Digital Control of Dynamic Systems 动态系统的数字控制实验答案

汽车工程Automotive Engineering2020年(第42卷)第12期2020(Vol.42)No.12 doi：10.19562/j.chinasae.qcgc.2020.12.001智能网联汽车云控系统及其实现李克强1,常雪阳「，李家文2,许庆1,高博麟「，潘济安1(1.清华大学，汽车安全与节能国家重点实验室，北京100084； 2.启迪云控(北京)科技有限公司，北京100084)［摘要］本文中提岀了基于信息物理系统(cyber-physical system,CPS)理论的智能网联汽车云控系统概念，该系统利用新一代信息与通信技术，将人、车、路、云的物理层、信息层、应用层连为一体，进行融合感知、决策与控制，可实现车辆行驶和交通运行安全、效率等性能的综合提升。

在介绍系统架构、工作原理与关键技术的基础上，研究了边缘云上融合感知技术与时变时延下车辆控制技术,开发了面向真实道路的云控系统。

通过仿真与道路试验，验证了系统的云端计算、融合感知、融合决策与网联控制的性能,展示了系统实际应用的可行性与先进性。

关键词：智能网联汽车；云控系统;信息物理系统;融合感知；网联车辆控制Cloud Control System for Intelligent and Connected Vehicles and Its ApplicationLi Keqiang1,Chang Xueyang1,Li Jiawen2,Xu Qing1,Gao Bolin1&Pan Jian11.Tsinghua University,State Key厶aboratory of Automotive Safety and Energy,Beijing100084；2.TUS Cloud Control(Beijing)Technology Co.,Ltd.,Beijing100084[Abstract]In this paper,the concept of cloud control system for intelligent and connected vehicles is pro­posed based on the theory of cyber-physical system(CPS).The system uses the new generation of information and communication technologies to integrate the physical layer,cyber layer and application layer of human,vehicles, road infrastructures and cloud for integrated perception,decision-making and control to realize comprehensive im­provement of vehicles and traffic safety and efficiency.Based on the introduction of the system architecture,working principle and key technologies,integrated perception technology on edge cloud and vehicle control technology under time-varying delay are studied.Furthermore,a cloud control system for real road is developed.Simulation and field test results verify the performance of cloud computing,integrated perception,decision-making,and connected con­trol of the proposed system,which demonstrates its feasibility and superiority in application.Keywords:intelligent connected vehicles；cloud control system；cyber-physical system；integrated per­ception；networked vehicle control前言自动驾驶是汽车与交通领域的颠覆性技术，正引发学术界和工业界开展广泛且深入的研究。

《自动控制原理》课程主要参考教材自动控制原理(第四版)【作者】胡寿松【出版社】科学出版社【出版时间】2001.2【内容简介】本书系《自动控制原理》一书的第四版，比较全面地阐述了自动控制的基本理论与应用。

全书共分十章，前八章着重介绍经典控制理论及应用，后两章介绍现代控制理论中的线性系统理论和最优控制理论。

本书精选了第三版中的主要内容，加强了对基本理论及其应用的阐述。

书中深入浅出地介绍了自动控制的基本概念，控制系统在时域和复域中的数学模型及其结构图和信号流图；比较全面地阐述了线性控制系统的时域分析法、根轨迹法、频域分析法以及校正和设计等方法；对线性离散系统的基础理论、数学模型、稳定性及稳态误差、动态性能分析以及数字校正等问题，进行了比较详细的讨论；在非线性控制系统分析方面，给出了相平面和描述函数两种常用的分析方法，对目前应用日益增多的非线性控制的逆系统方法也作了较为详细的介绍；最后两章根据高新技术发展的需要系统地阐述了线性系统的状态空间分析与综合，以及动态系统的最优控制等方法。

书末给出的两个附录，可供读者在学习本书的过程中查询之用。

本书1985年被评为航空工业部优秀教材，1988年被评为全国优秀教材，1997年被评为国家级教学成果二等奖，同年被批准列为国家“九五”重点教材。

本书可作为高等工业院校自动控制、工业自动化、电气自动化、仪表及测试、机械、动力、冶金等专业的教科书，亦可供从事自动控制类的各专业工程技术人员自学参考。

自动控制原理(第五版)【作者】胡寿松【出版社】科学出版社【出版时间】2007.6【内容简介】《自动控制原理》(第5版)精选了第四版中的主要内容，加强了对基本理论及其工程应用的阐述。

