driven Intelligent Perception Algorithm for Micro Mobile Robots2026-99-18437/17/2026
To address the challenges faced by micro flapping-wing flying robots in visual navigation—specifically, the large volume of visual information and the difficulty in transforming it into usable intelligent visual data—this paper proposes a clustering-based data-driven approach for directional and image perception. The aim is to enable intelligent visual navigation for flapping-wing robots. The proposed method performs clustering analysis on gyroscope data from the flapping-wing robot to extract directional features. Simultaneously, it applies clustering techniques to visual images captured by the robot to identify intelligent features such as edges. This approach enables the robot to acquire multiple optimized perceptual data types, thereby enhancing the behavior control system. Through the use of clustering analysis, the method not only improves the effectiveness of visual navigation but also extracts features related to visual targets and environmental information, providing technical support for visual target tracking. The experimental platform consists of a flapping-wing robot equipped with an onboard camera, and the proposed clustering-driven visual image perception approach has been experimentally validated. Experimental results demonstrate the high feasibility and effectiveness of the method in practical applications. The main contributions of this study lie in two aspects: (1) a clustering-driven visual image perception method for flapping-wing robots, and (2) a clustering-based approach for identifying posture and behavioral patterns of flapping-wing flying robots.
Li, Zixuan, Ding, Wei, Zhang, Feng, Song, Min, Liu, Zhaoming, Miao, Lei, Liu, Haotian, Bai, Ning, Tian, Shen, Cui, Long, Wang, Hongwei
Aircraft Performance Prediction Model Based on Deep Learning2026-99-18577/17/2026
Aiming at the problems of traditional physical model methods in aircraft endurance prediction, an end-to-end prediction model based on depth deterministic policy gradient (DDPG) is proposed. The model realizes continuous mapping from flight parameters to range index through Actor-Critic dual network architecture, and combines experience playback mechanism and soft update strategy of target network to effectively suppress training oscillation and improve convergence stability. UAV Delivery Aircraft Versus hybrid dataset was used to verify model performance in test samples. The results show that the MAE of the model is 9.2 km, which is 42.1% lower than that of DQN; the prediction accuracy of the model is the best (MAE 7.3 km) in cruise phase, which is due to the dynamic compensation of time series difference error to wind speed disturbance; in environmental disturbance test, the error increment (50.0%) is significantly lower than that of DQN (78.0%) at low temperature (-5 ° C), which highlights its robustness to battery voltage sag. The model provides real-time and reliable decision support for aircraft endurance management in high-dynamic airspace.
Bai, Rongqiang, Chen, Li
Research on Quadrotor Trajectory Tracking Based on Model Predictive Control2026-99-18507/17/2026
Quadrotors (UAVs) are widely used in intelligent inspection, environmental monitoring, and logistics due to their simple structure, strong maneuverability, and vertical take-off and landing capabilities. However, their highly nonlinear, strongly coupled, and highly constrained dynamic characteristics make trajectory tracking control a challenging task. To improve trajectory tracking accuracy and control robustness, this paper proposes a quadrotor trajectory tracking method based on model predictive control (MPC). First, a six-degree-of-freedom dynamic model of the quadrotor is established and linearized with small disturbances to transform it into a state-space model suitable for MPC design. An MPC optimization controller is then constructed, with an objective function that minimizes state error and imposes an input energy penalty, while explicitly considering the system's input and state constraints. Simulation results demonstrate that this method exhibits good tracking accuracy and control smoothness for typical trajectory tracking tasks (such as circular and spiral trajectory tracking). Compared with traditional PID and LQR controllers, the proposed method significantly improves maximum error, mean square error, and interference rejection. This study provides an engineering-feasible optimization control framework for UAV trajectory control.
Peng, Fei, Tao, Zhong, Gao, Qiang, Jia, Bobo
Asteroid Landing Navigation Based on Invariant Extended Kalman Filter2026-99-18567/17/2026
Optical navigation serves as a critical modality for autonomous guidance during small celestial body landing missions. To address the inherent strong nonlinearities in both the lander’s dynamic model and optical observation model, this paper investigates an invariant extended Kalman filter algorithm based on Lie group structures. First, we establish the state model and optical observation model on the special Euclidean group. Subsequently, a linearized right-invariant error dynamics equation is derived using invariance theory, along with the formulation of state prediction models. Furthermore, the feature vector observation model is modified into a right-invariant observation form, enabling state correction through exponential mapping of innovation vectors. Numerical simulations using asteroid Eros 433 demonstrate that the proposed invariant extended Kalman filter (InEKF) outperforms the conventional extended Kalman filter (EKF) in both estimation accuracy and convergence speed. Notably, the algorithm eliminates the need for online Jacobian matrix computations, satisfying the stringent navigation requirements for autonomous landing operations. The results validate the effectiveness of Lie group-based filtering in handling the nonlinear geometry of pose estimation for irregular celestial bodies.
