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第37届中国控制会议

作者： Yawu Wang Xuzhi Lai Pan Zhang School of Automation China University of Geosciences Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems

This paper presents a two-stage position control strategy based on the differential evolution(DE) algorithm for a planar second-order nonholonomic manipulator,which has one passive joint and this passive joint is not the first joint(planar APA manipulator for short,where m≥1,n≥0).An offline DE algorithm is used to calculate all link target angles corresponding to the target *** to these target angles,a Lyapunov function is constructed to design the controllers A for the control stage 1,which are used to control all active links to their target *** to the constraint equation of the planar APA manipulator,an oscillatory trajectory is planned for the first active link based on the online DE *** the first active link tracks the oscillatory trajectory,it will back to its target angle ***,the passive link will be jointly controlled to its target angle ***,the other Lyapunov function is constructed to design the controllers B for the control stage 2,which are used to control the first active link tracks the target trajectory and control the remaining m+n-1 active links maintain in their target *** results of a planar AAPA manipulator demonstrate the effectiveness of the proposed control strategy.

关键词： Planar second-order nonholonomic manipulator Differential evolution algorithm Lyapunov function Online intelligent trajectory planning

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Hyperspectral Remote Sensing Image Classification Based on Convolutional Neural Network

Hyperspectral Remote Sensing Image Classification Based on C...

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第37届中国控制会议

作者： Xiangyang Dai Wei Xue School of Automation China University of Geosciences Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems

Remote sensing hyperspectral imaging can obtain rich spectral information of terrestrial objects, which allows the indistinguishable matter in the traditional wideband remote sensing to be distinguished in hyperspectral remote *** image has the characteristics of "combining image with spectrum". Making full use of spectral information and spatial information in hyperspectral image is the premise of obtaining accurate classification results. At present, most of hyperspectral data feature extraction algorithms mainly utilize local spatial information in the same channel and spectral information in the same spatial location of different channels. However, these methods require a large amount of prior knowledge, it is difficult to fully grasp the hyperspectral data of all spatial and spectral information, and the model generalization ability is poor. With the development of deep learning, convolutional neural network shows superior performance in all kinds of visual tasks, especially in the two-dimensional image classification, and could get a high classification accuracy. In this paper, an image classification method based on three-dimensional convolution neural network is proposed based on the structural properties of hyperspectral data. In the proposed method, first the stereo image blocks of hyperspectral data are intercepted, then multi-layer convolution and pooling operation of extracted blocks by convolutional neural network are implemented to obtain the essential information of hyperspectral data, finally the classification of hyperspectral data is completed. The experimental results show the proposed method could provide better feature expression and classification accuracy for hyperspectral image.

关键词： Convolutional Neural Network Hyperspectral Image Deep Learning Classification

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Observer-Based Tracking control for Suppressing Stick-Slip Vibration of Drillstring System

Observer-Based Tracking Control for Suppressing Stick-Slip V...

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第37届中国控制会议

作者： Jun Cheng Min Wu Luefeng Chen School of Automation China University of Geosciences Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems

A control method is proposed to suppress stick-slip vibration of drillstring system based on tracking control with zero steady-state error and state observer in this paper. A multiple degree-of-freedom(DOF) model of drillstring dynamic is presented first, which considers high-order modal of stick-slip vibration. Then, state observer is constructed to estimate the states of drillstring system, whose states are usually difficult to measure in the top of drillstring system. Finally, combing state feedback control and internal model principle, a tracking control with zero steady-state error is proposed to ensure the speed of rotary table and bit are consistent. The proposal only need top measurement, can eliminate multiple torsional modes, and has a strong robustness. Simulations show the effective of the proposal.

关键词： Stick-slip vibration Multi-DOF model Tracking control State observer

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H∞ control for singular systems with time-varying delay based on augmented Lyapunov-Krasovskii functional

H∞ control for singular systems with time-varying delay bas...

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第37届中国控制会议

作者： ZHI Ya-Li HE Yong School of Automation China University of Geosciences Hubei key Laboratory of Advanced Control and Intelligent Automation for Complex Systems

This paper concers the H∞ control for singular systems with time-varying ***,an augmented LyapunovKrasovskii functional（ALKF） is constructed by adding some integral terms which are dependent on the singular matrix to the quadratic ***,to handle the derivative of the ALKF,a modified relaxed integral inequality is *** a result,a sufficient condition with a state feedback controller is derived to ensure the closed-loop system to be regular,impulse free and asymptotically stable with H∞ ***,a numerical example is given to verify the effectiveness and the superiority of the proposed method.

