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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。
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    Autonomous Vehicle Platoons In Urban Road Net-works:A Joint Distributed Reinforcement Learning and Model Predictive Control Approach

    Luigi D'AlfonsoFrancesco GianniniGiuseppe FranzèGiuseppe Fedele...
    141-156页
    查看更多>>摘要：In this paper,platoons of autonomous vehicles oper-ating in urban road networks are considered.From a method-ological point of view,the problem of interest consists of formally characterizing vehicle state trajectory tubes by means of routing decisions complying with traffic congestion criteria.To this end,a novel distributed control architecture is conceived by taking advantage of two methodologies:deep reinforcement learning and model predictive control.On one hand,the routing decisions are obtained by using a distributed reinforcement learning algorithm that exploits available traffic data at each road junction.On the other hand,a bank of model predictive controllers is in charge of computing the more adequate control action for each involved vehicle.Such tasks are here combined into a single framework:the deep reinforcement learning output(action)is translated into a set-point to be tracked by the model predictive controller;con-versely,the current vehicle position,resulting from the applica-tion of the control move,is exploited by the deep reinforcement learning unit for improving its reliability.The main novelty of the proposed solution lies in its hybrid nature:on one hand it fully exploits deep reinforcement learning capabilities for decision-making purposes;on the other hand,time-varying hard con-straints are always satisfied during the dynamical platoon evolu-tion imposed by the computed routing decisions.To efficiently evaluate the performance of the proposed control architecture,a co-design procedure,involving the SUMO and MATLAB plat-forms,is implemented so that complex operating environments can be used,and the information coming from road maps(links,junctions,obstacles,semaphores,etc.)and vehicle state trajecto-ries can be shared and exchanged.Finally by considering as oper-ating scenario a real entire city block and a platoon of eleven vehicles described by double-integrator models,several simula-tions have been performed with the aim to put in light the main features of the proposed approach.Moreover,it is important to underline that in different operating scenarios the proposed rein-forcement learning scheme is capable of significantly reducing traffic congestion phenomena when compared with well-reputed competitors.
      原文链接:
    • 国家科技期刊平台
    • NETL
    • NSTL
    • 万方数据

    Learning to Branch in Combinatorial Optimization With Graph Pointer Networks

    Rui WangZhiming ZhouKaiwen LiTao Zhang...
    157-169页
    查看更多>>摘要：Traditional expert-designed branching rules in branch-and-bound(B&B)are static,often failing to adapt to diverse and evolving problem instances.Crafting these rules is labor-intensive,and may not scale well with complex problems.Given the frequent need to solve varied combinatorial optimiza-tion problems,leveraging statistical learning to auto-tune B&B algorithms for specific problem classes becomes attractive.This paper proposes a graph pointer network model to learn the branch rules.Graph features,global features and historical fea-tures are designated to represent the solver state.The graph neu-ral network processes graph features,while the pointer mecha-nism assimilates the global and historical features to finally deter-mine the variable on which to branch.The model is trained to imitate the expert strong branching rule by a tailored top-k Kull-back-Leibler divergence loss function.Experiments on a series of benchmark problems demonstrate that the proposed approach significantly outperforms the widely used expert-designed bran-ching rules.It also outperforms state-of-the-art machine-lear-ning-based branch-and-bound methods in terms of solving speed and search tree size on all the test instances.In addition,the model can generalize to unseen instances and scale to larger instances.
      原文链接:
    • 国家科技期刊平台
    • NETL
    • NSTL
    • 万方数据
    • 维普

    Control Strategies for Digital Twin Systems

    Guo-Ping Liu
    170-180页
    查看更多>>摘要：With the continuous breakthrough in information technology and its integration into practical applications,indus-trial digital twins are expected to accelerate their development in the near future.This paper studies various control strategies for digital twin systems from the viewpoint of practical applications.To make full use of advantages of digital twins for control sys-tems,an architecture of digital twin control systems,adaptive model tracking scheme,performance prediction scheme,perfor-mance retention scheme,and fault tolerant control scheme are proposed.Those schemes are detailed to deal with different issues on model tracking,performance prediction,performance reten-tion,and fault tolerant control of digital twin systems.Also,the stability of digital twin control systems is analysed.The proposed schemes for digital twin control systems are illustrated by exam-ples.
      原文链接:
    • 国家科技期刊平台
    • NETL
    • NSTL
    • 万方数据
    • 维普