Liu, Zhengdong, ZHU, Shengying
Multi-Frame Centroid Extraction Method for Optical Navigation During the Approach Phase of Small Body2026-99-18517/17/2026
Autonomous optical navigation is one of the important navigation methods for the small bodies approach phase. To improve optical navigation performance during the approach phase to a small body, this paper presents a method for extracting the target centroid from sequential optical images. The process begins with fitting a minimum enclosing ellipse to the detected contours in each frame to obtain an initial estimate of the centroid. Building upon this, edge corner points across adjacent images are matched using normalized cross-correlation, and their displacement is tracked using optical flow techniques. The observed pixel trajectories are analyzed, and a predictive model of pixel motion is formulated based on the geometric relationship between the detector and the small body. By combining the directly extracted centroids with the predicted motion of key pixels, a fusion strategy is developed to improve the reliability of the centroid estimation. Finally, numerical simulation results demonstrate that the method significantly improves the accuracy of centroid extraction, thereby enhancing the overall performance of optical navigation during approach operations.
Liu, Jing, Zhu, Shengying
A Map-free Geomagnetic Navigation Method Based on the World Geomagnetic Model Position Estimation with a Fully Connected Neural Network2026-99-18557/17/2026
In map-free geomagnetic navigation conditions, the traditional matching algorithms will be ineffective, and the regular position searching optimization algorithms still face the problems of low navigation accuracy and inefficiency. How to further improve the accuracy and efficiency of the algorithm has become the key to the application of this method in maple’s geomagnetic navigation conditions. Based on the above background, this paper proposes an evolutionary gradient search navigation algorithm optimized via position estimation (PE-EGA). The world geomagnetic model (WMM) is used to establish the nonlinear correlation relationship between geographic position and geomagnetic features, and the inverse mapping of the geomagnetic model is fitted by a fully connected neural network to get the rough estimation of the geographic position of the vehicle, with a root mean square error (RMSE) of 0.0121 in position estimation. Finally, the information of the rough estimation is used to assist the decision-making of the navigational azimuth angle involved in the EGA algorithm. The simulation results show that the offset distance of the improved algorithm is only 27.09 m, and the path ratio reaches 1.0178 with an error ratio of 0.38%. Comparative study using measured geomagnetic data of Boao town with model data shows that the final offset distance is only 51.63 m, path ratio 1.0036, and error ratio 0.73%, which significantly improves the accuracy and timeliness of navigation compared to the original EGA algorithm. This article provides an innovative and practical solution strategy for map-free geomagnetic navigation.
Xie, Wenbin, Liu, Hongjie, Zheng, Ruifan, Ren, Xintian, Yan, Bing, Qiu, Wei, Chen, Zhuo
A Method for On-Orbit Testing of Long-Term Docking of Manned Spacecraft at the Space Station2026-99-18487/17/2026
To ensure the successful implementation of the separation, evacuation, and return processes of manned spacecraft after long-term docking at the space station, regular on-orbit health assessments must be conducted. Based on this requirement, a technical method for evaluation through autonomous on-orbit testing is proposed. First, the docking status and characteristics of the manned spacecraft’s systems, such as information management, crew environmental control, thermal control, power management, docking function, attitude, and orbit control function, are described. Then, the functional requirements for the separation, evacuation, and return of the manned spacecraft, such as the relative measurement, the relay communication, TT&C and data transmission, image and voice, instrument display and alarm, and the attitude measurement, are analyzed. Subsequently, the on-orbit testing system, test items, test procedures, and test methods for health assessment are detailed. It also provides the design of TT&C support, the design of energy support, and the main principle explanation for autonomous on-orbit testing of the system.
Cheng, Wei, Nan, Hongtao, Tian, Ye, Zhao, Zheng
The Terminal Guidance Method Improved Based on BP Neural Network2026-99-18227/17/2026
Terminal guidance is critical for ensuring strike precision in the final phase of flight. However, traditional methods, such as proportional navigation and optimal guidance laws, face significant challenges regarding real-time performance and adaptability to dynamic targets. To address these issues, neural networks offer a promising solution by enabling adaptive adjustments to guidance parameters, thereby improving performance under various constraints.
Ma, Hengwei, Wang, Yongfeng, Wen, Hong, Liu, Di, Wei, Yuanhang, Dong, Longhao, Luo, Ying
Research on the Protection Design of Flexible Cables for Civil Aircraft2026-99-18287/17/2026
Flexible cables are widely used in aircraft and are essential for ensuring the proper functioning of critical systems and flight safety. The design and validation of these cables represent a foundational technology in enabling the transmission of electrical power and signals throughout the entire aircraft. To achieve their intended service life, appropriate protective measures and experimental verification must be implemented. Drawing on the development experience of flexible cables for a specific domestic aircraft model, this paper proposes a combined protection method designed to extend the service life of flexible cables. Experimental analysis demonstrates the practicality and reference value of this approach.
Shi, Liqing, Hu, Huanghua, Ge, Zengwen
Research on Unmanned Aerial Vehicle Fault Detection Method Based on 1DCNN-BiLSTM2026-99-18667/17/2026
In recent years, drone technology has seen widespread application in both civilian and military fields. By 2025, China will introduce supportive policies from multiple dimensions, including industrial development, technological innovation, and application promotion, to significantly increase the number of UAVs in use and their frequency. However, drones are prone to malfunctions due to factors such as bad weather and electromagnetic interference, which may result in serious consequences, including property damage and casualties. Therefore, improving the accuracy of fault detection and the response time of drones is of great significance. Although current research has made progress, there are still deficiencies: First, most of them rely on a single or limited data source, resulting in incomplete information and vulnerability to interference, which leads to low detection accuracy and reliability; Second, traditional methods are mostly based on fixed thresholds or simple rules, lacking real-time dynamic monitoring and adaptive analysis capabilities, making it difficult to issue timely warnings of potential faults. To this end, this study proposes a multi-scale time series prediction model based on multimodal and multi-branch, integrating multimodal data, constructing a dual-branch architecture, and combining deep learning and attention mechanisms to enhance the anomaly detection effect of unmanned aerial vehicles. A dual-branch anomaly detection model based on 1DCNN-BiLSTM and continuous wavelet transform is proposed, including a trajectory prediction difference branch and a full time series data branch. In the dual-branch output stage, the attention gating mechanism is utilized to fuse features and improve the detection performance. The experimental results show that this model performs excellently in both normal trajectory prediction and anomaly detection, providing an effective solution for drone anomaly detection.
