Energy-efficient joint UAV secure communication and 3D trajectory optimization assisted by reconfigurable intelligent surfaces in the presence of eavesdroppers

We consider a scenario where an unmanned aerial vehicle(UAV),a typical unmanned aerial system(UAS),transmits confidential data to a moving ground target in the presence of multiple eavesdroppers.Multiple friendly reconfigurable intelligent surfaces(RISs) help to secure the UAV-target communication and improve the energy efficiency of the UAV.We formulate an optimization problem to minimize the energy consumption of the UAV,subject to the mobility constraint of the UAV and that the achievable secrecy rate at the target is over a given threshold.We present an online planning method following the framework of model predictive control(MPC) to jointly optimize the motion of the UAV and the configurations of the RISs.The effectiveness of the proposed method is validated via computer simulations.

A framework of force of information influence and application for C4KISR system

The subversive nature of information war lies not only in the information itself, but also in the circulation and application of information. It has always been a challenge to quantitatively analyze the function and effect of information flow through command, control, communications, computer, kill, intelligence,surveillance, reconnaissance (C4KISR) system. In this work, we propose a framework of force of information influence and the methods for calculating the force of information influence between C4KISR nodes of sensing, intelligence processing,decision making and fire attack. Specifically, the basic concept of force of information influence between nodes in C4KISR system is formally proposed and its mathematical definition is provided. Then, based on the information entropy theory, the model of force of information influence between C4KISR system nodes is constructed. Finally, the simulation experiments have been performed under an air defense and attack scenario. The experimental results show that, with the proposed force of information influence framework, we can effectively evaluate the contribution of information circulation through different C4KISR system nodes to the corresponding tasks. Our framework of force of information influence can also serve as an effective tool for the design and dynamic reconfiguration of C4KISR system architecture.

Key technologies and applications of intelligent dispatching command for high-speed railway in China

Purpose–The intelligent Central Traffic Control(CTC)system plays a vital role in establishing an intelligent high-speed railway(HSR)system.As the core of HSR transportation command,the intelligent CTC system is a new HSR dispatching command system that integrates the widely used CTC in China with the practical service requirements of intelligent dispatching.This paper aims to propose key technologies and applications for intelligent dispatching command in HSR in China.Design/methodology/approach–This paper first briefly introduces the functions and configuration of the intelligent CTC system.Some new servers,terminals and interfaces are introduced,which are plan adjustment server/terminal,interface for automatic train operation(ATO),interface for Dynamic Monitoring System of Train Control Equipment(DMS),interface for Power Supervisory Control and Data Acquisition(PSCADA),interface for Disaster Monitoring,etc.Findings–The key technologies applied in the intelligent CTC system include automatic adjustment of train operation plans,safety control of train routes and commands,traffic information data platform,integrated simulation of traffic dispatching and ATO function.These technologies have been applied in the Beijing-Zhangjiakou HSR,which commenced operations at the end of 2019.Implementing these key intelligent functions has improved the train dispatching command capacity,ensured the safe operation of intelligent HSR,reduced the labor intensity of dispatching operators and enhanced the intelligence level of China’s dispatching system.Originality/value–This paper provides further challenges and research directions for the intelligent dispatching command of HSR.To achieve the objectives,new measures need to be conducted,including the development of advanced technologies for intelligent dispatching command,coping with new requirements with the development of China’s railway signaling system,the integration of traffic dispatching and train control and the application of AI and data-driven modeling and methods.