书中深入浅出地介绍了自动控制的基本概念，控制系统在时域和复域中的数学模型及其结构图和信号流图；比较全面地阐述了线性控制系统的时域分析法、根轨迹法、频域分析法以及校正和设计等方法；对线性离散系统的基础理论、数学模型、稳定性及稳态误差、动态性能分析以及数字校正等问题，进行了比较详细的讨论；在非线性控制系统分析方面，给出了相平面和描述函数两种常用的分析方法，对目前应用日益增多的非线性控制的逆系统方法也作了较为详细的介绍；最后两章根据高新技术发展的需要，系统地阐述了线性系统的状态空间分析与综合，以及动态系统的最优控制等方法。

Intelligent Control Systems Intelligent Control Systems are a crucial aspect of modern technological advancements, playing a significant role in various industries such as automotive, aerospace, manufacturing, and robotics. These systems are designed to autonomously make decisions and control processes, ultimately improving efficiency, accuracy, and productivity. However, they also present a unique set of challenges and considerations that need to be addressed to ensure their successful implementation and operation. One of the primary challenges of intelligent control systems isthe complexity of the algorithms and software required to enable autonomous decision-making. Developing these intricate systems demands a high level of expertise in fields such as artificial intelligence, machine learning, and control theory. Moreover, the integration of these systems into existing infrastructurecan be a daunting task, requiring careful planning and execution to avoid disruptions and malfunctions. Another critical consideration is the ethical implications of intelligent control systems, particularly in applications where human safety is at stake. For instance, in autonomous vehicles, these systems must be programmed to make split-second decisions in potentially life-threatening situations. This raises questions about accountability and the moral implications of allowing machines to make decisions that could impact human lives. Furthermore, the reliability and robustness of intelligent control systems are paramount. These systems must be able to adapt to unforeseen circumstances and continue to operate effectively in dynamic environments. Failures in these systems can lead to costly downtime, safety hazards, and damage to equipment. Therefore, rigorous testing and validation procedures are essential to ensure the dependability of intelligent control systems. In addition to technical challenges, the implementation of intelligent control systems may also face resistance from workers who fear being replaced by automation. This fear is not unfounded, as the integration ofintelligent control systems may lead to a reduction in the demand for human laborin certain tasks. It is crucial for organizations to address these concerns and emphasize the collaborative nature of human-machine interaction, where intelligent control systems complement human capabilities rather than replace them. Despite these challenges, the potential benefits of intelligent control systems areundeniable. These systems have the capacity to revolutionize industries by improving efficiency, reducing human error, and enabling tasks that are beyond human capabilities. For example, in manufacturing, intelligent control systems can optimize production processes, minimize waste, and enhance product quality, ultimately leading to cost savings and competitive advantages. In conclusion, intelligent control systems hold great promise for the future of technology and industry. However, their implementation and operation present a myriad of challenges that need to be carefully considered and addressed. From technical complexities to ethical implications and human concerns, a holistic approach is necessary to ensure the successful integration of intelligent control systems. By acknowledging these challenges and working towards innovative solutions, we can harness the full potential of intelligent control systems while mitigating their associated risks.。

Optimization and Control of DynamicSystemsOptimization and control of dynamic systems are essential in various fields, including engineering, economics, and biology. These systems involve complex interactions and behaviors that require careful management to achieve desired outcomes. In this discussion, we will explore the challenges and approaches to optimizing and controlling dynamic systems from multiple perspectives, considering the technical, ethical, and practical implications of these processes. From a technical standpoint, optimizing and controlling dynamic systems often involves dealing with nonlinearities, uncertainties, and time-varying dynamics. These complexities pose significant challenges for engineers and researchers who seek to design effective control strategies. For example, in the field of robotics, controlling the motion of a humanoid robot requires accounting for dynamic interactions with the environment, sensor noise, and the robot's own flexibility. This necessitates the development of advanced control algorithms, such as model predictive control or reinforcement learning, to adapt to changing conditions and achieve optimal performance. Moreover, the optimization of dynamic systems is not limited to purely technical considerations. Ethical and societal implications also come into play, particularly when considering autonomous systems and artificial intelligence. For instance, in the context of autonomous vehicles, optimizing control systems must prioritize safety and ethical decision-making, such as in situations where a collision is unavoidable. This raises important questions about the ethical programming of such systems and the potential impact on human lives. As we continue to integrate autonomous technologies into everyday life, theethical dimension of optimization and control becomes increasingly critical. Beyond the technical and ethical aspects, the practical implementation of optimization and control strategies in dynamic systems presents its own set of challenges. Real-world systems often have constraints and limitations that must be accounted for in the design of control algorithms. In industrial processes, for example, optimizing the operation of complex manufacturing systems requires balancing competing objectives such as production efficiency, energy consumption,and equipment longevity. This calls for a holistic approach that considers not only the technical aspects of control, but also the economic and environmental implications of system optimization. Furthermore, the advancement of optimization and control techniques for dynamic systems is closely tied to ongoing research and innovation. As new technologies emerge and our understanding of complex systems deepens, there is a continuous need to develop more sophisticated control algorithms and optimization methods. This requires collaboration acrossdisciplines and a commitment to lifelong learning and professional development. Engineers and researchers must stay abreast of the latest developments in control theory, machine learning, and other relevant fields to effectively tackle the challenges of optimizing dynamic systems. In conclusion, the optimization and control of dynamic systems present multifaceted challenges that require a comprehensive and nuanced approach. From the technical complexities of nonlinear dynamics to the ethical considerations of autonomous systems, the pursuit of optimal control strategies demands a deep understanding of both the theoretical foundations and practical implications. As we navigate this complex landscape, it is essential to foster interdisciplinary collaboration and ethical reflection to ensure that our efforts in optimization and control ultimately serve the common good.。

