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第42届中国控制会议

作者： Linxiao Qin Tao Sun Shuo Zhang Xi-Ming Sun School of Control Science and Engineering Dalian University of Technology Department of Automation Tsinghua University

For many real-world scenarios,time series prediction needs to be performed on irregularly sampled *** ordinary differential equations have successfully introduced neural dynamical system(NDS) methods into such ***,these models suffer from a large computational cost,and cannot effectively utilize the temporal information in mini-batches,due to mismatched timestamps of *** this work,a unified architecture for NDS is developed,with two novel implementations on discrete input sequences(named TDINDS) and continuous sequences(named TCINDS).The reuse of network modules effectively reduces the scale of parameters and time consumption,while maintaining *** addition,a tensorized timeline alignment method is proposed,which preserves the temporal information of each training sample via reversible mappings,improving prediction performance and reducing time complexity by at least one order of *** are conducted on the Goo gleStock dataset,where we evaluate model accuracy and computational cost by predicting irregular future *** model robustness is tested using incomplete *** results demonstrate the superior performance of TDINDS and TCINDS,with mean square error reduced by 44% and 50% *** evaluation shows that tensorized timeline alignment is also a key factor to enhance irregular time series prediction ability.

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Iterative assist-as-needed control with interaction factor for rehabilitation robots

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science China(Technological sciences) 2021年第4期64卷 836-846页

作者： CAO Ran CHENG Long YANG ChenGuang DONG Zhe School of Artificial Intelligence University'of Chinese Academy of SciencesBeijing 100049China State Key Laboratory for Management and Control of Complex Systems Institute of Automation.Chinese Academy of SciencesBeijing 100190China School of Automation Science and Engineering South China University of TechnologyGuangzhou 510641China School of Electrical and Control Engineering North China Universityof TechnologyBeijing 100144China

Rehabilitation robots for stroke patients have drawn considerable attention because they can reduce the economic and labor costs brought by traditional rehabilitation. control methods for rehabilitation robots have been developed to stimulate the active motion of patients and to improve the effectiveness of rehabilitation care. However, current control methods can only roughly adjust the system’s stiffness and may fail in achieving satisfactory performance. To this end, this paper introduces a novel cost function consisting of the tracking error term and the stiffness term. The cost function contains an interaction factor that represents the patient’s motion intention to balance the weight of these two terms. When the patients try to actively do training tasks, the weight of stiffness term increases, which leads to the larger allowable tracking error and lower stiffness eventually. An iterative updating law of the stiffness matrix is given to reduce the proposed cost function. Theoretical analysis based on the Lyapunov theory is given to ensure the feasibility of the proposed algorithm. Furthermore, a force estimation is used to improve interaction control performance. Finally, simulation experiments are provided to show the effectiveness of the proposed algorithm.

关键词： human-robot interaction impedance stiffness control rehabilitation

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Vision-Based Intelligent Grasping System for Moving Object

Vision-Based Intelligent Grasping System for Moving Object

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International Conference on Guidance, Navigation and control, ICGNC 2022

作者： Wang, Chenyu Hu, Cunyi Wu, Yunjie State Key Laboratory of Virtual Reality Technology and System Beihang University Beijing100191 China School of Automation Science and Electrical Engineering Beihang University Beijing100191 China Science and Technology on Aircraft Control Laboratory Beijing100191 China

ISBN: (纸本)9789811966125

The grasping of moving objects is a widely studied application direction of manipulator. Because of the continuous change of the object position and shape, it is difficult to detect and track it. In this article, we propose an object detection and tracking algorithm based on the cascade of YOLOv4 and DSST. The algorithm can detect the object automatically and then track it steadily. It turns out that the algorithm realized accurate detection, real-time tracking and exact grasping of moving objects, and solved the problems of target loss in the tracking process. Through this algorithm and stereo vision method, we can further obtain the 3-D position of our target. Using Aubo-i5 manipulator and Intel RealSense D435 RGBD depth camera, we build a vision-based grasping system for moving object with the above algorithm, which achieved good real-time performance in the practical grasping experiments, and completed the accurate grasping of moving objects. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Object detection