Experimental Study of a Modified Command Governor Adaptive Controller for Depth Control of an Unmanned Underwater Vehicle

Command governor–based adaptive control(CGAC)is a recent control strategy that has been explored as a possible candidate for the challenging task of precise maneuvering of unmanned underwater vehicles(UUVs)with parameter variations.CGAC is derived from standard model reference adaptive control(MRAC)by adding a command governor that guarantees acceptable transient performance without compromising stability and a command filter that improves the robustness against noise and time delay.Although simulation and experimental studies have shown substantial overall performance improvements of CGAC over MRAC for UUVs,it has also shown that the command filter leads to a marked reduction in initial tracking performance of CGAC.As a solution,this paper proposes the replacement of the command filter by a weight filter to improve the initial tracking performance without compromising robustness and the addition of a closed-loop state predictor to further improve the overall tracking performance.The new modified CGAC(M-CGAC)has been experimentally validated and the results indicate that it successfully mitigates the initial tracking performance reduction,significantly improves the overall tracking performance,uses less control force,and increases the robustness to noise and time delay.Thus,M-CGAC is a viable adaptive control algorithm for current and future UUV applications.

Nonlinear Intelligent Flight Control for Quadrotor Unmanned Helicopter

Quadrotor unmanned helicopter is a new popular research platform for unmanned aerial vehicle(UAV),thanks to its simple construction,vertical take-off and landing(VTOL)capability.Here a nonlinear intelligent flight control system is developed for quadrotor unmanned helicopter,including trajectory control loop composed of co-controller and state estimator,and attitude control loop composed of brain emotional learning(BEL)intelligent controller.BEL intelligent controller based on mammalian middle brain is characterized as self-learning capability,model-free and robustness.Simulation results of a small quadrotor unmanned helicopter show that the BEL intelligent controller-based flight control system has faster dynamical responses with higher precision than the traditional controller-based system.

Flight Control System of Unmanned Aerial Vehicle

To date unmanned aerial system(UAS)technologies have attracted more and more attention from countries in the world.Unmanned aerial vehicles(UAVs)play an important role in reconnaissance,surveillance,and target tracking within military and civil fields.Here one briefly introduces the development of UAVs,and reviews its various subsystems including autopilot,ground station,mission planning and management subsystem,navigation system and so on.Furthermore,an overview is provided for advanced design methods of UAVs control system,including the linear feedback control,adaptive and nonlinear control,and intelligent control techniques.Finally,the future of UAVs flight control techniques is forecasted.

Optimal Distribution of N-Team Interacting Decision Makers with Hierarchical Command Inputs That Are Predicated on Order Statistics

The spectral analysis of simulated N-team of interacting decision makers with bounded rationality constraints of Oladejo, which assumes triangular probability density function of command inputs is hereby restructured and analysed, to have hierarchical command inputs that are predicated on order statistics distributions. The results give optimal distributions.

无人空战指控建模仿真方法研究

针对当前无人空战仿真中,缺乏支持全流程作战指挥控制建模与仿真这一问题,结合当前无人空战仿真系统相关研究,系统梳理和研究指控模型构建方法,通过探索不同仿真层级不同类型的无人机指挥控制建模原理和方法,设计并且构建了一套适合指挥信息系统装备特点的指控模型仿真系统。通过进行符合预设约束的想定设定,对目标作战任务进行了效能评估和对比,可为无人空战相关指挥信息系统装备的论证、评估方式提供理论和现实指导。

基于VRX的无人水面船智能控制虚拟仿真实验平台设计

船舶智能控制课程实践环节通常需要高投入且具有较高风险,虚拟仿真技术可以为学生提供了一个低成本、无风险的学习环境。该研究设计了一套基于VRX的无人水面船智能控制虚拟仿真实验平台,构建了无人水面船模型的基本结构并确定控制算法与无人船的通信方案;针对所使用的无人船建立其运动学模型和动力学模型,并根据实验结果对模型进行修正;设计了基于反步法的轨迹跟踪控制器,对算法的轨迹跟踪效果进行系统测试。通过三维虚拟场景展示了无人船跟踪航行过程,运行效果表明该虚拟仿真实验平台为学生提供了一个安全便捷的实践应用平台,有助于激发学生的学习兴趣。