第41卷第12期2020年12月自动化仪表PROCESS AUT0M\TI0N INSTRl MKNTATIONVol.41 No. 12Dec.2020核电厂数字仪控系统动态可靠性分析方法综述黄晓津，朱云龙，周树桥，郭超(淸屮大学核能与新能源技术研究院，先进反应堆丨:程与安全教部重点实验室，北京丨()()〇84)摘要：仪表~拧制（I&C)系统是核电厂的屮枢神经，对确保核电厂的安全、稳定和经济运行起矜至关®要的作It丨早期使用基于模拟技术的仪控系统对核电厂的状态进行监测和控制，®部件易老化.U维护成本高昂：W此，0前核电厂使用数卞化仪控系统（DCS) 代替模拟仪控系统对于数字化仪控系统软件、硬件耦合以及人因复杂交互等特点，传统的静态可靠性分析方法无法完全适用动态可靠性分析方法可以发现设计中的薄弱环节，改善或增强数字化仪控系统的可靠性总结了动态可靠性分析方法:①当前典型的动态可靠性分折7/法，包括动态失效模式与影响分析（FMEA)、动态故障/事件树（D FT/ET)、动态流图方法（DFM ))、马尔科夫区间映 射方法（Markm/CCMT);②堪于仿K的方法，包括动态决策事忭树（〇[)KT)和连续事件树（CET)方法;③}1；他动态分析方法.包括GO- FLOW、扩展事件序列罔，P etri网该分析为该领域的进一步研究提供参％，关键词：核电厂；数字化仪控系统；动态分析：可靠性；模拟仪控系统；静态可靠性分析中图分类号：TH-86 文献标志码：A D0I： 10. 16086/j. cnki. issn 1000-0380. 2020080019Review of Dynamic Reliability Analysis Methodsfor NPP Digital Instrument and Control SystemHUANG X iao jin,Z H U Y u n lo n g,Z H O U S h u q iao,G U O Chao(Key I^ihoraton of Advanced Reactor Engineering and Safety of Ministn of Education,Institute of Nuclear and N t»w Energy Technology of Tsinghua University, Beijing 100084, China)Abstract ：Instrument and control ( l&C) system is the central nerve of nuclear power plants and plays a vital role in ensuring the safety,stability and economic operation of nuclear power plants. In the past,analog I&C system were used to monitor and control the state of nuclear power plants,but the components were prone to aging and high maintenance costs. Therefore,cunently nuclear power plants have used digital I&C systems ( DCS) to substitute analog I&C systems. Traditional static reliahililv analysis methods are not fully qualified,as DCS is rendered by the complex interactions of the software,hardware and human components. Using the dynamic reliability analysis methods, designers can find weaknesses in the DCS design, improve or strengtlien the reliability of these stages. This article summarizes dynamic reliability analysis methods：1the current typical dynamic reliability analysis methods including dynamic failure modes and effect analysis (FM KA) ,dynamic fault/event tree (D F T/E T) ,dynamic flowgraph methodology ( D F M)，Markov cell-to-cell mapping technology ( M arkov/CCM T);②simulation-based methods including dynamic decision-event tree ( DDET) and continuous event tree ( C E T) ；(3) other dynamic analysis methods including GO-FLOW, extended event sequence diagram (E SD) ,and Petri net and provide reference for further research in this field.Keywords：Nuclear power plant；Digital instrument and control system；Dynaniic analysis；Reliability；Analog instRiment control system；Static reliability analysis〇引言核电厂具有结构复杂、放射性强的特点，其典型结 构具有两个冋路,运行着许多关键设备（如堆芯、蒸汽 发生器、冷却杲等），一旦设备发生事故，将会对公共 安全、周边环境以及核能产业发展造成巨大的负面影响~。

