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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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    Label Recovery and Trajectory Designable Network for Transfer Fault Diagnosis of Machines With Incorrect Annotation

    Bin YangYaguo LeiXiang LiNaipeng Li...
    932-945页
    查看更多>>摘要：The success of deep transfer learning in fault diag-nosis is attributed to the collection of high-quality labeled data from the source domain.However,in engineering scenarios,achieving such high-quality label annotation is difficult and expensive.The incorrect label annotation produces two negative effects:1)the complex decision boundary of diagnosis models lowers the generalization performance on the target domain,and 2)the distribution of target domain samples becomes misaligned with the false-labeled samples.To overcome these negative effects,this article proposes a solution called the label recovery and tra-jectory designable network(LRTDN).LRTDN consists of three parts.First,a residual network with dual classifiers is to learn features from cross-domain samples.Second,an annotation check module is constructed to generate a label anomaly indicator that could modify the abnormal labels of false-labeled samples in the source domain.With the training of relabeled samples,the com-plexity of diagnosis model is reduced via semi-supervised learning.Third,the adaptation trajectories are designed for sample distri-butions across domains.This ensures that the target domain sam-ples are only adapted with the pure-labeled samples.The LRTDN is verified by two case studies,in which the diagnosis knowledge of bearings is transferred across different working conditions as well as different yet related machines.The results show that LRTDN offers a high diagnosis accuracy even in the presence of incorrect annotation.
      原文链接:
    • 国家科技期刊平台
    • NETL
    • NSTL
    • 万方数据

    Quantization and Event-Triggered Policy Design for Encrypted Networked Control

    Yongxia ShiEhsan Nekouei
    946-955页
    查看更多>>摘要：This paper proposes a novel event-driven encrypted control framework for linear networked control systems(NCSs),which relies on two modified uniform quantization policies,the Paillier cryptosystem,and an event-triggered strategy.Due to the fact that only integers can work in the Pailler cryptosystem,both the real-valued control gain and system state need to be first quantized before encryption.This is dramatically different from the existing quantized control methods,where only the quantiza-tion of a single value,e.g.,the control input or the system state,is considered.To handle this issue,static and dynamic quantization policies are presented,which achieve the desired integer conver-sions and guarantee asymptotic convergence of the quantized sys-tem state to the equilibrium.Then,the quantized system state is encrypted and sent to the controller when the triggering condi-tion,specified by a state-based event-triggered strategy,is satis-fied.By doing so,not only the security and confidentiality of data transmitted over the communication network are protected,but also the ciphertext expansion phenomenon can be relieved.Addi-tionally,by tactfully designing the quantization sensitivities and triggering error,the proposed event-driven encrypted control framework ensures the asymptotic stability of the overall closed-loop system.Finally,a simulation example of the secure motion control for an inverted pendulum cart system is presented to evaluate the effectiveness of the theoretical results.
      原文链接:
    • 国家科技期刊平台
    • NETL
    • NSTL
    • 万方数据

    Path-Following Control With Obstacle Avoidance of Autonomous Surface Vehicles Subject to Actuator Faults

    Li-Ying HaoGege DongTieshan LiZhouhua Peng...
    956-964页
    查看更多>>摘要：This paper investigates the path-following control problem with obstacle avoidance of autonomous surface vehicles in the presence of actuator faults,uncertainty and external dis-turbances.Autonomous surface vehicles inevitably suffer from actuator faults in complex sea environments,which may cause existing obstacle avoidance strategies to fail.To reduce the influ-ence of actuator faults,an improved artificial potential function is constructed by introducing the lower bound of actuator effi-ciency factors.The nonlinear state observer,which only depends on measurable position information of the autonomous surface vehicle,is used to address uncertainties and external disturbances.By using a backstepping technique and adaptive mechanism,a path-following control strategy with obstacle avoidance and fault tolerance is designed which can ensure that the tracking errors converge to a small neighborhood of zero.Compared with exist-ing results,the proposed control strategy has the capability of obstacle avoidance and fault tolerance simultaneously.Finally,the comparison results through simulations are given to verify the effectiveness of the proposed method.
      原文链接:
    • 国家科技期刊平台
    • NETL
    • NSTL
    • 万方数据

    A Weakly-Supervised Crowd Density Estimation Method Based on Two-Stage Linear Feature Calibration