    Path Planning and Tracking Control for Parking via Soft Actor-Critic Under Non-Ideal Scenarios

    Xiaolin TangYuyou YangTeng LiuXianke Lin...
    181-195页
    查看更多>>摘要：Parking in a small parking lot within limited space poses a difficult task.It often leads to deviations between the final parking posture and the target posture.These deviations can lead to partial occupancy of adjacent parking lots,which poses a safety threat to vehicles parked in these parking lots.However,previous studies have not addressed this issue.In this paper,we aim to evaluate the impact of parking deviation of existing vehicles next to the target parking lot(PDEVNTPL)on the automatic ego vehicle(AEV)parking,in terms of safety,comfort,accuracy,and efficiency of parking.A segmented parking training framework(SPTF)based on soft actor-critic(SAC)is proposed to improve parking performance.In the proposed method,the SAC algo-rithm incorporates strategy entropy into the objective function,to enable the AEV to learn parking strategies based on a more com-prehensive understanding of the environment.Additionally,the SPTF simplifies complex parking tasks to maintain the high per-formance of deep reinforcement learning(DRL).The experimen-tal results reveal that the PDEVNTPL has a detrimental influ-ence on the AEV parking in terms of safety,accuracy,and com-fort,leading to reductions of more than 27％,54％,and 26％respectively.However,the SAC-based SPTF effectively mitigates this impact,resulting in a considerable increase in the parking success rate from 71％to 93％.Furthermore,the heading angle deviation is significantly reduced from 2.25 degrees to 0.43 degrees.
      原文链接:
    • 国家科技期刊平台
    • NETL
    • NSTL
    • 万方数据
    • 维普

    Analysis and Design of Time-Delay Impulsive Systems Subject to Actuator Saturation

    Chenhong ZhuXiuping HanXiaodi Li
    196-204页
    查看更多>>摘要：This paper investigates the exponential stability and performance analysis of nonlinear time-delay impulsive systems subject to actuator saturation.When continuous dynamics is unstable,under some conditions,it is shown that the system can be stabilized by a class of saturated delayed-impulses regardless of the length of input delays.Conversely,when the system is origi-nally stable,it is shown that under some conditions,the system is robust with respect to sufficient small delayed-impulses.More-over,the design problem of the controller with the goal of obtain-ing a maximized estimate of the domain of attraction is formu-lated via a convex optimization problem.Three examples are pro-vided to demonstrate the validity of the main results.
      原文链接:
    • 国家科技期刊平台
    • NETL
    • NSTL
    • 万方数据
    • 维普

    Data-Driven Learning Control Algorithms for Unachievable Tracking Problems

    Zeyi ZhangHao JiangDong ShenSamer S.Saab...
    205-218页
    查看更多>>摘要：For unachievable tracking problems,where the sys-tem output cannot precisely track a given reference,achieving the best possible approximation for the reference trajectory becomes the objective.This study aims to investigate solutions using the P-type learning control scheme.Initially,we demonstrate the neces-sity of gradient information for achieving the best approximation.Subsequently,we propose an input-output-driven learning gain design to handle the imprecise gradients of a class of uncertain systems.However,it is discovered that the desired performance may not be attainable when faced with incomplete information.To address this issue,an extended iterative learning control scheme is introduced.In this scheme,the tracking errors are modified through output data sampling,which incorporates low-memory footprints and offers flexibility in learning gain design.The input sequence is shown to converge towards the desired input,resulting in an output that is closest to the given reference in the least square sense.Numerical simulations are provided to validate the theoretical findings.
      原文链接:
    • 国家科技期刊平台
    • NETL
    • NSTL
    • 万方数据
    • 维普