ITS缩略词AADV ANCE（Advance Driver and Vehicle Advisory Navigation Concept）先进的驾驶员咨询与车辆导航概念ADEPT（Automatic Debiting and Electronic Payment for Transport）运输自动借账和电子支付AHS (Automated Highway Systems) 自动公路系统AI（Artificial Intelligence）人工智能ALI（Autofahrer Leit und Information System）驾驶员引导和信息系统AMIS（Advance Mobile Information System）先进的交通信息系统AMTICS（Advanced Mobile Traffic Information and Communication System）先进的汽车交通信息和通信系统APTS（Advanced Public Transportation Systems）先进的公共运输系统ARTIC（Advanced Rural Transportation Information and Coordination）先进的乡村运输信息与协调ARTS（Advanced Rural Transportation Systems）先进的乡村运输系统ARTS（Advanced Road Transportation System）先进的道路交通系统ASV（Advanced Safety Vehicle）先进的安全车辆ATIS（Advanced Traveler Information Systems）先进的旅行者信息系统ATMS（Advanced Traffic Management Systems）先进的交通管理系统ATT（Advanced Transport Telematics）先进的交通通信技术A VCS（Advanced V ehicle Control Systems）先进的车辆控制系统A VCSS（Advanced Vehicle Control and Safety Systems）先进的车辆控制和安全系统A VI （Automatic Vehicle Identification）自动车辆识别A VL（Automatic Vehicle Location）自动车辆定位CCACS（Comprehensive Automobile traffic Control System）汽车交通综合控制系统CCD（Charge-Coupled Device）电荷耦合器件CCTV（Closed Circuit Television）闭路电视CDRG（Centrally-Determined Route Guidance）中心决定的路径诱导CRT（Cathode Ray Tube）阴极射线管CTSCS（Centralized Traffic Signal Control System）中心交通信号控制系统CVISN（Commercial Vehicle information Systems and Networks）商用车辆信息系统与网络CVO（Commercial Vehicle Operation）商用车辆运营DDDN（Digital Data Network）数字数据网DRGS（Dynamic Route Guidance Systems）动态路径诱导系统DRIVE（Dedicated Road Infrastructure for Vehicle Safety in Europe）欧洲道路交通安全设施DRM（Digital Road Map）数字道路地图DRTS（Demand Responsive Public Transportation Services）响应需求的公共运输服务DSRC（Dedicated Shot Range Communication）专用短程通信EEC（European Commission）欧共体，欧洲委员会ECU（Electronic Control Unit）电子控制单元EDI（Electronic Data Interchange）电子数据交换EPMS（Environment Protection Management Systems）环境保护管理系统ERGS（Electronic Route Guidance System）电子路径诱导系统ERTICO（European Road Transport Telematics Implementation Organization）欧洲道路交通通信技术实用化促进组织ETC（Electronic Toll Collection）电子收费ETTM（Electronic Toll and Traffic Management）电子收费和交通管理EUREKA（European Research Coordination Agency）“尤里卡”FFMS（Freeway Management Systems）高速公路管理系统FHWA（Federal Highway Administration）联邦公路管理局GGIS（Geographical Information System）地理信息系统GPS（Global Positioning System）全球定位系统GSM（Global System for Mobile Communications）全球移动通信系统HHELP（Heavy Vehicle Electronic License Plate）重车电子许可牌照HMI（Human-Machime Interface）人机接口HOV（High Occupancy Vehicle）高乘载率车辆HUD（Head Up Display）平面显示器IIC（Integrated Circuit）集成电路IEC（International Electrotechnical Commission）国际电气标准化委员会IMS（Incident Management Systems）事故管理系统ISDN（Integrated Services Digital Network）综合业务数字网ISO（International Organization for Standardization）国际标准化组织ISTEA（Intermodal Surface Transportation Efficiency Act）陆上综合运输效率化法IRVD（infrared vehicle detector）红外车辆检测器ITCS（Integrated