    Yong-Chao LiRui-Sheng JiaYing-Xiang HuHong-Mei Sun...
    965-981页
    查看更多>>摘要：In a crowd density estimation dataset,the annota-tion of crowd locations is an extremely laborious task,and they are not taken into the evaluation metrics.In this paper,we aim to reduce the annotation cost of crowd datasets,and propose a crowd density estimation method based on weakly-supervised learning,in the absence of crowd position supervision informa-tion,which directly reduces the number of crowds by using the number of pedestrians in the image as the supervised informa-tion.For this purpose,we design a new training method,which exploits the correlation between global and local image features by incremental learning to train the network.Specifically,we design a parent-child network(PC-Net)focusing on the global and local image respectively,and propose a linear feature cali-bration structure to train the PC-Net simultaneously,and the child network learns feature transfer factors and feature bias weights,and uses the transfer factors and bias weights to linearly feature calibrate the features extracted from the Parent network,to improve the convergence of the network by using local fea-tures hidden in the crowd images.In addition,we use the pyra-mid vision transformer as the backbone of the PC-Net to extract crowd features at different levels,and design a global-local fea-ture loss function(L2).We combine it with a crowd counting loss(LC)to enhance the sensitivity of the network to crowd features during the training process,which effectively improves the accu-racy of crowd density estimation.The experimental results show that the PC-Net significantly reduces the gap between fully-supervised and weakly-supervised crowd density estimation,and outperforms the comparison methods on five datasets of Shang-haiTech Part A,ShanghaiTech Part B,UCF_CC_50,UCF_QNRF and JHU-CROWD++.
      原文链接:
    • 国家科技期刊平台
    • NETL
    • NSTL
    • 万方数据

    Detection of Oscillations in Process Control Loops From Visual Image Space Using Deep Convolutional Networks

    Tao WangQiming ChenXun LangLei Xie...
    982-995页
    查看更多>>摘要：Oscillation detection has been a hot research topic in industries due to the high incidence of oscillation loops and their negative impact on plant profitability.Although numerous auto-matic detection techniques have been proposed,most of them can only address part of the practical difficulties.An oscillation is heuristically defined as a visually apparent periodic variation.However,manual visual inspection is labor-intensive and prone to missed detection.Convolutional neural networks(CNNs),inspired by animal visual systems,have been raised with power-ful feature extraction capabilities.In this work,an exploration of the typical CNN models for visual oscillation detection is per-formed.Specifically,we tested MobileNet-V1,ShuffleNet-V2,EfficientNet-B0,and GhostNet models,and found that such a visual framework is well-suited for oscillation detection.The fea-sibility and validity of this framework are verified utilizing exten-sive numerical and industrial cases.Compared with state-of-the-art oscillation detectors,the suggested framework is more straightforward and more robust to noise and mean-nonstation-arity.In addition,this framework generalizes well and is capable of handling features that are not present in the training data,such as multiple oscillations and outliers.
      原文链接:
    • 国家科技期刊平台
    • NETL
    • NSTL
    • 万方数据

    Relaxed Stability Criteria for Time-Delay Systems:A Novel Quadratic Function Convex Approximation Approach

    Shenquan WangWenchengyu JiYulian JiangYanzheng Zhu...
    996-1006页
    查看更多>>摘要：This paper develops a quadratic function convex approximation approach to deal with the negative definite prob-lem of the quadratic function induced by stability analysis of lin-ear systems with time-varying delays.By introducing two adjustable parameters and two free variables,a novel convex function greater than or equal to the quadratic function is con-structed,regardless of the sign of the coefficient in the quadratic term.The developed lemma can also be degenerated into the existing quadratic function negative-determination(QFND)lemma and relaxed QFND lemma respectively,by setting two adjustable parameters and two free variables as some particular values.Moreover,for a linear system with time-varying delays,a relaxed stability criterion is established via our developed lemma,together with the quivalent reciprocal combination technique and the Bessel-Legendre inequality.As a result,the conservatism can be reduced via the proposed approach in the context of con-structing Lyapunov-Krasovskii functionals for the stability analy-sis of linear time-varying delay systems.Finally,the superiority of our results is illustrated through three numerical examples.
      原文链接:
    • 国家科技期刊平台
    • NETL
    • NSTL
    • 万方数据

