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Oscillation Trajectory Optimization and control Method for Two-Link Underactuated Manipulators

Oscillation Trajectory Optimization and Control Method for T...

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Chinese control Conference (CCC)

作者： Lejun Wang Huiming Wang Xianlun Tang Zixin Huang Chongqing Key Laboratory of Complex Systems and Bionic Control Chongqing University of Posts and Telecommunications Chongqing 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 Hubei Key Laboratory of Digital Textile Equipment Wuhan Textile University Wuhan China School of Electrical and Information Engineering Wuhan Institute of Technology Wuhan China

This paper provides an oscillation trajectory optimization and control method for two-link underactuated manipulators (UMs) in a vertical plane. The proposed method solves the problem that the UMs cannot always enter the balance region in the partitioning method. First, we establish the system dynamic model, and analyze the system couple characteristics. Then, we program an oscillation trajectory for the active link, and use the intelligent method to obtain the trajectory parameters, so ensuring the system can reach the area adjacent to the target position through tracking control. Next, we design the controller to realize the stable control at the target position. Finally, the simulation results show the effectiveness and generality of the control strategy.

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Design of a novel flexible assistive hand exoskeleton 37

Design of a novel flexible assistive hand exoskeleton

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37th Youth Academic Annual Conference of Chinese Association of Automation, YAC 2022

作者： Li, Guotao Cheng, Long Gao, Zhengrun Lu, Jilong Zhao, Yanjiang Institute of Automation State Key Laboratory of Management and Control for Complex Systems Chinese Academy of Sciences Beijing100190 China Fudan University The National Clinical Research Center for Aging and Medicine Shanghai China Harbin University of Science and Technology Key Laboratory of Advanced Manufacturing and Intelligent Technology Ministry of Education Harbin150080 China Intelligent Machine Institute Harbin University of Science and Technology Harbin150080 China

ISBN: (纸本)9781665465366

Hand paralysis caused by stroke, spinal cord injury, or neurological trauma has a significant impact on the independence and quality of life of the patients. The hand exoskeleton can provide hand assistance and improve the patient's grasp. In this article, a novel wearable flexible hand exoskeleton is proposed to assist users in activities of daily life. To create a simple and lightweight assistive hand exoskeleton, 3D printing is used to manufacture most of the components, and a remote drive system is designed to reduce the weight of the hand exoskeleton. A ball pair device and a slider device are used to ensure the freedom of the thumb and the adaptability of the hand exoskeleton to different hand sizes. Finally, the experiments are conducted to verify that the hand exoskeleton can achieve motion assistance. © 2022 IEEE.

关键词： 3D printing

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Online Identification of Boost Converter Components with Multiple Updating RLS Algorithm

Online Identification of Boost Converter Components with Mul...

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Chinese control Conference (CCC)

作者： Tong Zhou Zhicheng Zhang Zhiqiang Zuo Yijing Wang Peng Li Tianjin Key Laboratory of Intelligent Unmanned Swarm Technology and System School of Electrical and Information Engineering Tianjin University Tianjin P. R. China Key Laboratory of System Control and Information Processing Ministry of Education Shanghai P. R. China

In this paper, component parameters of the boost converter are identified online using a multiple updating recursive least squares (MURLS) algorithm. The component parameters, such as resistance, inductor inductance and capacitor capacitance, are obtained directly through the identification procedure rather than transfer function coefficients. The MURLS algorithm is applied to improve the rapidity of system identification compared to the traditional recursive least squares (RLS) algorithm, which is verified by a comparative simulation between MURLS and RLS and the simulation of a load-switching scenario.

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Virtual inertia control strategy for PV using DC capacitive and electrochemical energy storage 4

Virtual inertia control strategy for PV using DC capacitive ...

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4th IEEE International Conference on Power, intelligent Computing and systems, ICPICS 2022

作者： Jiang, Zhe Ma, Linlin Gao, Zhimin Cao, Yongji Qin, Hao Yang, Dong Ma, Huan Fang, Qiao State Grid Shandong Electric Power Research Institute State Grid Shandong Electric Power Company Jinan China Shandong Power Dispatch & Control Center State Grid Shandong Electric Power Company Jinan China Key Laboratory of Power System Intelligent Dispatch and Control of the Ministry of Education Shandong University Jinan China

ISBN: (数字)9781665467735

ISBN: (纸本)9781665467735

With the increasing penetration of renewable energies, the power system inertia is decreasing, and there is an urgent need for wind and PV generations to participate in frequency response. PV units are stationary power generating units and usually work in maximum power tracking mode. It is difficult to provide inertia support to enhance the power system frequency characteristics. Therefore, this paper proposes a frequency control strategy based on the DC capacitor energy storage on the high voltage side of PV generation, using its transient discharge process to make it participate in the frequency response. Considering the limited energy stored in the capacitor, droop control and virtual inertia control are added to the electrochemical energy storage for inertia compensation, and a cooperative inertia control strategy is proposed for PV and energy storage system. A case study is carried out to verify the effectiveness of the proposed control strategy. © 2022 IEEE.