Traffic Control Systems）综合交通管制系统ITI（Intelligent Transportation Infrastructure）智能交通运输基础设施ITS（Intelligent Transport Systems）智能运输系统ITS America（Intelligent Transportation Society of America）美国智能运输协会ITTCC（International Telephone and Telegraph Consultative Committee）国际电话与电报顾问委员会ITU（International Telecommunication Union）国际电气通信联合会ITU-R（International Telecommunication Union-Radio Communication Sector）国际电气通信联合会无线通信分委会IVHS（Intelligent Vehicle-Highway System）智能车辆——道路系统JJDRMA（Japan Digital Road Map Association）日本数字道路地图协会JPO（Joint Program Office）美国运输部ITS联合办公室KKAREN（Keystone Architecture Required for European Networks）欧洲运输网络体系结构KoCoRo（Kochi Communication Road）高知通信道路LCD（Liquid Crystal Display）液晶显示LCX（Leakage Coaxial Cable）漏泄同轴电缆LDRG（Locally-Determined Route Guidance）局部决定的路径诱导LED（Light Emitting Diode）发光二极管MMACS（Mainline Automated Clearance System）主线自动放行系统MAGIC（Metropolitan Area Guidance Information and Control）都市圈诱导信息与控制MDT（Mobile Data Terminal）移动数据终端MOCS（Mobile Operation Control Systems）车辆行驶管理系统MEPC（Metropolitan Expressway Public Corporation）都市高速公路公团NNA（National Architecture）（美国）国家（智能运输系统）体系结构NAHSC（National Automated Highway System Consortium）（美国）国家自动公路系统协会PPDP（Plasma Display Panel）等离子体显示屏PROMETHEUS（Program for Europe Traffic with Highest Efficiency and Unprecedented Safety）欧洲高效率和安全交通计划PROMOTE（Program for Mobility in Transportation in Europe）欧洲运输机动性计划PSTN（Public Switch Telephone Network）公众交换电话网PTPS（Public Transportation Priority Systems）公共运输优先系统RACS（Road Automobile Communication System）路、车间通信系统RDS-TMC（Radio Data System-Traffic Message Channel）无线电数据系统——交通消息频道RF（Radio Frequency）射频RM/OSI（Reference Model/Open System Interconnection）开放系统互连参考模型ROMANSE（Road Management System for Europe）欧洲道路管理系统RTT（Road Transport Telematics）道路交通运输信息通讯技术SSATIN（System Architecture And Traffic Control Integration）系统体系结构和交通控制集成SCATS（Sydney Coordinate Condition Adaptable Traffic system）悉尼并行环境自适应交通系统SWIFT（Seattle Wide-Area Information for Travelers）西雅图广域旅行者信息系统SCOOT（Split Cycle Offset Optimization Technique）绿信比-信号周期-绿时差优化技术SSVS（Super Smart Vehicle System）智能汽车系统TTCC（Traffic Control Center）交通控制中心TDC（Travel Dispatch Center）出行调度中心TDM（Transportation Demand Management）运输需求管理TDM（Time Division Multplexing）时分多路复用TEN-T（Trans-European Road Network for Transport）泛欧道路运输网络TERN（Trans-European Road Network）泛欧道路网络TICS（Transportation Information and Control Systems）运输信息和控制系统TravTek（Travel Technology）旅行技术TRB（Transportation Research Board）（美国）运输研究委员会T-TAP（Transport Telemetics Applications Programme）运输通信技术应用计划UUSDOT（United States Department of Transportation）美国运输部UTC（Urban Traffic Control）城市交通控制UTMS（Universal Traffic Management System）新交通管理系统VVERTIS （Vehicle Road Traffic Intelligence Society）道路、交通、车辆智能化推进协会VICS（Vehicle Information and Communication System）新公路交通信息通信系统VMS（Variable Message Signs）可变信息标志WWIM（Weight-In-Motion）动态称重。

