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IEEE International Symposium on Industrial Electronics (ISIE)

作者： Yujian Zhou Jinhua She Feng Wang Makoto Iwasaki School of Automation China University of Geosciences Wuhan China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan China Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Wuhan China School of Engineering Tokyo University of Technology Hachioji Tokyo Japan Department of Electrical and Mechanical Engineering Nagoya Institute of Technology Nagoya Japan

This study integrated an improved equivalent-input-disturbance (EID) and a repetitive control methods to ensure reference tracking and enhance disturbance-rejection performance for a pedaling rehabilitation robot. A repetitive controller ensures steady-state tracking of a periodic reference input. An EID estimator with a state observer estimates and compensates for the effect of disturbances generated by patients with lower-limb impairment. The stability condition of the closed-loop system is analyzed based on the Lyapunov stability theory. Simulation results show the effectiveness of this method.

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Can Digital Intelligence and Cyber-Physical-Social systems Achieve Global Food Security and Sustainability?

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IEEE/CAA Journal of Automatica Sinica 2023年第11期10卷 2070-2080页

作者： Yanfen Wang Mengzhen Kang Yali Liu Juanjuan Li Kai Xue Xiujuan Wang Jianqing Du Yonglin Tian Qinghua Ni Fei-Yue Wang College of Resources and Environment University of Chinese Academy of SciencesBeijing 100049 Beijing Yanshan Earth Critical Zone National Research Station University of Chinese Academy of SciencesBeijing 101408 State Key Laboratory of Tibetan Plateau Earth System Environment and ResourcesInstitute of Tibetan Plateau ResearchChinese Academy of SciencesBeijing 100101China State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of AutomationChinese Academy of Sciences(CASIA)Beijing 100190 School of Artificial Intelligence University of Chinese Academy of SciencesBeijing 100049China School of Grassland Science Beijing Forestry UniversityBeijing 100083China State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of AutomationChinese Academy of SciencesBeijing 100190China State Key Laboratory of Plateau Ecology and Agriculture Qinghai UniversityXining 810016 Key Laboratory of Adaptation and Evolution of Plateau Biota Northwest Institute of Plateau BiologyChinese Academy of SciencesXining 810001 Binzhou Institute of Technology Weiqiao-UCAS Science and Technology ParkBinzhou 256606China Beijing Yanshan Earth Critical Zone National Research Station University of Chinese Academy of SciencesBeijing 101408China Department of Engineering Science Faculty of Innovation EngineeringMacao University of Science and TechnologyMacao 999078 State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of AutomationChinese Academy of Sciences(CASIA)Beijing 100190China Faculty of Innovation Engineering Macao University of Science and TechnologyMacao 999078 Beijing Engineering Research Center of Intelligent Systems and Technology Chinese Academy of SciencesBeijing 100098 State Key Laboratory for Management and Control of Complex Systems Chinese Academy of SciencesBeijing 100190China IEEE

Plants sequester carbon through photosynthesis and provide primary productivity for the ecosystem. However, they also simultaneously consume water through transpiration, leading to a carbon-water balance relationship. Agricultural production can be regarded as a form of carbon sequestration *** the perspective of the natural-social-economic complex ecosystem, excessive water usage in food production will aggravate regional water pressure for both domestic and industrial purposes. Hence, achieving a harmonious equilibrium between carbon and water resources during the food production process is a key scientific challenge for ensuring food security and sustainability. Digital intelligence(DI) and cyber-physical-social systems(CPSS) are emerging as the new research paradigms that are causing a substantial shift in the conventional thinking and methodologies across various scientific fields, including ecological science and sustainability studies. This paper outlines our recent efforts in using advanced technologies such as big data, artificial intelligence(AI), digital twins, metaverses, and parallel intelligence to model, analyze, and manage the intricate dynamics and equilibrium among plants, carbon, and water in arid and semiarid ecosystems. It introduces the concept of the carbon-water balance and explores its management at three levels: the individual plant level, the community level, and the natural-social-economic complex ecosystem level. Additionally, we elucidate the significance of agricultural foundation models as fundamental technologies within this context. A case analysis of water usage shows that, given the limited availability of water resources in the context of the carbon-water balance, regional collaboration and optimized allocation have the potential to enhance the utilization efficiency of water resources in the river basin. A suggested approach is to consider the river basin as a unified entity and coordinate the relationship between the

关键词： Carbon-water balance decision-support digital intelligence(DI) foundation models planning

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Design of an Anti-Windup Equivalent-Input-Disturbance-Approach-Based control system

Design of an Anti-Windup Equivalent-Input-Disturbance-Approa...

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IEEE Conference on Industrial Electronics and Applications (ICIEA)

作者： Qicheng Mei Jinhua She Yosuke Nakanish School of Automation China University of Geosciences Wuhan China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan China Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Wuhan China School of Engineering Tokyo University of Technology Hachioji Tokyo Japan Graduate School of Environment and Energy Engineering Waseda University Shinjuku-ku Japan

ISBN: (纸本)9781665409858

This paper presents an anti-windup equivalent-input-disturbance (AEID) approach to eliminating both influences of disturbances and input saturation of a plant. The AEID approach has a conventional EID estimator and a newly added anti-windup structure. The EID estimator estimates the disturbances and uses estimated results to compensate for them. The anti-windup structure combats the influences of the input saturation. Numerical simulation verifies the effectiveness of the AEID approach.

