Safety Assessment of Liquid Launch Vehicle Structures Based on Interpretable Belief Rule Base

A liquid launch vehicle is an important carrier in aviation,and its regular operation is essential to maintain space security.In the safety assessment of fluid launch vehicle body structure,it is necessary to ensure that the assessmentmodel can learn self-response rules from various uncertain data and not differently to provide a traceable and interpretable assessment process.Therefore,a belief rule base with interpretability(BRB-i)assessment method of liquid launch vehicle structure safety status combines data and knowledge.Moreover,an innovative whale optimization algorithm with interpretable constraints is proposed.The experiments are carried out based on the liquid launch vehicle safety experiment platform,and the information on the safety status of the liquid launch vehicle is obtained by monitoring the detection indicators under the simulation platform.The MSEs of the proposed model are 3.8000e-03,1.3000e-03,2.1000e-03,and 1.8936e-04 for 25%,45%,65%,and 84%of the training samples,respectively.It can be seen that the proposed model also shows a better ability to handle small sample data.Meanwhile,the belief distribution of the BRB-i model output has a high fitting trend with the belief distribution of the expert knowledge settings,which indicates the interpretability of the BRB-i model.Experimental results show that,compared with other methods,the BRB-i model guarantees the model’s interpretability and the high precision of experimental results.

A Lyapunov-based three-axis attitude intelligent control approach for unmanned aerial vehicle

A novel Lyapunov-based three-axis attitude intelligent control approach via allocation scheme is considered in the proposed research to deal with kinematics and dynamics regarding the unmanned aerial vehicle systems.There is a consensus among experts of this field that the new outcomes in the present complicated systems modeling and control are highly appreciated with respect to state-of-the-art.The control scheme presented here is organized in line with a new integration of the linear-nonlinear control approaches,as long as the angular velocities in the three axes of the system are accurately dealt with in the inner closed loop control.And the corresponding rotation angles are dealt with in the outer closed loop control.It should be noted that the linear control in the present outer loop is first designed through proportional based linear quadratic regulator(PD based LQR) approach under optimum coefficients,while the nonlinear control in the corresponding inner loop is then realized through Lyapunov-based approach in the presence of uncertainties and disturbances.In order to complete the inner closed loop control,there is a pulse-width pulse-frequency(PWPF) modulator to be able to handle on-off thrusters.Furthermore,the number of these on-off thrusters may be increased with respect to the investigated control efforts to provide the overall accurate performance of the system,where the control allocation scheme is realized in the proposed strategy.It may be shown that the dynamics and kinematics of the unmanned aerial vehicle systems have to be investigated through the quaternion matrix and its corresponding vector to avoid presenting singularity of the results.At the end,the investigated outcomes are presented in comparison with a number of potential benchmarks to verify the approach performance.

Synthetical Efficiency-based Optimization for the Power Distribution of Power-split Hybrid Electric Vehicles

Now the optimization strategies for power distribution are researched widely, and most of them are aiming to the optimal fuel economy and the driving cycle must be preknown. Thus if the actual driving condition deviates from the scheduled driving cycle, the effect of optimal results will be declined greatly. Therefore, the instantaneous optimization strategy carried out on-line is studied in this paper. The power split path and the transmission efficiency are analyzed based on a special power-split scheme and the efficiency models of the power transmitting components are established. The synthetical efficiency optimization model is established for enhancing the transmission efficiency and the fuel economy. The identification of the synthetical efficiency as the optimization objective and the constrain group are discussed emphatically. The optimization is calculated by the adaptive simulated annealing （ASA） algorithm and realized on-line by the radial basis function （RBF）-based similar models. The optimization for power distribution of the hybrid vehicle in an actual driving condition is carried out and the road test results are presented. The test results indicate that the synthetical efficiency optimization method can enhance the transmission efficiency and the fuel economy of the power-split hybrid electric vehicle （HEV） observably. Compared to the rules-based strategy the optimization strategy is optimal and achieves the approximate global optimization solution for the power distribution. The synthetical efficiency optimization solved by ASA algorithm can give attentions to both optimization quality and calculation efficiency, thus it has good application foreground for the power distribution of power-split HEV.