会计系统用英语怎么说在单位的内部控制结构中，单位为了记录、分析、汇总、分类、报告单位的业务活动而建立的方法和程序，称之为会计系统。

那么你知道会计系统用英语怎么说吗?下面店铺为大家带来会计系统的英语说法，希望对大家的学习有所帮助!会计系统的英语说法：accounting system会计系统相关英语表达：内部会计系统 Internal accounting system纵横会计系统 Dynamic Accounting System银行会计系统 bank accounting system责任会计系统 Responsibility Accounting System会计系统的英语例句：1. He doesn't know the nuts and bolts of our accounting system.他一点也不了解我们会计系统的基本细节.2. Manual accounting system is used only by small businesses.手工会计系统只适用于小型企业.3. Accounting system is a kind of system of information business control.会计系统既是一种信息系统,更是一种控制系统.4. Most accounting systems and database management systems include an audit trail component.大多数的会计系统和数据库管理系统都包括审计跟踪模块.5. Understand how computerized and manual accounting systems are used.了解计算机会计系统和手工会计系统如何使用.6. Demonstrate the use of double entry and accountingsystems.怎样运用复式计帐法和会计系统.7. Manual accounting systems use special journals to record transactions by category.手工会计系统根据种类使用专门的日记账来记录交易.8. Second, the accounting system separately measures the performance of each responsibility center.第二, 利用会计系统分别测量每个责任中心的绩效.9. Every organization needs an accounting system. Decision makers need information.每个组织都需要一个会计系统, 决策者需要信息.10. To be thoroughly familiar with the accounting computer systems.必须精通电脑会计系统的使用.11. Describe the financial reporting process and how accounting systems control business operations.描述财政报告的过程,并且怎麽会计系统控制经营活动.12. Systems of accounting for manufacturing operations that incorporate perpetual inventories are usually called cost accounting systems.使用永续盘存制的会计系统叫做成本会计系统.13. Secondly, economic and technical changing of enterprise related to digital accounting system, is studied.接着从经济和技术两个方面分析了与数字会计系统相关的企业变化.14. Internal control is a management priority, not merely a part of the accounting system.内部控制首先是一项管理活动, 而不仅仅是会计系统的一个组成部分.15. Updates and maintains the financial information systemwith daily transactions. Ensures that all the accounting system.每日更新和维护中心的财务信息系统以确保所有交易和转账都如期的编入会计系统.。

Control of Dynamic Systems Control of dynamic systems is a crucial aspect of engineering and technology, as it involves the management and regulation of systems that are constantly changing and evolving. This field encompasses a wide range of applications, from industrial processes and manufacturing to aerospace and automotive systems. The ability to effectively control dynamic systems is essential for ensuring safety, efficiency, and reliability in various engineering and technological domains. One of the key challenges in the control of dynamic systems is the inherent complexity and unpredictability of these systems. Dynamic systems are characterized by their continuous and time-varying behavior, making it difficult to accurately model and predict their responses to different inputs and disturbances. This complexity often requires the use of advanced control techniques and algorithms toeffectively manage and regulate dynamic systems in real-time. Another important aspect of controlling dynamic systems is the need to account for uncertainties and disturbances that can affect the system's behavior. These uncertainties can arise from various sources, such as variations in operating conditions, environmental factors, and component failures. As a result, control strategies must be robust and adaptive to ensure that the system can continue to operate safely and effectively under changing and unpredictable conditions. In addition to technical challenges, the control of dynamic systems also involves ethical and social considerations. For example, in the automotive industry, the development of autonomous vehicles raises important questions about safety, liability, and the ethical implications of delegating control to machines. Similarly, in industrial automation, the implementation of advanced control systems can have significant implications for the workforce and employment, raising concerns about job displacement and the ethical use of technology. From a practical standpoint, the control of dynamic systems also requires a multidisciplinary approach, involving expertise in engineering, mathematics, computer science, and other related fields. Engineers and technologists working in this field must be able to collaborate effectively across different disciplines to develop and implement controlsolutions that are both technically sound and practical to deploy in real-world applications. Furthermore, the control of dynamic systems also presentsopportunities for innovation and advancement in engineering and technology. As new control techniques and technologies continue to emerge, there is potential for significant improvements in the performance, efficiency, and safety of dynamic systems across various industries. This ongoing innovation is essential for addressing the evolving needs and challenges of modern society, from sustainable energy systems to advanced transportation solutions. In conclusion, the control of dynamic systems is a complex and multifaceted field that plays a critical role in engineering and technology. From technical challenges and ethical considerations to practical and interdisciplinary requirements, the control of dynamic systems requires a comprehensive and holistic approach. By addressing these various perspectives and challenges, engineers and technologists can continue to advance the state of the art in controlling dynamic systems, leading to safer, more efficient, and more reliable technologies for the benefit of society.。

