首页期刊导航|自动化学报（英文版）
期刊信息/Journal information
自动化学报（英文版）
中国自动化学会、中国科学院自动化研究所、中国科技出版传媒股份有限公司
自动化学报（英文版）

中国自动化学会、中国科学院自动化研究所、中国科技出版传媒股份有限公司

双月刊

2329-9266

yan.ou@ia.ac.cn

010-82544459

自动化学报（英文版）/Journal IEEE/CAA Journal of Automatica SinicaCSCDCSTPCD北大核心SCI
查看更多>>《自动化学报》（英文版），刊名为 IEEE/CAA Journal of Automatica Sinica (JAS)，创刊于2014年，由中国自动化学会、中国科学院自动化研究所主办，与IEEE合作，报道自动控制、人工智能、机器人等领域热点和前沿方向的研究成果。JAS被SCI, EI, Scopus等数据库收录，是ESI刊源期刊，也是自动化与控制系统领域唯一的中国主办Q1区SCI期刊。2019年首个JCR影响因子5.129，在自动化与控制领域全球63种SCI期刊中排名第11（前17%），位列Q1区。2019年CiteScore为8.3，位于所属各领域Q1区前列；国内外综合他引影响因子为6.688，在自动化、计算机领域的中国英文期刊中排名第1。
正式出版
收录年代

    When Does Sora Show:The Beginning of TAO to Imaginative Intelligence and Scenarios Engineering

    Fei-Yue WangQinghai MiaoLingxi LiQinghua Ni...
    809-815页
      原文链接:
    • 国家科技期刊平台
    • NETL
    • NSTL
    • 万方数据

    Goal-Oriented Control Systems(GOCS):From HOW to WHAT

    Wen-Hua Chen
    816-819页
      原文链接:
    • 国家科技期刊平台
    • NETL
    • NSTL
    • 万方数据

    Digital CEOs in Digital Enterprises:Automating,Augmenting,and Parallel in Metaverse/CPSS/TAOs

    Juanjuan LiRui QinSangtian GuanXiao Xue...
    820-823页
      原文链接:
    • 国家科技期刊平台
    • NETL
    • NSTL
    • 万方数据

    A Tutorial on Federated Learning from Theory to Practice:Foundations,Software Frameworks,Exemplary Use Cases,and Selected Trends

    M.Victoria LuzónNuria Rodríguez-BarrosoAlberto Argente-GarridoDaniel Jiménez-López...
    824-850页
    查看更多>>摘要：When data privacy is imposed as a necessity,Feder-ated learning(FL)emerges as a relevant artificial intelligence field for developing machine learning(ML)models in a distributed and decentralized environment.FL allows ML models to be trained on local devices without any need for centralized data transfer,thereby reducing both the exposure of sensitive data and the possibility of data interception by malicious third parties.This paradigm has gained momentum in the last few years,spurred by the plethora of real-world applications that have leveraged its ability to improve the efficiency of distributed learning and to accommodate numerous participants with their data sources.By virtue of FL,models can be learned from all such distributed data sources while preserving data privacy.The aim of this paper is to provide a practical tutorial on FL,including a short methodology and a systematic analysis of existing software frameworks.Fur-thermore,our tutorial provides exemplary cases of study from three complementary perspectives:i)Foundations of FL,describ-ing the main components of FL,from key elements to FL cate-gories;ii)Implementation guidelines and exemplary cases of study,by systematically examining the functionalities provided by existing software frameworks for FL deployment,devising a methodology to design a FL scenario,and providing exemplary cases of study with source code for different ML approaches;and iii)Trends,shortly reviewing a non-exhaustive list of research directions that are under active investigation in the current FL landscape.The ultimate purpose of this work is to establish itself as a referential work for researchers,developers,and data scien-tists willing to explore the capabilities of FL in practical applica-tions.
      原文链接:
    • 国家科技期刊平台
    • NETL
    • NSTL
    • 万方数据