关键词： Industrial electronics Simulation control systems Numerical simulation

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Blockchain based digital asset management system architecture for power grid big data

arXiv

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arXiv 2018年

作者： Jun, Zhang Fei-Yue, Wang State Key Laboratory of Management and Control for Complex System Institute of Automation Chinese Academy of Sciences Beijing100190 China School of Electrical Engineering Wuhan University Wuhan430072 China Qingdao Academy of Intelligent Industries Qingdao266071

Chinese power grid enterprises are in need for development of digital asset management system. The characteristics of decentralization, self-trust and self-confidence, pave a promising technical path for power grid digital assess management and Big Data applications. This article firstly introduces the state-of-the-art of power grid Big Data and digital asset management, and the related issues in power grid enterprises. The solution of blockchain based digital asset management technology and its implementation are presented in details, followed by a discussion of their future developmental directions. Copyright © 2018, The Authors. All rights reserved.

关键词： Big data

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A Subspace-Based Non-Dominated Subset Selection Method

A Subspace-Based Non-Dominated Subset Selection Method

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Congress on Evolutionary Computation

作者： Qingshan Tan Changhe Li Sanyou Zeng Shengxiang Yang School of Automation China University of Geosciences Wuhan China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Wuhan China School of Mechanical Engineering and Electronic Information China University of Geosciences Wuhan China School of Computer Science and Informatics De Montfort University Leicester United Kingdom

Environmental selection is an important process in multi-objective evolutionary algorithms (MOEAs). As the evolution progresses, the number of non-dominated solutions increases. This paper is focused on selecting a subset from excess non-dominated solutions for evolutionary or the final output. However, traditional selection methods in classical MOEAs encounter difficulties when dealing with candidate solutions that possess irregular topologies. Although the distance-based subset selection methods are not sensitive to the topologies of the candidate points, they have significant room for reducing computational complexity. In order to address the above issues, a subspace selection method is proposed in this paper. It partitions the objective space into multiple subspaces that have comparable volumes and shapes. The maximal minimum distance of each solution is considered to ensure that the sparsest solution is always chosen first. To save computational costs, only the solutions in the neighboring subspaces are taken into account. The experimental results demonstrate that the proposed subspace selection method outperforms classical selection methods in solving problems with various shapes of the Pareto front.

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A Fault Detection Method for Power Transmission Lines Using Aerial Images

A Fault Detection Method for Power Transmission Lines Using ...

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International Conference on Unmanned Aircraft systems (ICUAS)

作者： Yulong Zhang Shaowei Cao Lingxia Mu Xianghong Xue Jing Xin Youmin Zhang Shaanxi Key Laboratory of Complex System Control and Intelligent Information Processing School of Automation and Information Engineering Xi'an University of Technology Xi'an Shaanxi China Department of Mechanical Industrial and Aerospace Engineering Concordia University Montreal Quebec Canada

ISBN: (数字)9798350357882

ISBN: (纸本)9798350357899

It is necessary to regularly detect faults to maintain the safety and stability of power lines. Insulators are one of the important electrical components in high-voltage transmission lines. It is extremely necessary to check the working status of insulators regularly. Traditional manual inspection is inefficient because it requires a significant amount of labor costs. In this paper, a method for detecting insulators' missing defect based on aerial images is proposed to address the issue by unmanned aerial vehicle (UAV). Firstly, the improved Faster R-CNN (region-based convolutional neural network) is used to identify and locate insulators in aerial images. Secondly, the U-Net image segmentation network segments insulators from the images. The adaptive threshold segmentation method completely separates the insulator from the background. Then the binary image of the insulator is obtained. Finally, the binary image is converted into a fault curve which is used for determining the missing insulators based on the distribution of the fault curve. By using collected insulator datasets on a 330kV overhead transmission line using a DJI M300 UAV platform and an onboard H20T camera/sensor, the detection accuracy of glass insulators is as high as 0.98 with the proposed algorithm. The positioning accuracy of the proposed algorithm is also higher than other algorithms. This method has high detection accuracy for missing defects in insulators. The experimental results show that compared with similar algorithms, this method has higher accuracy and efficiency.

关键词： Image segmentation Accuracy Power transmission lines Glass Manuals Power system stability Insulators

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Fast control Strategy of Grid-forming Inverter Based on Power Feedforward

Fast Control Strategy of Grid-forming Inverter Based on Powe...