面向易行的高速铁路智能平行调度系统构架及关键技术

针对我国高铁成网运行、管控列车数量和种类多、运营方式复杂等特点,以构建新一代智能动态调度系统为目标,探讨面向易行的高速铁路智能平行调度系统构架,从5个方面提炼出高速铁路智能平行调度系统的技术特征。总结目前高速铁路智能平行调度系统研究亟待解决的关键技术,介绍智能高铁平行调度一体化仿真原型系统研制及实现的部分场景功能。研究工作可为建立面向易行服务的,适应中国高铁“效率变革”“质量变革”需求的智能动态调度理论与技术体系,实现我国高速铁路智能调度系统和运输服务水平的升级发展提供参考。

跨域杀伤网架构及其技术分析

在全域作战理论推动下,杀伤链正向更加复杂、更加多维的杀伤网发展。从作战概念入手,分析了杀伤网的运行特点,针对全域作战中跨域杀伤网构建问题,提出了体系能力服务平台+作战应用的系统架构,设计了相关智能体的职责分配和信息交互方法;从杀伤网构建和效能优化的角度,提出了基于作战资源可识别、可定义及可组合的能力生成机理,并对作战资源调用的合作博弈机制进行了探讨。最后,从技术实现角度,提出了智能体构建及智能体参数选择的方法,并分析了人工智能技术在跨域杀伤网构建过程中的赋能作用。

不平衡数据下基于SVM增量学习的指挥信息系统状态监控方法

针对指挥信息系统历史状态样本有限的特点,基于支持向量机(support vector machines,SVM)设计了一种面向不平衡数据的SVM增量学习方法。针对系统正常/异常状态样本不平衡的情况,首先利用支持向量生成一部分新样本,然后通过分带的思想逐带产生分布更加均匀的新样本以调节原样本集的不平衡比。针对系统监控实时性要求高且在运行过程中会有新样本不断加入的特点,采用增量学习的方式对分类模型进行持续更新,在放松KKT(Karush-Kuhn-Tucker)更新触发条件的基础上,通过定义样本重要度并引入保留率和遗忘率的方式减少了增量学习过程中所需训练的样本数量。为了验证算法的有效性和优越性,实验部分在真实系统中获得的数据集以及UCI数据集中3类6组不平衡数据集中与现有的算法进行了对比。结果表明,所提算法能够有效实现对不平衡数据的增量学习,从而满足指挥信息系统状态监控的需求。

城市智慧排水管控中多元监测体系构建研究

城市河流与排水管网污染溯源的时效性差与监测体系空间覆盖不平衡等仍是城市水环境管理的突出问题。随着无人机、物联网、水质在线监测、高精度定位等技术的发展,实现快速污染溯源的多元监测体系正逐步实现。该研究通过对中山市濠头涌区域的排水管网关键节点与河流关键断面安装分布式检测仪表,并应用无人监测船对城市河流进行地形测量、水质监测,完成了监测体系的构建和多元监测数据的获取,实现了分布式仪表与无人监测船的优势互补。此外,基于电导率与氨氮相关性系数大于0.92的结果及氨氮作为主要污染物的现状,验证了电导率作为主要水质实时监测指标的合理性。

预警机装备形态演变的新特征

自1945年投入使用以来,预警机装备经过近80年的发展,其在作战体系和杀伤链构建中,经历了从低空补忙雷达向空中C2节点的转变。现今,一方面,有人预警机的核心节点作用愈发凸显,主要担负空战场管理任务;另一方面,无人化预警机形态正在加速出现,重点发挥预警探测而不是指挥控制作用,作为有人预警机的重要补充。在产品形态上,未来的预警机将具备敏捷性、协同性以及自主性等重要特征。

无人集群系统深度强化学习控制研究进展

随着无人集群在物流运输、农业管理、军事行动等场景的试验和应用,其面临的作业环境和任务内容日趋复杂,亟需设计效率更高、泛化能力更强、适应性更好的控制算法.将人工智能引入到无人集群系统控制的研究中,能够大幅提升现有无人集群的能力,完成复杂的作业任务.深度强化学习具有深度学习和强化学习的优点,无人集群系统深度强化学习控制研究受到了国内外科研人员的广泛关注,涌现出许多标志性成果.本文将从原理、特点等方面阐述深度强化学习概念,深入分析深度强化学习的多种典型算法,并讨论无人机集群的各类控制需求,进而介绍深度强化学习在无人机集群控制领域的典型研究成果,最后针对该领域研究成果的落地转化总结了应用前景和面临的挑战.