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Discrete memristive neuron model and its interspike interval-encoded application in image encryption

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science China(Technological sciences) 2021年第10期64卷 2281-2291页

作者： BAO Han HUA ZhongYun LIU WenBo BAO BoCheng College of Automation Engineering Nanjing University of Aeronautics and AstronauticsNanjing 210016China School of Computer Science and Technology Harbin Institute of TechnologyShenzhenShenzhen 518055China School of Microelectronics and Control Engineering Changzhou UniversityChangzhou 213164China

Bursting is a diverse and common phenomenon in neuronal activation patterns and it indicates that fast action voltage spiking periods are followed by resting *** interspike interval(ISI)is the time between successive action voltage spikes of neuron and it is a key indicator used to characterize the ***,a three-dimensional memristive Hindmarsh-Rose(mHR)neuron model was constructed to generate hidden chaotic ***,the properties of the discrete mHR neuron model have not been investigated,*** this article,we first construct a discrete mHR neuron model and then acquire different hidden chaotic bursting sequences under four typical sets of *** make these sequences more suitable for the application,we further encode these hidden chaotic sequences using their ISIs and the performance comparative results show that the ISI-encoded chaotic sequences have much more complex chaos properties than the original *** addition,we apply these ISI-encoded chaotic sequences to the application of image *** image encryption scheme has a symmetric key structure and contains plain-text permutation and bidirectional diffusion *** results and security analyses prove that it has excellent robustness against various possible attacks.

关键词： chaos complexity chaotic bursting sequence memristive neuron model interspike interval(ISI) image encryption

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A Dynamic Event-Triggered Transmission Strategy for Distributed Convex Optimization 42

A Dynamic Event-Triggered Transmission Strategy for Distribu...

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42nd Chinese control Conference, CCC 2023

作者： Liu, Pengfei Liu, Kun-Zhi Zhang, Dianfeng Du, Shengli Dalian University of Technology Academy of Control Science and Engineering Dalian116024 China School of Mathematics and Informational Science Yantai University Yantai100124 China College of Automation Faculty of Information Technology Beijing University of Technology Yantai100124 China

ISBN: (纸本)9789887581543

A dynamic event-triggered transmission strategy is considered for the continuous-time distributed convex optimization algorithm over a strongly connected and weight-balanced directed graph. An auxiliary variable is introduced in the dynamic event-triggered control (DETC) that is free of Zeno phenomenon. We model the DETC system into a hybrid system. Using Lyapunov stability theory, we give a detailed proof for convergence of the continuous-time distributed convex optimization algorithm. Finally, a numerical simulation shows that the DETC can effectively save network communication resources compared with a static event-triggered control (SETC). © 2023 Technical Committee on control Theory, Chinese Association of automation.

关键词： Convex optimization

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Bit Bounce Detection for Drilling Process Based on Multi-Feature Graph and Graph Convolutional Network 49

Bit Bounce Detection for Drilling Process Based on Multi-Fea...

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49th Annual Conference of the IEEE Industrial Electronics Society, IECON 2023

作者： Zhang, Peng Hu, Wenkai Cao, Weihua Wu, Min Wang, Jiandong School of Automation China University of Geosciences Wuhan430074 China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan430074 China Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Wuhan430074 China College of Electrical Engineering and Automation Shandong University of Science and Technology Shandong Province Qingdao China

ISBN: (纸本)9798350331820

Prompt detection of bit bounce can prevent serious incidents and is of great importance for safe and efficient deep geological drilling. In the early stage of bit bounce, signal changes are relatively weak. In addition, there are differences in the topological relationships of samples at different time instances in normal state and bit bounce. These factors present a challenge to timely and accurate bit bounce detection. Therefore, this paper proposes a bit bounce detection method based on multi-feature graph and graph convolution networks. A multi-feature graph construction method using process variables, mean value, Mahalanobis distance, and Euclidean distance is proposed, and a two-layer graph convolutional network is designed to realize deep feature extraction and incident detection. The effectiveness and superiority of the proposed method are demonstrated by a real drilling industrial case. © 2023 IEEE.