A 3D Geometry-Based Scattering Model for Vehicleto-Vehicle Wideband MIMO Relay-Based Cooperative Channels

In this paper, a three-dimensional(3D) geometry- based stochastic scattering model(GBSSM) for wideband multi-input multi-output(MIMO) vehicle-to-vehicle(V2V) relay-based cooperative fading channel based on geometrical three-cylinder is proposed. Non-line-of-sight(NLOS) propagation condition is assumed in amplify-and-forward(AF) cooperative networks from the source mobile station(S) to the destination mobile station(D) via the mobile relay station(R). We extend the proposed narrowband model to wideband and also introduce the carrier frequency and bandwidth into the model. To avoid complicated procedure in deriving the analytical expressions of the channel parameters and functions, the channel is realized first. By using the realized channel matrix, the channel properties are further investigated.

Automatic Carrier Landing Control for Unmanned Aerial Vehicles Based on Preview Control

For carrier-based unmanned aerial vehicles(UAVs),one of the important problems is the design of an automatic carrier landing system(ACLS)that would enable the UAVs to accomplish autolanding on the aircraft carrier.However,due to the movements of the flight deck with six degree-of-freedom,the autolanding becomes sophisticated.To solve this problem,an accurate and effective ACLS is developed,which is composed of an optimal preview control based flight control system and a Kalman filter based deck motion predictor.The preview control fuses the future information of the reference glide slope to improve landing precision.The reference glide slope is normally a straight line.However,the deck motion will change the position of the ideal landing point,and tracking the ideal straight glide slope may cause landing failure.Therefore,the predictive deck motion information from the deck motion predictor is used to correct the reference glide slope,which decreases the dispersion around the desired landing point.Finally,simulations are carried out to verify the performance of the designed ACLS based on a nonlinear UAV model.

Parallel Neural Network-Based Motion Controller for Autonomous Underwater Vehicles

A parallel neural network-based controller （PNNC） is presented for the motion control of underwater vehicles in this paper. It consists of a real-time part, a self-learning part and a desired-state programmer, and it is different from normal adaptive neural network controller in structure. Owing to the introduction of the self-learning part, on-line learning can be performed without sample data in several sample periods, resulting in high learning speed of the controller and good control performance. The desired-state programmer is utilized to obtain better learning samples of the neural network to keep the stability of the controller. The developed controller is applied to the 4-degree of freedom control of the AUV “IUV- IV” and is successful on the simulation platform. The control performance is also compared with that of neural network controller with different structures such as normal adaptive neural network and different learning methods. Current effects and surge velocity control are also included to demonstrate the controller＇ s performance. It is shown that the PNNC has a great possibility to solve the problems in the control system design of underwater vehicles.

Vehicle-Bridge Interaction Simulation and Damage Identification of a Bridge Using Responses Measured in a Passing Vehicle by Empirical Mode Decomposition Method

To prevent early bridge failures, effective Structural Health Monitoring (SHM) is vital. Vibration-based damage assessment is a powerful tool in this regard, as it relies on changes in a structure’s dynamic characteristics as it degrades. By measuring the vibration response of a bridge due to passing vehicles, this approach can identify potential structural damage. This dissertation introduces a novel technique grounded in Vehicle-Bridge Interaction (VBI) to evaluate bridge health. It aims to detect damage by analyzing the response of passing vehicles, taking into account VBI. The theoretical foundation of this method begins with representing the bridge’s superstructure using a Finite Element Model and employing a half-car dynamic model to simulate the vehicle with suspension. Two sets of motion equations, one for the bridge and one for the vehicle are generated using the Finite Element Method, mode superposition, and D’Alembert’s principle. The combined dynamics are solved using the Newmark-beta method, accounting for road surface roughness. A new approach for damage identification based on the response of passing vehicles is proposed. The response is theoretically composed of vehicle frequency, bridge natural frequency, and a pseudo-frequency component related to vehicle speed. The Empirical Mode Decomposition (EMD) method is applied to decompose the signal into its constituent parts, and damage detection relies on the Intrinsic Mode Functions (IMFs) corresponding to the vehicle speed component. This technique effectively identifies various damage scenarios considered in the study.