    Adaptive Trajectory Tracking Control for Nonholonomic Wheeled Mobile Robots:A Barrier Function Sliding Mode Approach

    Yunjun ZhengJinchuan ZhengKe ShaoHan Zhao...
    1007-1021页
    查看更多>>摘要：The trajectory tracking control performance of non-holonomic wheeled mobile robots(NWMRs)is subject to non-holonomic constraints,system uncertainties,and external distur-bances.This paper proposes a barrier function-based adaptive sliding mode control(BFASMC)method to provide high-preci-sion,fast-response performance and robustness for NWMRs.Compared with the conventional adaptive sliding mode control,the proposed control strategy can guarantee that the sliding mode variables converge to a predefined neighborhood of origin with a predefined reaching time independent of the prior knowledge of the uncertainties and disturbances bounds.Another advantage of the proposed algorithm is that the control gains can be adaptively adjusted to follow the disturbances amplitudes thanks to the bar-rier function.The benefit is that the overestimation of control gain can be eliminated,resulting in chattering reduction.Moreover,a modified barrier function-like control gain is employed to pre-vent the input saturation problem due to the physical limit of the actuator.The stability analysis and comparative experiments demonstrate that the proposed BFASMC can ensure the pre-specified convergence performance of the NWMR system output variables and strong robustness against uncertainties/distur-bances.
      原文链接:
    • 国家科技期刊平台
    • NETL
    • NSTL
    • 万方数据

    Computational Experiments for Complex Social Systems:Experiment Design and Generative Explanation

    Xiao XueDeyu ZhouXiangning YuGang Wang...
    1022-1038页
    查看更多>>摘要：Powered by advanced information technology,more and more complex systems are exhibiting characteristics of the cyber-physical-social systems(CPSS).In this context,computa-tional experiments method has emerged as a novel approach for the design,analysis,management,control,and integration of CPSS,which can realize the causal analysis of complex systems by means of"algorithmization"of"counterfactuals".However,because CPSS involve human and social factors(e.g.,autonomy,initiative,and sociality),it is difficult for traditional design of experiment(DOE)methods to achieve the generative explanation of system emergence.To address this challenge,this paper pro-poses an integrated approach to the design of computational experiments,incorporating three key modules:1)Descriptive module:Determining the influencing factors and response vari-ables of the system by means of the modeling of an artificial soci-ety;2)Interpretative module:Selecting factorial experimental design solution to identify the relationship between influencing factors and macro phenomena;3)Predictive module:Building a meta-model that is equivalent to artificial society to explore its operating laws.Finally,a case study of crowd-sourcing platforms is presented to illustrate the application process and effectiveness of the proposed approach,which can reveal the social impact of algorithmic behavior on"rider race".
      原文链接:
    • 国家科技期刊平台
    • NETL
    • NSTL
    • 万方数据

    Finite-time Prescribed Performance Time-Varying Formation Control for Second-Order Multi-Agent Systems With Non-Strict Feedback Based on a Neural Network Observer

    Chi MaDianbiao Dong
    1039-1050页
    查看更多>>摘要：This paper studies the problem of time-varying for-mation control with finite-time prescribed performance for non-strict feedback second-order multi-agent systems with unmea-sured states and unknown nonlinearities.To eliminate nonlinear-ities,neural networks are applied to approximate the inherent dynamics of the system.In addition,due to the limitations of the actual working conditions,each follower agent can only obtain the locally measurable partial state information of the leader agent.To address this problem,a neural network state observer based on the leader state information is designed.Then,a finite-time prescribed performance adaptive output feedback control strat-egy is proposed by restricting the sliding mode surface to a pre-scribed region,which ensures that the closed-loop system has practical finite-time stability and that formation errors of the multi-agent systems converge to the prescribed performance bound in finite time.Finally,a numerical simulation is provided to demonstrate the practicality and effectiveness of the developed algorithm.
      原文链接:
    • 国家科技期刊平台
    • NETL
    • NSTL
    • 万方数据

    3D Localization for Multiple AUVs in Anchor-Free En-vironments by Exploring the Use of Depth Information

    Yichen LiWenbin YuXinping Guan
    1051-1053页
      原文链接:
    • 国家科技期刊平台
    • NETL
    • NSTL
    • 万方数据