关键词： Frequency response

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Large Receptive Field Boundary Matching Networks for Generating Better Proposals

Large Receptive Field Boundary Matching Networks for Generat...

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第40届中国控制会议

作者： Lei Xu Jiexi Liu Bo Chen Beijing Institute of Technology School of Automation Key Laboratory of Complex System Intelligent Control and Decision

Temporal Action Detection is a research hotspot in Video *** is necessary not only to give the specific moments of the beginning and end of each action instance in the video,but also to give the category of the action *** present,most of the methods in temporal action detection are divided into two steps:The video is divided into a series of video clips to determine whether each clip is an action instance,and the fragments that are not an action instance are deleted;Segments that may be action instances are then classified to get the final *** present,the difficulties in action detection research mainly include the following two points:1) The boundary is not *** from action recognition,action detection requires precise positioning,but an action in life is often not very *** is also the reason why the mean average precision(mAP) of action detection is low at present.2) The time span is *** life,an action often spans a very long *** movements such as waving can last for a few seconds,while long movements such as rock climbing or cycling can last for tens of minutes,which makes it extremely difficult for us to extract *** view of the above difficulties,this paper proposes a large receptive field boundary matching network(LRFBMN) model which takes advantage of the relationship between proposals to improve the accuracy of proposal *** model is mainly divided into two parts:1) Clipping feature map is processed by large kernel convolution,and then proposal feature is generated by ROI-pooling;2) The proposals are arranged in a certain order to form a fixed graph,and the information exchange between graph nodes is realized by using convolution with *** experiments,this model is 2.35% higher than baseline and 1.06% higher than state-of-the-art in THUMOS14 data set.

关键词： Temporal Action Detection Video Understanding Proposal Large Receptive Field

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Optimal Sequential False Data Injection Attack Scheme: Finite-Time Inverse Convergence

Optimal Sequential False Data Injection Attack Scheme: Finit...

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IEEE Conference on decision and control

作者： Xiaoyu Luo Chongrong Fang Chengcheng Zhao Peng Cheng Jianping He The Department of Automation Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai Jiao Tong University Shanghai Engineering Research Center of Intelligent Control and Management Shanghai China The State Key Laboratory of Industrial Control Technology Institute of Cyberspace Research Zhejiang University China

In this paper, we explore the relationship between the injected attack signal and the attack selection strategy in networked control systems where the adversary desires to steer the system state to the expected malicious one. We construct a sequential attack framework, i.e., the injected false data varies with the sampling time in discrete-time systems, and then derive an optimal sequential FDI attack strategy. The optimal sequential FDI attack strategy reveals the strongly coupled relationship between the injected attack signal and the attack selection strategy. Furthermore, we prove the finite-time inverse convergence of the critical parameters in the injected optimal attack signal by discrete-time Lyapunov analysis, which enables the efficient off-line design of the attack strategy and saves computing sources. Extensive simulations are conducted to show the effectiveness of the injected optimal sequential attack and the relationship between the attack signal and the attack selection strategy.

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Power system Equivalent Inertia Estimation Method Using system Identification 5

Power System Equivalent Inertia Estimation Method Using Syst...

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5th IEEE International Conference on Electronics Technology, ICET 2022

作者： Ma, Huan Yang, Dong Qin, Hao Cao, Yongji Cheng, Dingyi Zhang, Bing Hao, Xudong Zhou, Ning State Grid Shandong Electric Power Research Institute State Grid Shandong Electric Power Company Jinan China Shandong University Key Laboratory of Power System Intelligent Dispatch and Control Ministry of Education Jinan China Shandong Power Dispatch & Control Center State Grid Shandong Electric Power Company Jinan China

ISBN: (纸本)9781665485081

As the proportion of renewable energy sources increase, the inertia of the power system continues to decrease, leading to a serious impact on frequency security and stability. There is an urgent need for an online estimation of the equivalent inertia of power systems. Most of the existing inertia estimation methods are based on the rate of change of frequency under large disturbance scenarios. According to the measurement data under normal operation, this paper estimates the equivalent inertia using system identification. The inertia response of the power system is analysed and an expression for the equivalent inertia is derived. An improved numerical algorithm based on subspace state space system identification and a least squares estimation method is used to calculate the equivalent inertia of the system. The validity of the proposed method is verified in a WSCC 9-bus power system case, showing that the proposed method has a high identification accuracy and can be adapted to different forms of load fluctuations. © 2022 IEEE.