Biogeography-Based Combinatorial Strategy for Efficient Autonomous Underwater Vehicle Motion Planning and Task-Time Management

Autonomous Underwater Vehicles （AUVs） are capable of conducting various underwater missions and marine tasks over long periods of time. In this study, a novel conflict-free motion-planning framework is introduced. This framework enhances AUV mission performance by completing the maximum number of highest priority tasks in a limited time through a large-scale waypoint cluttered operating field and ensuring safe deployment during the mission. The proposed combinatorial route-path-planner model takes advantage of the Biogeography- Based Optimization （BBO） algorithm to satisfy the objectives of both higher- and lower-level motion planners and guarantee the maximization of mission productivity for a single vehicle operation. The performance of the model is investigated under different scenarios, including cost constraints in time-varying operating fields. To demonstrate the reliability of the proposed model, the performance of each motion planner is separately assessed and statistical analysis is conducted to evaluate the total performance of the entire model. The simulation results indicate the stability of the proposed model and the feasibility of its application to real-time experiments.

Cloud Control System Architectures,Technologies and Applications on Intelligent and Connected Vehicles:a Review

The electrification of vehicle helps to improve its operation efficiency and safety.Due to fast development of network,sensors,as well as computing technology,it becomes realizable to have vehicles driving autonomously.To achieve autonomous driving,several steps,including environment perception,path-planning,and dynamic control,need to be done.However,vehicles equipped with on-board sensors still have limitations in acquiring necessary environmental data for optimal driving decisions.Intelligent and connected vehicles(ICV)cloud control system(CCS)has been introduced as a new concept as it is a potentially synthetic solution for high level automated driving to improve safety and optimize traffic flow in intelligent transportation.This paper systematically investigated the concept of cloud control system from cloud related applications on ICVs,and cloud control system architecture design,as well as its core technologies development.Based on the analysis,the challenges and suggestions on cloud control system development have been addressed.

Human Memory/Learning Inspired Control Method for Flapping-Wing Micro Air Vehicles

The problem of flapping motion control of Micro Air Vehicles (MAVs) with flapping wings was studied in this paper.Based upon the knowledge of skeletal and muscular components of hummingbird, a dynamic model for flapping wing wasdeveloped.A control scheme inspired by human memory and learning concept was constructed for wing motion control ofMAVs.The salient feature of the proposed control lies in its capabilities to improve the control performance by learning fromexperience and observation on its current and past behaviors, without the need for system dynamic information.Furthermore,the overall control scheme has a fairly simple structure and demands little online computations, making it attractive for real-timeimplementation on MAVs.Both theoretical analysis and computer simulation confirms its effectiveness.

Localization and Evaluation of Motor Vehicle Emission Simulator (MOVES) Model for Predicting the Emissions of Private Cars:a Case Study of Shenzhen City,China

Motor Vehicle emission simulator(MOVES)model was localized by changing the base emission rates in MySQL database of the model,and using the actual measured data for private cars in Shenzhen City,China.The performances of localized MOVES model and non-localized MOVES model were evaluated by comparing the predicted emission factors to the measured ones.The results showed that by localization of the base emission rates,the prediction accuracy of the localized MOVES model for hydrocarbon(HC)and nitrogen oxides(NO x)was significantly improved.The accuracy of the localized MOVES model simulations in the Opmode increased by 86%,88%and 71%for HC,76%,42%and 72%for NO x on arterial roads,expressways and highways.For carbon monoxide(CO),however,the simulation performance based on the average velocity mode on expressways and highways became poor after localization,with the decrease of 28%and 8%respectively.Overall,by the localization of the base emission rates,the relative errors of the simulated emission factors of HC,CO and NO x of private cars were less than 37%.