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Chinese control Conference (CCC)

作者： Sheng Zhang Zhijian Fang Qine Liu Muchao Xiang Dingbang Huang School of Automation China University of Geosciences Wuhan China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan China Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Wuhan China State Grid Hubei Electric Power Co. Ltd Xiangyang Power Supply Company Xiangyang China Electric Power Scientific Research Institute of State Grid Hubei Electric Power Co. Ltd Wuhan China

Grid-forming inverter is widely used in grid-connected systems of distributed generation because of its frequency and voltage support capacity and good stability in microgrid, but its large inertia will affect the dynamic response speed of grid-forming inverter. In order to solve this problem, this paper introduces the loop that affects the dynamic response of grid-forming inverter, and carries out small signal modeling for active loop, analyzes the dynamic performance indicators and determinants of typical second-order system. Thus, a method of adding power feedforward coefficient to the forward channel of the power loop is designed, and the response speed of the system with or without feedforward coefficient under the unit step response is compared. Finally, the simulation results show that adding the power feedforward coefficient can improve the response speed of the grid-forming inverter during startup and power switching, then achieves the effect of fast control.

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Real-world Robot Reaching Skill Learning Based on Deep Reinforcement Learning

Real-world Robot Reaching Skill Learning Based on Deep Reinf...

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第32届中国控制与决策会议

作者： Naijun Liu Tao Lu Yinghao Cai Rui Wang Shuo Wang State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences University of Chinese Academy of Sciences Guangdong Provincial Key Lab of Robotics and Intelligent System Shenzhen Institutes of Advanced TechnologyChinese Academy of Sciences Center for Excellence in Brain Science and Intelligence Technology of the Chinese Academy of Sciences

Traditional programming method can achieve certain manipulation tasks with the assumption that robot environment is known and ***,with robots gradually applied in more domains,robots often encounter working scenes which are complicated,unpredictable,and *** overcome the limitation of traditional programming method,in this paper,we apply deep reinforcement learning(DRL) method to train robot agent to obtain skill *** policy trained with DRL on real-world robot is time-consuming and costly,we propose a novel and simple learning paradigm with the aim of training physical robot ***,our method train a virtual agent in an simulated environment to reach random target position from random initial ***,virtual agent trajectory sequence obtained with the trained policy,is transformed to real-world robot command with coordinate transformation to control robot performing reaching *** show that the proposed method can obtain self-adaptive reaching policy with low training cost,which is of great benefits for developing intelligent and robust robot manipulation skill system.

关键词： Robot reaching skill learning deep reinforcement learning

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Spin-Orbit Torque Driven Memristor in Bulk L10 FePt system for Neuromorphic Computing

SSRN

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SSRN 2022年

作者： Tao, Ying Sun, Chao Li, Wendi Wang, Cen Jin, Fang Zhang, Yue Guo, Zhe Zheng, Yuhong Wang, Xiaoguang Dong, Kaifeng 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 Wuhan National Laboratory for Optoelectronics Huazhong University of Science & Technology Wuhan430074 China Changxin Memory Technologies Inc. Hefei230601 China

In this study, the memristor driven by spin-orbit torque (SOT) is realized in the bulk L1 0 FePt systems with high perpendicular magnetization anisotropy (PMA). Due to the domain nucleation and expansion driven by current pulses, multi-level Hall resistances can be continuously tuned by the current density, where the memristive states are retained by the domain wall pinning effects. The properties of multi-level resistances for samples with different structures are associated with the magnitude of field-like torque, and the larger efficiency of field-like torque enhances the multiple resistance characteristics. Furthermore, the memristive behaviors in the field-free condition are obtained by utilizing the interlayer exchange coupling. Finally, based on the memristive characteristics of our devices, the three-layer multilayer perceptron (MLP) neural network is built to perform the MNIST handwritten digit recognition task, and the accuracy of weight update can reach up to 88.55%. These results pave the way for the application of SOT-driven memristors in neuromorphic computing. © 2022, The Authors. All rights reserved.

关键词： Memristors

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A Swing-Up and Stable control Strategy for Pendubot Based on Nonlinear Model Predictive Method

A Swing-Up and Stable Control Strategy for Pendubot Based on...

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Chinese automation Congress (CAC)

作者： Lejun Wang Yiqin Liu Ruihuan Zhang Zixin Huang Zhen Cai School of Automation/School of Industrial Internet Chongqing University of Posts and Telecommunications Chongqing China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan China Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Wuhan China Hubei Key Laboratory of Digital Textile Equipment Wuhan Textile University Wuhan China School of Electrical and Information Engineering Wuhan Institute of Technology Wuhan China School of Information Engineering Wuhan City Polytechnic Wuhan China

The control objective of Pendubot is to stabilise the system in a vertically upward position by swinging up from a vertically downward position. Facing various physical constraints in practical applications, this paper proposes a swing-up stabil-isation control strategy based on a nonlinear model prediction method to accomplish the above control objective. On the basis of the system dynamics model, the forward Euler method is used to construct a nonlinear prediction model of the system. According to the control objectives and physical constraints of the system, the performance index function is designed, and then the control inputs of each sampling cycle are obtained through rolling optimisation and feedback correction, so that the system state gradually converges to the target trajectory, thus achieving the motion control objective. Based on the simulation results, it can be seen that the strategy proposed in this paper is efficient and robust under external disturbances.

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