关键词： Numerical methods

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DOFF-RLS-Based Equivalent Inertia Estimation for Power system with PV 4

DOFF-RLS-Based Equivalent Inertia Estimation for Power Syste...

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4th IEEE International Conference on Power, intelligent Computing and systems, ICPICS 2022

作者： Ma, Huan Yang, Dong Qin, Hao Cao, Yongji Gao, Zhimin Zhang, Bing Cheng, Dingyi Zhang, Zhixuan State Grid Shandong Electric Power Research Institute State Grid Shandong Electric Power Company Jinan China Key Laboratory of Power System Intelligent Dispatch and Control The Ministry of Education Shandong University Jinan China Shandong Power Dispatch & Control Center State Grid Shandong Electric Power Company Jinan China

ISBN: (数字)9781665467735

ISBN: (纸本)9781665467735

With the use of photovoltaic (PV) generation, a large number of synchronous generation units have been replaced, resulting in obvious decline in the equivalent inertia of power system. Facing a lack of inertia, virtual inertia control techniques are applied to PV, but there is a lack of methods to estimate the equivalent inertia of PV in real time, making it difficult to quantitatively estimate the effect of control method with virtual inertia. Therefore, this paper adopts the controlled autoregressive (CAR) identification model and the recursive least square algorithm of dynamic optimal forgetting factor (DOFF-RLS) to estimate the PV equivalent inertia, in order to address the time varying characteristics of the PV equivalent virtual inertia. The proposed identification model and solution algorithm have superior tracking capability and good convergence. All that is required is to obtain the disturbance data of frequency and active power from the point of common coupling from the phasor measurement unit (PMU). Finally, the accuracy and suitability of the identification model and method are verified by simulation. © 2022 IEEE.

关键词： Virtual addresses

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Interpretable analysis of feature importance and implicit correlation based on sEMG grayscale. images

Interpretable analysis of feature importance and implicit co...

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Industrial Cyber-Physical systems (ICPS)

作者： Xiaohu AO Feng Wang Juan Zhao Jinhua She School of Automation China University of Geosciences Wuhan China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex System 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

For patients requiring upper limb rehabilitation, the hand rehabilitation robot assists the patient in completing movements within a certain training trajectory to achieve therapeutic results. There have been studies based on deep learning to convert surface electromyography (sEMG) signals into sEMG images for motion intention analysis. Although good recognition accuracy has been achieved, the working principle of neural networks and the processing of image features by the networks are not well explained. The interpretability of deep neural networks determines human confidence in neural network decisions. In this paper, we design a method based on feature importance and implicit correlation for hand motion intention recognition, experimentally explored that convolutional neural networks have implicit definitions for sEMG grayscale images of the same hand gesture action, and verified the effectiveness of the designed method.

关键词： Deep learning Analytical models Correlation Image recognition Neural networks Thumb Gray-scale

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ADFNet: A two-branch robotic grasping network based on attention mechanism

ADFNet: A two-branch robotic grasping network based on atten...

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第40届中国控制会议

作者： Pengwei Xie Nan Hao Bo Chen Beijing Institute of Technology School of Automation Key Laboratory of Complex System Intelligent Control and Decision

One of the challenges of RGB-D-based robotic grasping technology is how to make full use of these two complementary heterogeneous data sources while ensuring real-time performance. The existing robotic grasping methods mainly extract information from single-mode images or use a one-branch network to process RGB and depth images. These methods are insufficient in fully fusing the effective information of the two modes, which limits the anti-jamming performance. In this work,we propose Attention Dense Fusion Network（ADFNet）, a novel RGB-D based robotic grasping system that directly predicts the optimal grasping pose from RGB-D images. Our system uses a two-branch network with a heterogeneous framework that processes RGB and depth image information separately and retain the original structure of each data source. After applying dense fusion networks at different scales, the high-dimensional features in the RGB and depth branches are embedded and fused at the pixel level. Besides, we incorporate attention mechanisms to effectively suppress the independent background regions of the feature map and enhance the significant features, which improves the prediction accuracy. We conduct qualitative and quantitative experiments on the standard Cornell Grasp Dataset. The experimental results show that ADFNet can effectively improve the prediction accuracy to 98.9 %, which is better than existing methods while ensuing real-time performance.

关键词： robotic grasping RGB-D attention mechanism

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