Boosted Vehicle Detection Using Local and Global Features

This study presents a boosted vehicle detection system. It first hypothesizes potential locations of vehicles to reduce the computational costs by a statistic of the edge intensity and symmetry, then verifies the accuracy of the hypotheses using AdaBoost and Probabilistic Decision-Based Neural Network (PDBNN) classifiers, which exploit local and global features of vehicles, respectively. The combination of 2 classifiers can be used to learn the complementary relationship between local and global features, and it gains an extremely low false positive rate while maintaining a high detection rate. For the MIT Center for Biological & Computational Learning (CBCL) database, a 96.3% detection rate leads to a false alarm rate of approximately 0.0013%. The objective of this study is to extract the characteristic of vehicles in both local- and global-orientation, and model the implicit invariance of vehicles. This boosted approach provides a more effective solution to handle the problems encountered by conventional background-based detection systems. The experimental results of this study prove that the proposed system achieves good performance in detecting vehicles without background information. The implemented system also extract useful traffic information that can be used for further processing, such as tracking, counting, classification, and recognition.

Highway Toll Collection Method for Connected Automated Vehicle Platooning Using Spatio-Temporal Grid Reservation

In the intelligent transportation system, the autonomous vehicle platoon is a promising concept for addressing traffic congestion problems. However, under certain conditions, the platoon’s advantage cannot be properly developed, especially when stopping for electronic toll collection (ETC) to pay the toll fee using the highway. This study proposes a software architectural platform that enables connected automated vehicles to reserve a grid-based alternative approach to replace current highway toll collection systems. A planned travel route is reserved in advance by a connected automated vehicle in a platoon, and travel is based on reservation information. We use driving information acquired by communication mechanisms installed in connected automated vehicles to develop a dynamic map platform that collects highway toll tax based on reserving spatio-temporal grids. Spatio-temporal sections are developed by dividing space and time into equal grids and assigning a certain road tax rate. The results of the performance evaluation reveal that the proposed method appropriately reserves the specified grids and collects toll taxes accurately based on a spatio-temporal grid with minimal communication time and no data package loss. Likely, using the proposed method to mediate driving on a one-kilometer route takes an average of 36.5 seconds, as compared to ETC and the combination of ETC and freeway road lane methods, which take 46.6 and 53.8 seconds, respectively, for 1000 vehicles. Consequently, our proposed method’s travel time improvements will reduce congestion by more effectively exploiting road capacity as well as enhance the number of platoons while providing non-stoppable travel for autonomous vehicles.

典型海上热发射动力学效应及影响因素分析

考虑海上发射环境特殊性,分析了发射船承受的典型海浪以及风环境,针对陆基发射时运载火箭直接坐落在发射台上的裸箭热发射方式,分析了该支撑形式在海上动态环境下的射前稳定性,获得海上裸箭热发射方式的承受晃动极限。为解决陆基热发射支撑方式不满足三级海况发射稳定性的问题,提出运载火箭的框架式扶稳措施,通过框架内的柔性支撑保证运载火箭的射前稳定性。构建由火箭、等效甲板、导向框架、导轨、柔性支撑、发射台等组成的发射系统动力学仿真模型,开展复杂海况条件下的发射动力学分析,获得了框架式热发射方式在典型海况条件下,不同因素对火箭出框过程飞行姿态、安全间隙等影响规律,可为运载火箭框架式热发射的海上动基座发射动力学安全性评估提供技术支撑,并用于框架式热发射方式的海上动基座环境适应性的综合评价。

航母舰载无人机全自动着舰技术特点分析

全自动着舰技术是无人机上舰必须解决的“使能”技术。2013年,美军已完成X-47B无人机验证机在航母上的全自动着舰试验验证,为其发展MQ-25A“黄貂鱼”航母舰载无人机装备奠定了良好的技术基础。在梳理飞机着陆与着舰差异的基础上,给出航母舰载无人机全自动着舰的通用流程;从地位作用、工作范围、系统构架、系统要求、实现手段以及关键技术支撑等角度,对比有人机重点分析了航母舰载无人机全自动着舰的特点,为后续关键技术研究和武器装备建设发展提供参考。