关键词： Infill drilling

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High-precision flexible sweat self-collection sensor for mental stress evaluation

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npj Flexible Electronics 2024年第1期8卷 417-426页

作者： Chenhao Wang Zhengyu Wang Wei Wei Zhenjie Zhang Anne Ailina Li Guanghao Huang Xian Li Shuzhi Sam Ge Lianqun Zhou Hui Kong Institute for Future School of AutomationShandong Key Laboratory of Industrial Control TechnologyQingdao UniversityQingdao266071China ETH Zürich Department of Biosystems Science and EngineeringBio Engineering LaboratoryBasel4056Switzerland The University of Sydney 2006 SydneyAustralia Natl Univ Singapore Dept Elect&Comp EngnSingapore117576Singapore Suzhou Institute of Biomedical Engineering and Technology Chinese.Academy of SciencesSuzhou215163China

As a stress hormone existing in the human body,cortisol can reflect the psychological stress and health status in daily life,and is a potential biomarker of the body’s stress *** effectively collect sweat and accurately identify the target,this paper reports a flexible wearable cortisol detection device with outstanding reliability and *** imprinted polymer(MIP)ensures cortisol *** carbon nanotubes(CNT)on electrodes increase sensitivity,expanding the detection range to 10^(−3) to 10^(4) nM,with sensitivity at 189.2 nA/lg(nM).In addition,porous chitosan hydrogel(PCSH)collects sweat effectively,its adhesive properties and 80%swelling rate offer a low-cost alternative to *** printed circuit board(FPCB)and serpentine electrode(SE)ensure device *** non-invasive,highly sensitive device offers a novel method for mental stress monitoring and clinical diagnosis,advancing human physiological state monitoring.

关键词： stress durability swelling

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Generalized-Extended-State-Observer and Equivalent-Input-Disturbance Methods for Active Disturbance Rejection: Deep Observation and Comparison

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IEEE/CAA Journal of Automatica Sinica 2023年第4期10卷 957-968页

作者： Jinhua She Kou Miyamoto Qing-Long Han Min Wu Hiroshi Hashimoto Qing-Guo Wang School of Engineering Tokyo University of TechnologyHachiojiTokyo 192-0982Japan K.Miyamoto is with the Institute of Technology Shimizu CorporationKotoTokyo 135-0044Japan School of Science Computing and Engineering TechnologiesSwinburne University of TechnologyMelbourneVIC 3122Australia School of Automation China University of GeosciencesWuhan 430074 Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of EducationWuhan 430074China School of Industrial Technology Advanced Institute of Industrial TechnologyTokyo 140-0011Japan Institute of Artificial Intelligence and Future Networks Beijing Normal UniversityZhuhai 519087 Guangdong Key Lab of AI and Multi-Modal Data Processing Guangdong Provincial Key Laboratory of Interdisciplinary Research and Application for Data Science BNUHKBU United International College Zhuhai 519087China

Active disturbance-rejection methods are effective in estimating and rejecting disturbances in both transient and steady-state *** paper presents a deep observation on and a comparison between two of those methods:the generalized extended-state observer(GESO)and the equivalent input disturbance(EID)from assumptions,system configurations,stability conditions,system design,disturbance-rejection performance,and extensibility.A time-domain index is introduced to assess the disturbance-rejection performance.A detailed observation of disturbance-suppression mechanisms reveals the superiority of the EID approach over the GESO method.A comparison between these two methods shows that assumptions on disturbances are more practical and the adjustment of disturbance-rejection performance is easier for the EID approach than for the GESO method.