Pu, Zhenglin, Zhang, Lin
2025 International Conference on Aircraft Control and Navigation Technology2026-99-18007/17/2026
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Tobolski, Sue
Optimization of the Light Environment for the Remote Control Tower Humancomputer Interaction Interface Based on Ergonomics2026-99-18477/17/2026
The development of remote tower systems in aviation and the resurgence of multi-display interfaces and virtual environments have dramatically influenced ATC, increasing both controllers’ visual demands and their ergonomic needs. This study uses the Visual Ergonomics to study the impact of screen luminance level, along with color temperature, on trainees’ visual performance, fatigue, and physical discomfort in the control rooms of the Remote Tower. By combining a simulated remote control system with spectrometer measurements, PVT alertness tests, VMT (Visual Memory Test) measurements, and subjective evaluations, COST B21 can build up a multi-dimensional ergonomic assessment framework. Eight levels of display luminance (and color temperature) were tested, including two illuminance levels (300 lx and 400 lx) and four color temperature ranges (6000 K–9000 K). Using the Analytic Hierarchy Process (AHP), these parameters were assigned weights to derive a Visual Ergonomics (VE) scoring model, and the ideal visual performance was observed at 400 lx illuminance and 8000 K CCT. The results clearly illustrate the significant impact of display parameters on operational performance in remote tower systems and provide both practical data and a theoretical basis for the human factors design and fatigue reduction research on RTSs.
Zhong, Linfeng, Hu, Ruohui, Luo, Peilin, Zuo, Qinghai, Zhong, Qingwei, Ai, Yi
Research on Multi-Model Fusion Method for Small Satellite Abnormal State Detection Based on Machine Learning2026-99-18177/17/2026
Aiming at problems such as low efficiency and poor accuracy in fault identification for traditional small satellites, this paper proposes a multi-model fusion method based on machine learning. By constructing a telemetry data preprocessing module based on the Data Generation Adversarial Network, it effectively deals with outliers and fills missing values. Combining single model methods such as polynomial curve fitting, the grey model, and the ARMA model, and introducing the Long Short-Term Memory network and Gated Recurrent Unit to fuse with these models enhance the ability to process complex data features. The prediction results of each model are fused using machine learning methods, and finally, the fused value is taken as the final prediction result. The numerical simulation results show that this prediction method can predict the anomalies of different types of satellite telemetry parameters and has achieved good results.
Liu, Biyan, Chen, Ye, Guo, Qi
Key Technologies of Aircraft Flight Test Based on Structural Control2026-99-18257/17/2026
A comprehensive solution integrating advanced sensor technology, structural dynamics models, and intelligent control algorithms is proposed to address the shortcomings of traditional flight testing techniques in monitoring and controlling aircraft structures under complex flight conditions. By establishing precise aircraft structural dynamic equations through fiber optic sensors, an accurate description of the dynamic characteristics of the aircraft structure can be achieved. A distributed structural monitoring system is constructed through FBG to monitor the physical quantities, such as strain and temperature, of key parts of the aircraft in real time during flight testing. Based on the real-time monitoring data, the structural state of the aircraft can be predicted, and the structural response can be actively adjusted by controlling the actuator. The experimental results show that this technology system effectively improves the accuracy of structural monitoring and the effectiveness of control during aircraft flight testing, providing strong guarantees for the safety and reliability of aircraft flight testing, and laying a solid foundation for aircraft structural design optimization and flight performance improvement.
Gao, Sheng
Research on Convergence Analysis of Multi Objective Optimization Algorithm for Lightweight Bus Chassis Architecture2026-99-18427/17/2026
The multi-objective optimization algorithm framework for lightweight bus chassis architecture selects new sample points by utilizing the optimal solution obtained during the iterative process, and then reshapes the dynamic Kriging surrogate model, ultimately achieving the implementation of multi-objective optimization for lightweight bus chassis architecture. Its core lies in whether the NM-MOPSO algorithm can accurately converge to the global optimal solution of the model. This determines the accuracy of the sampling area and the effectiveness of the new sample points. If the algorithm converges inaccurately, it will result in poor performance of the model in the optimal solution region, thereby affecting the accuracy of the solution. Therefore, the precise convergence of NM-MOPSO algorithm is crucial for the success of multi-objective optimization algorithm for lightweight bus chassis architecture.