舰载航空飞行器气动技术发展趋势研究

针对舰载航空飞行器的应用背景和发展现状,综述了舰载航空飞行器的主要类型及其存在的特殊空气动力学问题,研究了美国在解决舰载航空飞行器空气动力学问题方面的方法,阐述了利用数值仿真、地面试验、飞行试验三种研究手段所开展的具体研究工作和获得的研究成果,展望了舰载航空飞行器气动技术的未来发展趋势,主要包括在气动布局技术上将重点发展高升力构型和无尾飞翼构型,在流动控制技术上将综合考虑主动流动控制技术与被动流动控制技术的结合,在地面试验技术上将结合无人机的试验需求发展多空间维度的试验技术,在飞行控制技术上将根据悬停能力、高速飞行、自主着舰、编队飞行等任务需求发展多项新型飞行控制技术。

基于分组教与学的无人战斗机自适应路径规划

针对无人战斗机(unmanned combat air vehicle,UCAV)处于存在威胁区域的战场中路径规划问题,提出一种基于分组教与学算法的UCAV自适应路径规划方法。通过分析UCAV路径评价指标,提出一种自适应的UCAV路径评价模型,根据作战环境规划出距离短、威胁小的任务路径。针对教与学算法寻优精度低、耗时长的问题,提出一种分组教与学算法,引入动态分组和高斯分布扰动策略,提高算法寻优性能。通过仿真实验,该方案求解的最优路径更短且安全。

车辆道路行驶阻力国六测试方法分析比对

依据现行国家标准GB 18352.6—2016《轻型汽车污染物排放限值及测量方法(中国第六阶段)》的规定要求,通过三种方法(固定式风速仪滑行法、车载风速仪滑行法、基于车辆参数计算默认道路载荷法)获取测试样车的车辆道路行驶阻力,模拟出滑行阻力系数,用于后续对轻型混合动力电动汽车能量消耗量进行测试,进而分析优化汽车燃油消耗量和汽车续驶里程。测试结果表明,采用车载风速仪滑行法和固定式风速仪滑行法对样车进行摸底试验,获得的车辆道路行驶阻力能有效模拟出道路阻力系数,优化OVC-HEV测试数据。

地下车辆基地运用库内防火墙设置必要性研究

车辆基地由地上转入地下,疏散救援和排烟难度增大,规范要求室内最远一点至最近安全出口的疏散距离不应大于60 m,实际工程中常在运用库内设置防火墙并在墙上设置防火门作为安全出口,以满足规范要求。然而,设置防火墙在增加建设成本的同时,将原本通透开阔的大空间划分为多个相对独立的小空间,影响车辆基地的使用功能;同时,车辆基地工作人员少,烟气在大空间内沉降慢,对人员的安全疏散影响较小。因此,地下车辆基地防火墙设置的必要性有待进一步研究。基于经过试验验证的车辆基地火灾动力学模型和人员疏散模型,研究防火墙设置对人员疏散安全性、烟气蔓延和火灾蔓延的影响。研究结果表明:运用库内不设置防火墙时,烟气在库内大空间沉降速度慢,即使排烟系统失效,烟气也不会下降至最小清晰高度;设置防火墙后,火灾烟气更易蔓延至垂直于列车轨道的消防车道。无防火墙且排烟系统失效时,人员必需安全疏散时间为346 s,小于最不利点可用安全疏散时间610 s,库内人员均能安全疏散。火源相邻列车所受辐射热通量为3.72 kW/m^(2),小于列车着火所需临界热通量,火灾不会蔓延至火源相邻列车。综合考虑防火墙设置对人员疏散安全性、烟气蔓延和火灾蔓延的影响,地下车辆基地运用库内可不设置防火墙。

基于时空关联的车联网假位置筛选算法

目前,车联网位置隐私保护方法没有充分考虑到不同的时间段内假位置查询概率的变化,同时忽视了对于连续时间背景下同一位置单元每天的用户访问量变化趋势有较大差异的问题。针对上述缺陷,提出空间敏感度度量标准,在选择假位置时综合考虑位置语义相似度提出语义-空间敏感度期望的筛选指标,生成满足分时间段查询概率、匿名区域面积要求的假位置集合。实验结果证明了新算法的可行性及有效性,能够适应当下的车联网位置隐私保护需求。