中大型无人船舶自主控制系统架构设计及实现

[目的]自主控制系统作为无人船舶的核心系统,负责协调全船其他系统,共同完成各项既定使命任务,是无人船舶技术的重要研究方向。[方法]首先,借鉴主导人类智能的“神经系统”的生理学结构和认知机制,归纳出对于无人船舶自主控制系统架构仿生设计的6点启示。然后,基于这些启示,从“中枢神经系统”和“周围神经系统”两个维度,研究自主控制系统的物理架构及其典型实现。最后,借鉴人类智能的信息处理流程,提出8大功能模块构成的自主控制系统的功能架构以及面向服务架构(SOA)的设计思路。[结果]经研究得到了无人船舶自主控制系统物理架构和功能架构,[结论]其可为中大型无人船舶自主控制系统的总体架构、系统设计与产品研制提供路线参考。

智能化分布式指挥与控制建设及关键技术应用

为厘清智能化分布式指挥与控制体系的发展态势及其关键技术,基于系统工程思维,通过收集开源情报、整合体系架构与建设进展等方式,对美军典型案例进行深入研究。同时,对其在特定作战场景中的应用进行阐释。结果表明,面向未来大规模、快节奏、多样化的战争,美军通过构建联合战术网、布局无人控制平台、研发智能决策辅助系统的方式增强作战指挥与控制的智能性。通过具体案例分析可知,分布式指挥与控制的智能化主要依赖边缘云、人工智能、无人自主系统及先进网络通信等关键技术。

无人机集群空中博弈对抗体系指挥控制模型

针对无人机集群空中博弈对抗体系的指挥控制问题,系统阐述了无人机集群协同空战典型场景的作战过程模型,分析设计了空中无人机集群博弈对抗体系的作战任务剖面,构建了空中无人机集群协同作战体系信息流程模型。研究结论能够为空中无人作战体系构建、无人集群协同空战指挥控制架构以及协同作战规划设计方法等研究提供理论支撑。

基于智能预测控制的鱼雷状小型无人艇轨迹跟踪研究

[目的]针对无人艇(USV)在狭窄湖泊、涵洞作业时存在精度保持难和航迹控制难的问题,以自主研制的一款鱼雷状小型USV为对象,提出一种轨迹跟踪智能预测控制方法。[方法]首先,构建自主研制的欠驱动USV非线性状态空间模型;然后,设计智能预测控制器,该控制器基于模型预测控制的设计思想并结合改进的粒子群算法,在线决策、优化每一时刻的性能指标并纠正预测状态;最后,开展仿真和湖试试验测试系统对参考轨迹的跟踪性能,并与线性模型预测控制器的跟踪性能进行比较。[结果]结果表明,所设计的智能预测控制器超调小、抗干扰性好。[结论]所提方法不仅能运用于鱼雷状小型USV跟踪系统,也能对其他USV跟踪系统起到很好的借鉴作用。

考虑输入饱和的无人艇神经网络命令滤波反步控制

针对具有输入饱和和未知非线性的无人水面艇(USV)系统的轨迹跟踪控制问题,提出一种基于命令滤波反步的自适应轨迹跟踪控制策略。利用径向基函数神经网络(RBFNN)逼近系统中的未知动态,减少了需要设计的自适应律数量,降低了系统的复杂度,并基于反步法设计了系统的控制律;通过引入命令滤波器及其补偿机制,解决了计算复杂的问题,消除了滤波误差的影响,提高了系统的控制精度;最后仿真验证了所提方法的有效性。