当前,计算机技术与网络技术得到了较快发展,计算机软件工程进入到社会各个领域当中,使很多操作实现了自动化,得到了人们的普遍欢迎,解放了大量的人力.为了适应时代的发展,社会各个领域大力引进计算机软件工程.下面是软件工程英文参考文献105个，供大家参考阅读。

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Soft Computing; Investigators from Lovely Professional University Release New Data on Soft Computing (An intensify Harris Hawks optimizer for numerical and engineering optimization problems)[J]. Computer Technology Journal,2020.[46]. GlobalLogic Inc.; GlobalLogic Acquires Meelogic Consulting AG, a European Healthcare and Automotive-Focused Software Engineering Services Firm[J]. Computer Technology Journal,2020.[47]. Engineering - Circuits and Systems Research; Data on Circuits and Systems Research Described by Researchers at Northeastern University (Softcharge: Software Defined Multi-device Wireless Charging Over Large Surfaces)[J]. TelecommunicationsWeekly,2020.[48]. Soft Computing; Researchers from Department of Electrical and Communication Engineering Report on Findings in Soft Computing (Dynamic Histogram Equalization for contrast enhancement for digital images)[J]. Technology News Focus,2020.[49]Mohamed Ellithey Barghoth,Akram Salah,Manal A. Ismail. 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Science - Social Science; Studies from University of Burgos Yield New Information about Social Science (Diagnosis of Software Projects Based on the Viable System Model)[J]. Computer Technology Journal,2020.[58]. Technology - Powder Technology; Investigators at Research Center Pharmaceutical Engineering GmbH Discuss Findings in Powder Technology [Extended Validation and Verification of Xps/avl-fire (Tm), a Computational Cfd-dem Software Platform][J]. Computer Technology Journal,2020.[59]Guadalupe-Isaura Trujillo-Tzanahua,Ulises Juárez-Martínez,Alberto-Alfonso Aguilar-Lasserre,María-Karen Cortés-Verdín,Catherine Azzaro-Pantel. Multiple software product lines to configure applications of internet of things[J]. IET Software,2020,14(2).[60]Eduardo Juárez,Rocio Aldeco-Pérez,Jose.Manuel Velázquez. Academic approach to transform organisations: one engineer at a time[J]. IET Software,2020,14(2).[61]Dennys García-López,Marco Segura-Morales,Edson Loza-Aguirre. Improving the quality and quantity of functional and non-functional requirements obtained during requirements elicitation stage for the development of e-commerce mobile applications: an alternative reference process model[J]. IET Software,2020,14(2).[62]. Guest Editorial: Software Engineering Applications to Solve Organisations Issues[J]. IET Software,2020,14(2).[63]?,?. Engine Control Unit ? ? ?[J]. ,2020,47(4).[64]. Engineering - Software Engineering; Study Data from Nanjing University Update Understanding of Software Engineering (Identifying Failure-causing Schemas In the Presence of Multiple Faults)[J]. Mathematics Week,2020.[65]. Energy - Renewable Energy; Researchers from Institute of Electrical Engineering Detail New Studies and Findings in the Area of Renewable Energy (A Local Control Strategy for Distributed Energy Fluctuation Suppression Based on Soft Open Point)[J]. Journal of Mathematics,2020.[66]Ahmed Zeraoui,Mahfoud Benzerzour,Walid Maherzi,Raid Mansi,Nor-Edine Abriak. New software for the optimization of the formulation and the treatment of dredged sediments for utilization in civil engineering[J]. Journal of Soils and Sediments,2020(prepublish).[67]. Engineering - Concurrent Engineering; Reports from Delhi Technological University Add New Data to Findings in Concurrent Engineering (Systematic literature review of sentiment analysis on Twitter using soft computing techniques)[J]. Journal of Engineering,2020.[68]. Engineering; New Findings from Future University in Egypt in the Area of Engineering Reported (Decision support system for optimum soft clay improvementtechnique for highway construction projects)[J]. Journal of Engineering,2020.[69]Erica Mour?o,Jo?o Felipe Pimentel,Leonardo Murta,Marcos Kalinowski,Emilia Mendes,Claes Wohlin. On the performance of hybrid search strategies for systematic literature reviews in software engineering[J]. Information and Software Technology,2020,123.[70]. Soft Computing; Researchers from Anna University Discuss Findings in Soft Computing (A novel fuzzy mechanism for risk assessment in software projects)[J]. News of Science,2020.软件工程英文参考文献三：[71]. Software and Systems Research; New Software and Systems Research Study Results from Chalmers University of Technology Described (Why and How To Balance Alignment and Diversity of Requirements Engineering Practices In Automotive)[J]. Journal of Transportation,2020.