Han, Yangqi, Hu, Jing, Chen, Yajuan, Hu, Guangxue
Aircraft Optoelectronic Payload Transfer Alignment and Dynamic Error Compensation Algorithm Research2026-99-18497/17/2026
This paper proposes a multi-source dynamic error compensation algorithm for the transfer alignment of airborne optoelectronic payloads. This method addresses performance limitations of micro-inertial navigation systems (micro-INS) in complex dynamic environments, specifically those arising from accumulated device noise and the inability to perform static alignment due to installation errors. The algorithm’s core is the Extended Kalman Filter (EKF) technology. By constructing a “velocity + attitude” matching model between the UAV’s master inertial navigation system (MINS) and the optoelectronic payload’s slave inertial navigation system (SINS), it leverages high-precision MINS navigation information to correct SINS errors. Utilizing a 21-dimensional state space equation and measurement equation, the algorithm achieves real-time estimation and compensation of various errors, including attitude misalignment angles, sensor biases, installation errors, and flexure deformation. Simulation results demonstrate significant alignment accuracy improvement. Post-lever arm effect compensation, velocity errors are stably controlled within 0.01 m/s. Concurrently, flexure deformation angle compensation substantially reduces misalignment angle fluctuations across all directions, enhancing system stability and maintaining low misalignment angles. These findings validate the proposed error compensation strategy’s effectiveness.
Zhang, Lu, Li, Mao, Wang, Shiyong, Lei, Chao
Aerodynamic Optimization Method Based on a Hierarchical Adaptive Activation Function Neural Network2026-99-18237/17/2026
Efficient optimization of aerodynamic shapes is a critical challenge in aircraft design. Traditional CFD-based optimization workflows suffer from high computational costs and low efficiency, which severely restricts their practical engineering application. In this paper, a novel aerodynamic optimization method based on a hierarchical neural network with adaptive activation functions is proposed. The network adopts learnable B-spline activation functions and is hierarchically constructed in accordance with the sharing status of B-spline control points. After being trained to achieve fast and accurate prediction of aerodynamic performance, the network can effectively replace the traditional CFD module in the optimization loop. The primary advantage of the proposed method is that it significantly reduces the computational cost during the optimization process while ensuring that the prediction accuracy is not compromised. This work thereby presents a novel strategy and technical framework for streamlining the design process of hypersonic vehicles.
Liu, Di, Wang, Yongfeng, Wen, Hong, Wei, Yuanhang, Ma, Hengwei, Zhao, Runhui
Design of Carrier-Based Aircraft Landing Control Law Based on First-Order Linear Active Disturbance Rejection Control2026-99-18357/17/2026
This paper investigates the high-precision landing control problem of carrier-based aircraft. An Active Disturbance Rejection Control (ADRC) technique is employed to design longitudinal and lateral-directional landing control laws. The landing process is simulated by incorporating an airwake model, and the results are compared with those of a PID control law. The analysis demonstrates that the proposed ADRC controller reduces lateral deviation errors and significantly improves landing accuracy and success rate.
Yu, Jiayang, Yin, Yong
Machine Learning-Based Prediction of Landing Dynamic Response for Bogie Landing Gear2026-99-18297/17/2026
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Zhang, Hong, Fang, Wei, Zhao, Junfeng
Flight Test Methodology for Verification of Precipitation Static Protection in Civil Aircraft Radio Navigation Systems2026-99-18097/17/2026
The verification of Precipitation static (P-static) protection for the radio navigation system of civil aircraft is a critical test item for airworthiness certification. However, determining the presence of P-Static on the aircraft fuselage and assessing whether its discharge interferes with the radio navigation system remains challenging, with testing methods still under exploration. By analyzing airworthiness certification test provisions, the necessity of conducting flight tests for P-static protection verification of the radio navigation system was clarified. Based on existing conditions for civil aircraft flight tests, a comprehensive flight test method was proposed to verify the P-satic protection capability of the radio navigation system. This method includes determining external meteorological conditions, measuring electrostatic parameters, and designing aircraft maneuvers and states. The test plan was validated on a test aircraft. Discharge current data measured on a discharger indicates that during the flight of a civil aircraft through cirrus clouds, negative charge accumulated on the aircraft's surface, leading to electrostatic discharge. The maximum peak discharge current recorded was 330 μA. P-satic radiation field data were obtained near the Automatic Direction Finder (ADF) antenna; the radiation energy is primarily concentrated within the 200 MHz range, with some energy distribution still observed between 200 MHz and 500 MHz. Within the 200 MHz range, the signal amplitude exceeds the background noise, and stable peaks appear at multiple frequency points, with the maximum amplitude reaching up to 50 dBm.confirming the presence of a P-Static environment. This achieved the objective of evaluating the functional performance of the radio navigation system in an electrostatic environment, providing technical support for P-Static protection verification flight tests and offering a reference for the practical application of electrostatic protection design.