    Cybersecurity Landscape on Remote State Estimation:A Comprehensive Review

    Jing ZhouJun ShangTongwen Chen
    851-865页
    查看更多>>摘要：Cyber-physical systems(CPSs)have emerged as an essential area of research in the last decade,providing a new paradigm for the integration of computational and physical units in modern control systems.Remote state estimation(RSE)is an indispensable functional module of CPSs.Recently,it has been demonstrated that malicious agents can manipulate data packets transmitted through unreliable channels of RSE,leading to severe estimation performance degradation.This paper aims to present an overview of recent advances in cyber-attacks and defensive countermeasures,with a specific focus on integrity attacks against RSE.Firstly,two representative frameworks for the synthesis of optimal deception attacks with various performance metrics and stealthiness constraints are discussed,which provide a deeper insight into the vulnerabilities of RSE.Secondly,a detailed review of typical attack detection and resilient estimation algorithms is included,illustrating the latest defensive measures safeguarding RSE from adversaries.Thirdly,some prevalent attacks impair-ing the confidentiality and data availability of RSE are examined from both attackers'and defenders'perspectives.Finally,several challenges and open problems are presented to inspire further exploration and future research in this field.
      原文链接:
    • 国家科技期刊平台
    • NETL
    • NSTL
    • 万方数据

    Data-Based Filters for Non-Gaussian Dynamic Sys-tems With Unknown Output Noise Covariance

    Elham JavanfarMehdi Rahmani
    866-877页
    查看更多>>摘要：This paper proposes linear and nonlinear filters for a non-Gaussian dynamic system with an unknown nominal covari-ance of the output noise.The challenge of designing a suitable fil-ter in the presence of an unknown covariance matrix is addressed by focusing on the output data set of the system.Considering that data generated from a Gaussian distribution exhibit ellipsoidal scattering,we first propose the weighted sum of norms(SON)clustering method that prioritizes nearby points,reduces distant point influence,and lowers computational cost.Then,by intro-ducing the weighted maximum likelihood,we propose a semi-def-inite program(SDP)to detect outliers and reduce their impacts on each cluster.Detecting these weights paves the way to obtain an appropriate covariance of the output noise.Next,two filtering approaches are presented:a cluster-based robust linear filter using the maximum a posterior(MAP)estimation and a cluster-based robust nonlinear filter assuming that output noise distribu-tion stems from some Gaussian noise resources according to the ellipsoidal clusters.At last,simulation results demonstrate the effectiveness of our proposed filtering approaches.
      原文链接:
    • 国家科技期刊平台
    • NETL
    • NSTL
    • 万方数据

    Designing Proportional-Integral Consensus Protocols for Second-Order Multi-Agent Systems Using Delayed and Memorized State Information

    Honghai WangQing-Long Han
    878-892页
    查看更多>>摘要：This paper is concerned with consensus of a second-order linear time-invariant multi-agent system in the situation that there exists a communication delay among the agents in the network.A proportional-integral consensus protocol is designed by using delayed and memorized state information.Under the proportional-integral consensus protocol,the consensus problem of the multi-agent system is transformed into the problem of asymptotic stability of the corresponding linear time-invariant time-delay system.Note that the location of the eigenvalues of the corresponding characteristic function of the linear time-invariant time-delay system not only determines the stability of the system,but also plays a critical role in the dynamic performance of the system.In this paper,based on recent results on the distribution of roots of quasi-polynomials,several necessary conditions for Hurwitz stability for a class of quasi-polynomials are first derived.Then allowable regions of consensus protocol parameters are esti-mated.Some necessary and sufficient conditions for determining effective protocol parameters are provided.The designed proto-col can achieve consensus and improve the dynamic performance of the second-order multi-agent system.Moreover,the effects of delays on consensus of systems of harmonic oscillators/double integrators under proportional-integral consensus protocols are investigated.Furthermore,some results on proportional-integral consensus are derived for a class of high-order linear time-invari-ant multi-agent systems.
      原文链接:
    • 国家科技期刊平台
    • NETL
    • NSTL
    • 万方数据