[72]Anupama Kaushik,Devendra Kr. Tayal,Kalpana Yadav. A Comparative Analysis on Effort Estimation for Agile and Non-agile Software Projects Using DBN-ALO[J]. Arabian Journal for Science and Engineering,2020,45(6).[73]Subhrata Das,Adarsh Anand,Mohini Agarwal,Mangey Ram. Release Time Problem Incorporating the Effect of Imperfect Debugging and Fault Generation: An Analysis for Multi-Upgraded Software System[J]. International Journal of Reliability, Quality and Safety Engineering,2020,27(02).[74]Saerom Lee,Hyunmi Baek,Sehwan Oh. The role of openness in open collaboration:A focus on open‐source software development projects[J]. ETRI Journal,2020,42(2).[75]. Soft Computing; Study Results from Computer Science and Engineering Broaden Understanding of Soft Computing (Efficient attribute selection technique for leukaemia prediction using microarray gene data)[J]. Computer Technology Journal,2020.[76]. Engineering - Computational Engineering; Findings from University of Cincinnati in the Area of Computational Engineering Described (Exploratory Metamorphic Testing for Scientific Software)[J]. Computer Technology Journal,2020.[77]. Organizational and End User Computing; Data from Gyeongnam National University of Science and Technology Advance Knowledge in Organizational and End User Computing (A Contingent Approach to Facilitating Conflict Resolution in Software Development Outsourcing Projects)[J]. Computer Technology Journal,2020.[78]. Soft Computing; Findings from Department of Industrial Engineering in the Area of Soft Computing Reported (Analysis of fuzzy supply chain performance based on different buyback contract configurations)[J]. Computer Technology Journal,2020.[79]Hana Mkaouar,Bechir Zalila,Jér?me Hugues,Mohamed Jmaiel. A formal approach to AADL model-based software engineering[J]. International Journal on Software Tools for Technology Transfer,2020,22(5).[80]Riesch Michael,Nguyen Tien Dat,Jirauschek Christian. bertha: Project skeleton for scientific software.[J]. PloS one,2020,15(3).[81]. Computers; Findings from Department of Computer Sciences and Engineering Reveals New Findings on Computers (An assessment of software defined networking approach in surveillance using sparse optimization algorithm)[J]. Telecommunications Weekly,2020.[82]Luigi Ranghetti,Mirco Boschetti,Francesco Nutini,Lorenzo Busetto. “sen2r”: An R toolbox for automatically downloading and preprocessing Sentinel-2 satellite data[J]. 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Computer Technology Journal,2020.[87]. Engineering - Software Engineering; Findings from Nanjing University Broaden Understanding of Software Engineering (Boosting Crash-inducing Change Localization With Rank-performance-based Feature Subset Selection)[J]. Computer Technology Journal,2020.[88]. Engineering - Software Engineering; Study Data from Queen's University Belfast Update Knowledge of Software Engineering (Practical relevance of software engineering research: synthesizing the community's voice)[J]. Computer Technology Journal,2020.[89]. Engineering - Software Engineering; Researchers from Concordia University Detail New Studies and Findings in the Area of Software Engineering (MSRBot: Using bots to answer questions from software repositories)[J]. Computer Technology Journal,2020.[90]Anonymous. DBTA LIVE[J]. Database Trends and Applications,2020,34(2).[91]Tachanun KANGWANTRAKOOL,Kobkrit VIRIYAYUDHAKORN,Thanaruk THEERAMUNKONG. 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Automatic control has played a vital role in the advancement of engineering and science. In addition to its extreme importance in space-vehicle, missile-guidance, and aircraft-piloting systems, automatic control has become an important and integral part of modern manufacturing and industrial processes.自动控制在工程和科学领域起着很重要的作用。

除了在宇宙飞船、导弹的制导和飞机驾驶系统中起重要作用外，自动控制已经成为现代生产及工业过程中重要而不可缺少的组成部分。

Since advances in the theory and practice of automatic control provide means for attaining optimal performance of dynamic systems, improve the quality and lower the cost of production, expand the production rate, relieve the drudgery of many routine, repetitive manual operations, most engineers and scientists must now have a good understanding of this field. 由于自动控制理论和实践的不断发展给人们提供了获得动态系统最佳特性的方法，提高了产品质量，降低了生产成本，提高了生产率，使人们从繁重的日常工作和重复的手工操作中解放出来。