Han, Chunyong, Wang, Fusheng
Landing Site Selection and Visual Scene Generation Based on LiDAR Point Cloud2026-99-18197/17/2026
This paper focuses on autonomous drone landing scenarios. Addressing the core requirements of accurate landing site assessment and intuitive visual presentation, it conducts in-depth research on the application of 3D LiDAR (TOF technology) point cloud data. LiDAR captures point cloud data containing 3D coordinates and reflection intensity values. While sparse, non-uniform, and disordered, its high measurement accuracy and strong anti-interference capabilities make it a key sensor for landing terrain perception. Based on a review of recent research results from related teams, this study designed and implemented a comprehensive technical solution: First, raw point cloud data is acquired via the UDP protocol combined with an SDK interface. Preprocessing is then performed using voxel grid filtering (downsampling) and radius filtering (denoising). The assessment area is then divided into a row-by-column grid. A sliding window method is used to calculate the elevation difference, empty grid ratio, flatness, and slope of each grid. Based on these attributes, the grids are classified into six categories: Risk, Warning, Blank, Unknown, No Landing, and Landing. Finally, a grid attribute coloring method and OpenGL 3D rendering are used to generate the visual scene. Through the development of verification programs and moving obstacle experiments, it has been proven that the solution can efficiently process point cloud data and accurately identify safe landing areas, providing key technical support for the engineering realization of the autonomous landing function of drones, and also laying the foundation for the intelligent development of drone landing decisions in complex environments.
Guo, Hangyu, Shi, Zhe
Research on UAV Anti-UAV Strategies Based on Deep Reinforcement Learning2026-99-18157/17/2026
This paper constructs a reinforcement learning framework based on the PPO algorithm for drone air combat to solve 1v1 pursuit-evasion in 2D beyond-visual-range air combat. Firstly, the mission scenario is modeled, defining key roles of ATA and AA. Then, state transition models of pursuer and evader are built based on flight kinematics. To handle reward sparsity in policy network training, a dense reward function combining distance and angle rewards is designed to guide the agent in learning tail-chasing and interception strategies. Using the Actor-Critic architecture, deep neural networks implement the decision-making and evaluation modules. The PPO algorithm trains the pursuing drone in a simulation. Results show that after ~5 million steps, the agent learns a stable strategy, completing tasks promptly and generalizing well in unseen scenarios. This research offers ideas for drone combat and guidance, and supports autonomous decision-making in complex air battles.
Yu, Kangjie, Gong, Zheng, Hu, Runchang, Liu, Huixiang
A Mode Code Configuration Method for Remote Terminals in 1553B Bus Systems2026-99-18267/17/2026
Requirements of Interface for Aircraft/Store Electrical Interconnection System (GJB 1188A-99) is the current standard followed by all types of carrier aircraft and stores. This paper designed a 1553B bus remote terminal mode code configuration method that met the requirements of GJB1188A standard, completing the interrupt initialization and data initialization of compulsory mode codes. These comprehensive test results confirm that the proposed mode code configuration method is both reliable and effective, and provides strong portability, which can be used as a reference for the GJB1188A interface software design of other components
Han, Bin, Zhang, Kun, Liu, Xuhan, Ye, Jinhan, Li, Zhengmao
A Detection Method for 10N Thruster Ignition Anomaly Based on the Temperature Model2026-99-18147/17/2026
To solve a problem that ignition anomaly can’t be detected in time, based on the thermal equilibrium equation, the space heat flow, heater heating, propellant combustion, and thermal radiation to cryogenic space are considered to build an accurate ignition temperature method for the 10 N thruster by using on-orbit true temperature. Further, considering the error of measuring the thermistor, an envelope model for the 10 N thruster ignition temperature is established. Based on the above, a detection method for the 10 N thruster ignition anomaly of on-orbit satellites is proposed. The accuracy of the method is relatively high, and the absolute error is less than 3 degrees Celsius. An anomaly can be quickly detected when the 10N thruster ignition temperature deviates from the normal trend by 3–5 degrees celsius. The method is applied to a DFH-3 satellite, and the maximum difference of 10 N thruster ignition temperature between the theoretical values calculated by the proposed method and the measured values is only 2.72 degrees celsius. It has been proven that the prediction accuracy of the proposed method is high. It plays an important role in discovering the 10N thruster ignition anomaly in time and ensuring the success of satellite orbit or attitude control.
Li, Liling, Tian, Huadong, Wei, Yubo, Fei, Di, Xing, Chao
Analysis of Quadrotor UAV Position Accuracy Based on EKF Multi-Sensor Data Fusion2026-99-18597/17/2026
When quadrotor unmanned aerial vehicles (UAVs) operate in urban low-altitude airspace, especially within complex environments, their sensor perception signals are highly susceptible to blockages, deviations, and the inclusion of high-frequency noise. These factors, in turn, induce nonlinear variations in the UAVs’ flight mechanical properties, giving rise to abnormal flight stability issues such as attitude jitter, altitude fluctuations, and trajectory deviations. To address these challenges, this paper puts forward a method aimed at enhancing the positional accuracy of quadrotor UAVs, which is based on Extended Kalman Filter (EKF) multi-sensor fusion. In conjunction with the redundant configuration of sensors, a proportional-integral controller is specifically designed to allow optical flow sensors to compensate for the speed data generated by inertial sensors. Building on the EKF method, a comprehensive data fusion model is established, encompassing both position and speed states. Leveraging the MATLAB platform, trajectory flight simulations are conducted, utilizing multi-sensor data fused via EKF, with the sensor suite including GPS, IMU, Optical Flow sensors, and Barometers. The simulation results demonstrate that this proposed method can effectively mitigate the adverse impacts of environmental interference and sensor noise on the positional accuracy of quadrotors. By continuously correcting position information and accurately estimating position states, it significantly improves the UAVs’ flight position accuracy. This research outcome lays a robust and theoretically sound foundation for in-depth investigations on critical issues related to general aviation applications, such as the safe and efficient autonomous flight, adaptive and reliable intelligent navigation, and ultra-precise and mission-critical operations of quadrotor UAVs, thereby significantly contributing to the sustained and innovative advancement of the field.