    A Novel Sensing Imaging Equipment Under Extre-mely Dim Light for Blast Furnace Burden Surface:Starlight High-Temperature Industrial Endoscope

    Zhipeng ChenXinyi WangWeihua GuiJilin Zhu...
    893-906页
    查看更多>>摘要：Blast furnace(BF)burden surface contains the most abundant,intuitive and credible smelting information and acquiring high-definition and high-brightness optical images of which is essential to realize precise material charging control,optimize gas flow distribution and improve ironmaking efficiency.It has been challengeable to obtain high-quality optical burden surface images under high-temperature,high-dust,and extremely-dim(less than 0.001 Lux)environment.Based on a novel endo-scopic sensing detection idea,a reverse telephoto structure starlight imaging system with large field of view and large aper-ture is designed.Combined with a water-air dual cooling intelli-gent self-maintenance protection device and the imaging system,a starlight high-temperature industrial endoscope is developed to obtain clear optical burden surface images stably under the harsh environment.Based on an endoscope imaging area model,a material flow trajectory model and a gas-dust coupling distribu-tion model,an optimal installation position and posture configu-ration method for the endoscope is proposed,which maximizes the effective imaging area and ensures large-area,safe and stable imaging of the device in a confined space.Industrial experiments and applications indicate that the proposed method obtains clear and reliable large-area optical burden surface images and reveals new BF conditions,providing key data support for green iron smelting.
      原文链接:
    • 国家科技期刊平台
    • NETL
    • NSTL
    • 万方数据

    Adaptive Sensor-Fault Tolerant Control of Unmanned Underwater Vehicles With Input Saturation

    Xuerao WangQingling WangYanxu SuYuncheng Ouyang...
    907-918页
    查看更多>>摘要：This paper investigates the tracking control problem for unmanned underwater vehicles(UUVs)systems with sensor faults,input saturation,and external disturbance caused by waves and ocean currents.An active sensor fault-tolerant control scheme is proposed.First,the developed method only requires the inertia matrix of the UUV,without other dynamic information,and can handle both additive and multiplicative sensor faults.Subsequently,an adaptive fault-tolerant controller is designed to achieve asymptotic tracking control of the UUV by employing robust integral of the sign of error feedback method.It is shown that the effect of sensor faults is online estimated and compen-sated by an adaptive estimator.With the proposed controller,the tracking error and estimation error can asymptotically converge to zero.Finally,simulation results are performed to demonstrate the effectiveness of the proposed method.
      原文链接:
    • 国家科技期刊平台
    • NETL
    • NSTL
    • 万方数据

    Constrained Multi-Objective Optimization With Deep Reinforcement Learning Assisted Operator Selection

    Fei MingWenyin GongLing WangYaochu Jin...
    919-931页
    查看更多>>摘要：Solving constrained multi-objective optimization problems with evolutionary algorithms has attracted consider-able attention.Various constrained multi-objective optimization evolutionary algorithms(CMOEAs)have been developed with the use of different algorithmic strategies,evolutionary operators,and constraint-handling techniques.The performance of CMOEAs may be heavily dependent on the operators used,however,it is usually difficult to select suitable operators for the problem at hand.Hence,improving operator selection is promising and nec-essary for CMOEAs.This work proposes an online operator selection framework assisted by Deep Reinforcement Learning.The dynamics of the population,including convergence,diversity,and feasibility,are regarded as the state;the candidate operators are considered as actions;and the improvement of the population state is treated as the reward.By using a Q-network to learn a policy to estimate the Q-values of all actions,the proposed approach can adaptively select an operator that maximizes the improvement of the population according to the current state and thereby improve the algorithmic performance.The framework is embedded into four popular CMOEAs and assessed on 42 bench-mark problems.The experimental results reveal that the pro-posed Deep Reinforcement Learning-assisted operator selection significantly improves the performance of these CMOEAs and the resulting algorithm obtains better versatility compared to nine state-of-the-art CMOEAs.
      原文链接:
    • 国家科技期刊平台
    • NETL
    • NSTL
    • 万方数据