关键词： Active disturbance-rejection control(ADRC) disturbance observer(DOB) equivalent input disturbance(EID) extendedstate observer(ESO) generalized extended-state observer(GESO)

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Indoor Height Estimation Method for Scissor Lift Based on Sensor Information Fusion 25

Indoor Height Estimation Method for Scissor Lift Based on Se...

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Proceedings of the 2025 3rd International Conference on Communication Networks and Machine Learning

作者： Xinhui Song Yuzhe Li Qinmin Yang Bo Fan School of Engineering Huzhou University Huzhou Zhejiang China College of Control Science and Engineering Zhejiang University Hangzhou Zhejiang China School of Automation Science and Engineering Xi'an Jiaotong University Xi'an Shaanxi China

ISBN: (纸本)9798400713231

Accurate height information is crucial for enhancing the safety and operability of scissor lift in indoor operations. To address issues such as outlier interference, integral drift, and barometric pressure perturbation in existing single-sensor height measurement methods, this paper proposes a height estimation algorithm that fuses accelerometer and barometer data. The dynamic threshold outlier rejection mechanism is developed using jerk analysis combined with the improved Interquartile Range (IQR) outlier detection algorithm, reducing high-frequency noise interference in the accelerometer. The Finite State Machine (FSM)-based adaptive Kalman filter identifies the motion state and dynamically adjusts drift compensation, enhancing the algorithm's stability. An Extended Kalman Filtering (EKF) fusion model is developed to construct an observation equation using the barometer's absolute height measurement and the accelerometer's relative displacement. The height estimation error is kept within 0.14mby leveraging the complementary characteristics of both sensors, providing a high-precision height reference for scissor lift in indoor operations. This method has broad application potential in indoor positioning and navigation.

关键词： Extended Kalman Filtering

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Collective Entity Alignment for Knowledge Fusion of Power Grid Dispatching Knowledge Graphs

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IEEE/CAA Journal of Automatica Sinica 2022年第11期9卷 1990-2004页

作者： Linyao Yang Chen Lv Xiao Wang Ji Qiao Weiping Ding Jun Zhang Fei-Yue Wang the State Key Laboratory for Management and Control of Complex Systems Institute of AutomationChinese Academy of SciencesBeijing 100190 the School of Artificial Intelligence University of Chinese Academy of SciencesBeijing 100049China the China Electric Power Research Institute Beijing 100192China IEEE the Qingdao Academy of Intelligent Industries Qingdao 256200China the School of Information Science and Technology Nantong UniversityNantong 226019China the School of Electrical Engineering and Automation Wuhan UniversityWuhan 430072China

Knowledge graphs(KGs)have been widely accepted as powerful tools for modeling the complex relationships between concepts and developing knowledge-based *** recent years,researchers in the field of power systems have explored KGs to develop intelligent dispatching systems for increasingly large power *** multiple power grid dispatching knowledge graphs(PDKGs)constructed by different agencies,the knowledge fusion of different PDKGs is useful for providing more accurate decision *** achieve this,entity alignment that aims at connecting different KGs by identifying equivalent entities is a critical *** entity alignment methods cannot integrate useful structural,attribute,and relational information while calculating entities’similarities and are prone to making many-to-one alignments,thus can hardly achieve the best *** address these issues,this paper proposes a collective entity alignment model that integrates three kinds of available information and makes collective counterpart *** model proposes a novel knowledge graph attention network(KGAT)to learn the embeddings of entities and relations explicitly and calculates entities’similarities by adaptively incorporating the structural,attribute,and relational ***,we formulate the counterpart assignment task as an integer programming(IP)problem to obtain one-to-one *** not only conduct experiments on a pair of PDKGs but also evaluate o ur model on three commonly used cross-lingual *** comparisons indicate that our model outperforms other methods and provides an effective tool for the knowledge fusion of PDKGs.

关键词： Entity alignment integer programming(IP) knowledge fusion knowledge graph embedding power dispatch

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