Cui, Nan, Liu, Wenzhi, Liu, Hanqi, Wang, Jingrui, Wang, Zhizhong, Zhi, Haonan
Attitude Control of a Variant Flying Wing UAV Based on L1 Adaptive and Fuzzy Clustering2026-99-18587/17/2026
This study presents a full-envelope attitude-stabilisation and trajectory-tracking strategy for morphing flying-wing UAVs operating in highly nonlinear and strongly coupled conditions. The approach integrates fuzzy C-means (FCM) envelope partitioning with L1 adaptive control. Small-disturbance linear models are first generated at multiple altitude–Mach trim points; the FCM algorithm then performs unsupervised clustering in the state space, yielding representative subintervals that capture local flight-dynamic characteristics. The optimal cluster number and fuzziness exponent are selected using the partition coefficient, partition index, partition entropy, and Xie–Beni indices. For each sub-interval, an LQR baseline controller is designed and augmented by an L1 adaptive compensator, where a low-pass filter decouples adaptation from robustness to guarantee specified transient-performance bounds under matched/unmatched uncertainties, actuator saturation, and external disturbances. A feed-forward pre-filter realises online decoupling of the multi-input multi-output channels, thereby enhancing adaptability to variable sweep angles and large aerodynamic variations. Simulations covering low-speed/small-sweep and high-speed/large-sweep scenarios demonstrate that the proposed method sustains robust stability across the clustered envelope, outperforming conventional control schemes and confirming its engineering applicability.
Tang, Longhao, Sun, Xiaoxu, Liu, Changlin
J1979-DA, Digital Annex of E/E Diagnostic Test ModesJ1979DA_202607 (Current)7/16/2026
The SAE J1979 document set includes the communication between the vehicle's On-Board Diagnostic (OBD) systems and test equipment implemented across vehicles within the scope of regulated emissions and propulsion related OBD. The SAE J1979 Digital Annex supplements the J1979 base documents primarily by maintaining a global registry of regulated emissions and propulsion related data identifiers.
Vehicle E E System Diagnostic Standards Committee
Starter Battery Identification and ClassificationJ2981_202607 (Current)7/15/2026
This document provides recommendations to identify battery group sizes and dimensions for 6 V, 8 V, 12 V, and 24 V lead acid batteries.
Starter Battery Standards Committee
Worldwide On-Board Diagnostic Requirements OverviewJ3248_202607 (Current)7/14/2026
This document focuses on the latest in-force regulations. However, in addition to latest information, the report may include historical information. As regulations are superseded, the previous entry will remain to help understand the change in requirements over time. The initial focus of the document includes light-, medium-, and heavy-duty on-road vehicles with all propulsion systems. The document will include information from the United States and Canada, with later publications expanding to other regions. Forecasts for future regulations will not be included in the spreadsheet but be kept in a separate document. The document may be expanded to other types of applications/vehicles as information becomes available.
Vehicle E E System Diagnostic Standards Committee
RESS Cell Package Integrity Requirements and TestsJ3337_202607 (Current)7/14/2026
This recommended practice (RP) presents a methodology to evaluate RESS Cells Closure Integrity (Leak Tightness) requirement. This RP applies to two types of RESS Cells, each containing liquid electrolyte: Lithium ion (Li-ion) Cells and Sodium ion (Na-ion) Cells. The Equivalent Channel Method is used as a suggested cell closure integrity requirement for a given RESS Cell design during its production and product validation phases. The Closure Integrity requirements intended to assure no electrolyte leakage and no excessive moisture ingress during the usage of these cells as part of the RESS (Battery Pack), which is crucial to assure the safety and performance of these RESS. This RP specifies non-destructive Integrity (leak) testing processes of the Cell Closure. It describes approved leak testing technologies, testing procedures, tooling requirements, and leak test systems validation/verification requirements. This document may be applied to RESS Cell Closure Integrity testing during their initial product validation and their in-line 100% of production integrity/leak testing. This RP applies to RESS Cells with rigid packaging (cylindrical or prismatic) or flexible packaging (pouch).
Battery Standards Testing Committee
Drop Impact Phenomena: An Experimental Study of Isooctane Interaction with Dry Walls and Liquid Films2026-01-50157/13/2026
In this study, high-speed back-illuminated imaging and laser-induced fluorescence (LIF) methods were employed to investigate the impingement behavior of millimeter-sized single isooctane drops on a dry solid wall and various liquid films, including isooctane and glycerol solution films of different concentrations. Various fuel spray impingement scenarios in gasoline direct injection engines were examined. High-speed back-illuminated imaging was primarily used to examine the impact of fuel drops on a dry wall and a fuel film of the same composition as the drops. The LIF method was used to examine the impact of fuel drops on the glycerol solution film, allowing for the distinction between fuel drops and the glycerol solution film. The impingement behavior varied depending on the Weber number of the incident drop and the wall condition. When fuel drops impacted the solid dry wall vertically, they spread into a circular liquid film. The outer edge of the liquid film folded and bulged, and upon reaching the maximum spreading diameter, it maintained equilibrium and did not retract. When isooctane fuel drops impacted the isooctane film, they broke and splashed, with thinner films producing stronger splashes. Additionally, the Weber number of the fuel drops significantly influenced the crown shape and splashing after impact. The impingement behavior of fuel drops on the glycerol solution film was also investigated, focusing on the liquid film morphology after impact. Based on the experimental data, empirical correlations were established between the critical Weber numbers for transitions among different crown morphologies and the dimensionless film thickness under varying film viscosities.