    Practical Prescribed Time Tracking Control With Bounded Time-Varying Gain Under Non-Vanishing Uncertainties

    Dahui LuoYujuan WangYongduan Song
    219-230页
    查看更多>>摘要：This paper investigates the prescribed-time control(PTC)problem for a class of strict-feedback systems subject to non-vanishing uncertainties.The coexistence of mismatched uncertainties and non-vanishing disturbances makes PTC syn-thesis nontrivial.In this work,a control method that does not involve infinite time-varying gain is proposed,leading to a practi-cal and global prescribed time tracking control solution for the strict-feedback systems,in spite of both the mismatched and non-vanishing uncertainties.Different from methods based on control switching to avoid the issue of infinite control gain that involves control discontinuity at the switching point,in our method a soft-ening unit is exclusively included to ensure the continuity of the control action.Furthermore,in contrast to most existing pre-scribed-time control works where the control scheme is only valid on a finite time interval,in this work,the proposed control scheme is valid on the entire time interval.In addition,the prior information on the upper or lower bound of gi is not in need,enlarging the applicability of the proposed method.Both the the-oretical analysis and numerical simulation confirm the effective-ness of the proposed control algorithm.
      原文链接:
    • 国家科技期刊平台
    • NETL
    • NSTL
    • 万方数据
    • 维普

    Point Cloud Classification Using Content-Based Transformer via Clustering in Feature Space

    Yahui LiuBin TianYisheng LvLingxi Li...
    231-239页
    查看更多>>摘要：Recently,there have been some attempts of Trans-former in 3D point cloud classification.In order to reduce com-putations,most existing methods focus on local spatial attention,but ignore their content and fail to establish relationships between distant but relevant points.To overcome the limitation of local spatial attention,we propose a point content-based Transformer architecture,called PointConT for short.It exploits the locality of points in the feature space(content-based),which clusters the sampled points with similar features into the same class and com-putes the self-attention within each class,thus enabling an effec-tive trade-off between capturing long-range dependencies and computational complexity.We further introduce an inception fea-ture aggregator for point cloud classification,which uses parallel structures to aggregate high-frequency and low-frequency infor-mation in each branch separately.Extensive experiments show that our PointConT model achieves a remarkable performance on point cloud shape classification.Especially,our method exhibits 90.3％Top-1 accuracy on the hardest setting of ScanOb-jectNN.Source code of this paper is available at https://github.com/yahuiliu99/PointConT.
      原文链接:
    • 国家科技期刊平台
    • NETL
    • NSTL
    • 万方数据
    • 维普

    Non-Deterministic Liveness-Enforcing Supervisor Tolerant to Sensor-Reading Modification Attacks

    Dan YouShouguang Wang
    240-248页
    查看更多>>摘要：In this paper,we study the supervisory control problem of discrete event systems assuming that cyber-attacks might occur.In particular,we focus on the problem of liveness enforcement and consider a sensor-reading modification attack(SM-attack)that may disguise the occurrence of an event as that of another event by intruding sensor communication channels.To solve the problem,we introduce non-deterministic supervisors in the paper,which associate to every observed sequence a set of possible control actions offline and choose a control action from the set randomly online to control the system.Specifically,given a bounded Petri net(PN)as the reference formalism and an SM-attack,an algorithm that synthesizes a liveness-enforcing non-deterministic supervisor tolerant to the SM-attack is proposed for the first time.
      原文链接:
    • 国家科技期刊平台
    • NETL
    • NSTL
    • 万方数据
    • 维普

    Protocol-Based Non-Fragile State Estimation for Delayed Recurrent Neural Networks Subject to Replay Attacks

    Fan YangHongli DongYuxuan ShenXuerong Li...
    249-251页
      原文链接:
    • 国家科技期刊平台
    • NETL
    • NSTL
    • 万方数据
    • 维普