Yang, Tian, Lu, Lili, Guo, Zongwei, Song, Enzhe, Yao, Chong, Ning, Yilin, Ke, Yun
Validation and Exploration of Multimodal Deep-Learning Camera–LiDAR Calibration Models2026-01-50377/13/2026
This paper presents an innovative study in exploring, evaluating, and implementing deep-learning architectures for the calibration of multimodal sensor systems. The aim of this paper is to leverage the use of sensor fusion to achieve dynamic, real-time alignment between 3D LiDAR and 2D camera sensors. Static calibration methods are tedious and time-consuming, which is why we propose utilizing conventional neural networks (CNNs) coupled with geometrically informed learning to solve this issue. We leverage the foundational principles of extrinsic LiDAR–camera calibration tools such as RegNet, CalibNet, and LCCNet by exploring open-source models that are available online and compare our results with their corresponding research papers. Requirements for extracting these visual and measurable outputs involved tweaking source code, fine-tuning, training, validation, and testing of each of these frameworks for equal comparisons. This approach aims to investigate which of these advanced networks produces the most accurate and consistent predictions. Through a series of experiments, we reveal some of their shortcomings and areas for potential improvements. We find that LCCNet yields the best results among all the models that we validated.
Karramreddy, Venkat Sai Raxit, Mitchell, Liam
Risk Assessment after Tin Whisker ObservationARP7505 (Current)7/13/2026
This document contains information and guidance on assessment of the risk posed by observed tin whiskers for aerospace, defense, and high-performance (ADHP) products or other products that demand high reliability.
G-24 Pb-free Risk Management Committee for ADHP
CRIMPING TOOLS, TYPE I, TERMINAL, HAND OR POWER ACTUATED, WIRE TERMINATION, PNEUMATIC TOOL FOR AS22520/7 POSITIONERS, USE FOR WIRE BARREL SIZES 16 AND SMALLERAS22520/49 (Current)7/13/2026
AE-8C2 Terminating Devices and Tooling Committee
CONNECTOR, RECEPTACLE, ELECTRIC, CRIMP TYPE, WALL MOUNTING FLANGE, PUSH-PULL, COUPLING, SERIES 2 (CLASS E AND J)AS31401B (Current)7/13/2026
AE-8C1 Connectors Committee
Automatic Synthetic Road Network Drivability Evaluation2026-01-50427/13/2026
Simulation plays a significant role in the validation and verification of Automated Driving Systems (ADS). In a scenario-based validation strategy, the road and the actions of the traffic participants must be captured in a portable and flexible format for simulation. XML-based parametric models constitute a common combination upon which the static and dynamic aspects of the environment are captured. Although there are plenty of tools for generating these XML files there are few alternatives to verify their content. This paper suggests a method for converting and simplifying a synthetic road network into a graph for which the Chinese Postman Problem is solved. The resulting sequence can be converted back into a route that can be sampled to verify the drivability of the whole network. Once the network is verified, it can be safely used for simulation, increasing the speed at which ADS systems are developed. The graph representation can also be used to provide interactive feedback to LLMs (Large Language Model), which are increasingly used for automatic generation of roads and scenarios.
Vargas Rivero, Jose Roberto, Kern, Andreas, Menken, Stefan, Harth, Michael, Kuipou, Franck Russel
Traffic Flow Prediction System Based on Deep Learning2026-99-05497/10/2026
Traffic flow prediction is of great significance for improving the operation efficiency of the transportation system, optimizing travel experience and reducing traffic congestion. Traditional traffic flow prediction methods are difficult to capture the spatio-temporal nonlinear characteristics of traffic flow due to its simple model and insufficient feature extraction ability. Therefore, an intelligent traffic flow prediction system based on deep learning is proposed, constructs a deep learning model based on graph convolution and fusion of attention mechanism LSTM. Based on this, a traffic flow prediction system is implemented. Experiments show that, on the PeMSD4 and PeMSD4 datasets, the error of the model in RMSE and Mae indicators is significantly reduced compared with the traditional methods, which provides an efficient solution for traffic flow prediction and congestion analysis, and has both theoretical innovation and engineering practical value.
Tang, Zhan, Lu, Xiaoyu, Yang, Nian, Xiang, Xiaohong, Hou, Xiang, Peng, Xiaoli
Research on Battery Environment Heat Dissipation Control System2026-99-05357/10/2026
Batteries generate a large amount of heat during operation, and if it cannot be dissipated in a timely and effective manner, it will seriously affect the performance, lifespan, and even safety of the battery. Therefore, battery heat dissipation has become a key challenge in the development of new energy vehicles. The traditional liquid cooling system has problems such as complex design and control, and the need to improve heat dissipation efficiency. To address these issues, this study proposes an optimized design scheme for battery environment heat dissipation control system based on liquid cooling heat dissipation system. This study first conducted an in-depth analysis of the thermal generation mechanism of lithium-ion batteries and studied existing examples of thermal management schemes. On this basis, an innovative forward and reverse circulation device was designed, combined with a liquid cooling heat dissipation structure. The Keil uVision4 programming software was used to write the microcontroller control program, and the circuit was simulated and verified using Proteus simulation software. This study established an experimental platform and conducted physical testing and thermal imaging detection. By collecting temperature change data under different heat dissipation modes and analyzing the experimental data, the results show that the optimized liquid cooling heat dissipation system significantly improves the heat dissipation efficiency. The system exhibits good performance under different cooling modes.
Ding, Xvqiang, Ni, Yiwei, Gu, Chen, Zhang, Jin, Chen, Mingyang, Jiao, Yunxiao
A Novel Rotating Component Comprehensive Diagnostic Network Based on Stacked Auto Encoders and Sparse Filter Combined with Generative Adversarial Network2026-99-05347/10/2026
Nowadays, the majority of intelligent fault diagnosis approaches are still centered on individual faulty components, while only a limited number of models are capable of performing integrated diagnosis for rotating systems that consist of shafts, bearings, and gears. Under variable-speed operating conditions, the large scale of vibration data further complicates the process of effective feature extraction. To improve these challenges, this study develops a comprehensive diagnostic framework for rotating components, termed WGAN-SAFC. The proposed architecture integrates a Wasserstein Generative Adversarial Network (WGAN) with a hybrid structure of stacked autoencoders and sparse filtering (SAFC). SAFC integrates the feature-learning capability of SAE and the sparsity-driven representation of SF, while incorporating adversarial data generation to address sample imbalance and enhance fault diagnosis performance. Experimental verification on collected vibration datasets demonstrates that WGAN-SAFC achieves superior diagnostic accuracy and robustness compared with existing methods.
Li, Shunming, Feng, Mengqi
Research on the Design of Lane Line Recognition and Deviation Warning System Based on Machine Vision2026-99-05667/10/2026
In order to reduce traffic accidents caused by cars straying from lanes, a lane line recognition and deviation warning system based on machine vision is designed. It mainly includes image preprocessing, lane line detection, and the design of a deviation warning model. “In this study, an ROS-based intelligent vehicle-mounted camera is adopted for road image collection. To reduce the computational load of data processing while guaranteeing the algorithm’s accuracy and reliability, grayscale conversion and region of interest (ROI) extraction are implemented to finish the image preprocessing stage. Additionally, a fusion strategy of global and local thresholds is introduced to enhance both the operational speed and detection accuracy of the algorithm” use the Canny operator for the edge feature extraction; and complete the fitted lane lines with the improved Hough transform. Finally, based on the Kalman filter and camera viewpoint conversion coefficient algorithm, the lane line offset is detected in real time, and the deviation is judged in combination with the monitoring interface. Simulation experiments show that the system is able to effectively recognize the lane line and judge the deviation status under the condition of setting the offset threshold of 70 pixels, which significantly improves the accuracy and real-time performance of the lane deviation warning and provides effective technical support for reducing traffic accidents.
Wang, Xufeng, Zhang, Chunshu, Wang, Yan, Chen, Yihui, Ji, Rui
Simulation Analysis of Pontoon Structure Based on Tunnel Bank Evacuation Channel2026-99-05417/10/2026
To meet the requirements for efficient evacuation during tunnel navigation, the pontoon of the tunnel bank wall evacuation channel in a large-scale navigation building is taken as the research object. The water body and water wave are simulated using the coupled Euler-Lagrangian method and the push-plate wave method, respectively. The water boundary is processed using the viscoelastic artificial boundary method, and a simulation analysis model of the pontoon under the combined action of water waves and load is established. The results show that the average relative vertical displacement of the pontoon is basically the same under the condition of water wave and no water waves, but the fluctuation range of the pontoon is larger under the condition of water waves. When there are water waves and different loads, the maximum Mises stress distribution of the pontoon is essentially the same, and both are less than 80 MPa, meeting the strength requirements and demonstrating the rationality of the pontoon design.
Tang, Weibi, Qin, Pan, Li, Ran, Tao, Ran, Hu, Zhifang
Research on Urban Bus Timetable Optimization Based on Deep Reinforcement Learning2026-99-05387/10/2026
Public transportation serves as a crucial component of urban mobility, contributing to the alleviation of urban congestion, reduction of travel expenses, and mitigation of air pollution. Nonetheless, the dynamic passenger demand and the complex traffic conditions render traditional bus timetables inadequate, leading to ineffective allocation of public transportation resources. Consequently, it is essential to create bus timetables that are responsive to actual traffic scenarios and fluctuating passenger demand. This study regards the bus timetable planning problem as a Markov decision-making process within a discrete time framework, proposing a deep reinforcement learning-based optimization model for bus timetables. In particular, the model is designed to account for both bus companies and passengers, incorporating a state space and reward calculation method that emphasizes passenger comfort. Then Deep Q-Network (DQN) methodology is employed to issue instructions on whether a bus departure at each time, and bus timetable is generated gradually over time. Experimental results indicate that the proposed approach significantly reduces bus travel costs and enhances the overall travel experience for passengers in comparison to traditional methods.
Xu, Jie, Xia, Dong, Yang, Jianxi, Wang, Bing
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