关键词： H∞ control Singular systems with time-varying delay Augmented Lyapunov-Krasovskii functional Relaxed integral inequality State feedback

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GMI Effect Automatic Test System Based on Network Analyzer

GMI Effect Automatic Test System Based on Network Analyzer

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第37届中国控制会议

作者： Zhi Zhao Hengchang Rao Fang JIn Jinchao Wang Lei Zhu Junlei Song Kaifeng Dong Wenqin Mo School of Automation China University of Geosciences Hubei key Laboratory of Advanced Control and Intelligent Automation for Complex Systems

The measuring instrument selects the network analyzer, combines the direct current source and the Helmholtz coil to form the GMI effect test system. PC program written in Lab VIEW, network analyzer and DC current source is controlled by the host computer. The host computer can process and plot the impedance data collected by the network analyzer, and save the data and images locally. The test system by measuring the commercial Vitrovac6025 strip material, found that the material all showed a strong GMI effect at different frequencies. By comparing the measured data of the network analyzer and the impedance analyzer measured in the same frequency band, we found that the two are consistent, which verifies the accuracy and reliability of the test system.

关键词： Giant magneto-impedance（GMI） LabVIEW Network Analyzer

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Synchronization control of star network based on quorum sensing mechanism 8

Synchronization control of star network based on quorum sens...

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8th International Symposium on Computational Intelligence and Industrial Applications and 12th China-Japan International Workshop on Information Technology and control Applications, ISCIIA and ITCA 2018

作者： Liu, Feng Mateus, Felix Mei, Qicheng Wu, Xianyong School of Automation China University of Geosciences Wuhan430074 China Hubei key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan430074 China College of Electrical Engineering and New Energy China Three Gorges University Yichang443002 China

In this paper, we combined the quorum sensing mechanism of bacteria with the Repressilator oscillator to describe the rhythmicity of bacteria. It is found that the transmission of signal molecules between cells produces a star network, and the dynamical equation of the star network is given. Then, the error projection synchronization of star networks is defined and analyzed. A Lyapunov function is constructed to prove that the error system will be close to zero within a certain time under the control of the controller. Finally, a four node star network is used for simulation. The results show that the controller can achieve the desired results. © ISCIIA and ITCA 2018 - 8th International Symposium on Computational Intelligence and Industrial Applications and 12th China-Japan International Workshop on Information Technology and control Application.

关键词： Synchronization

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Optimization of Grouping Evacuation Strategy in High-rise Building Fires Based on Graph Theory and Computational Experiments

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IEEE/CAA Journal of Automatica Sinica 2018年第6期5卷 1104-1112页

作者： Yuling Hu Xiwei Liu School of Electrical and Information Engineering Beijing University of Civil Engineering and Architecture Beijing 100044China Beijing Key Laboratory of Intelligent Processing for Building Big Data Beijing 100044China State Key Laboratory fo Management and Control for Complex Systems Institute of Automation Chinese Academy of SciencesBeijing 100190China Qingdao Academy of Intelligent Industries Qingdao 266109China

It is difficult to rescue people from outside, and emergency evacuation is still a main measure to decrease casualties in high-rise building fires. To improve evacuation efficiency, a valid and easily manipulated grouping evacuation strategy is proposed. Occupants escape in groups according to the shortest evacuation route is determined by graph theory. In order to evaluate and find the optimal grouping, computational experiments are performed to design and simulate the evacuation processes. A case study shown the application in detail and quantitative research conclusions is obtained. The thoughts and approaches of this study can be used to guide actual high-rise building evacuation processes in future.

关键词： Computational experiments grouping evacuation graph theory high-rise building fire management strategies

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Residential virtual power plant with photovoltaic output forecasting and demand response

Residential virtual power plant with photovoltaic output for...

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作者： Cui, Shichang Wang, Yan-Wu Lin, Xiangning Xiao, Jiang-Wen School of Automation Huazhong University of Science and Technology Wuhan China Key Laboratory of Image Processing and Intelligent Control Ministry of Education Huazhong University of Science and Technology Wuhan China Hubei Provincial Collaborative Innovation Center for New Energy Microgrid China Three Gorges University Yichang China State Key Laboratory of Advanced Electromagnetic Engineering and Technology Huazhong University of Science and Technology Wuhan China

Decreasing conventional power supply is promoting the development of the distributed renewable energy sources, such as solar power and wind power. Recently rooftop photovoltaic has been widely applied, and accordingly efficient energy management is getting increasingly important for fully use of renewable energy and the peak shaving of the main grid. This paper investigates the residential energy management as a small-scale virtual power plant (VPP) connected to the main grid includes distributed energy resources, energy storages and residential loads. The self-organizing map (SOM) and the radical basis function (RBF) networks are adopted to classify the weather types and predict hourly photovoltaic output precisely. In a time-of-use electricity market, price-based demand response is applied to adjust the demand. The residential VPP has two goals: maximum profit by selling surplus power to grid and minimum power purchased from grid. The two goals are integrated as an optimization object by introducing a weight parameter. The algorithm combining receding horizon optimization and linear programming is proposed to solve the optimization problem in residential VPP. Numerical simulation tests can help to find the most suitable value of the weight parameter. Different scenarios are simulated and discussed to demonstrate the performance of the VPP and the proposed algorithm. © 2019 Chinese Automatic control Society and John Wiley & Sons Australia, Ltd

关键词： Energy management

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A Grasping CNN with Image Segmentation for Mobile Manipulating Robot

A Grasping CNN with Image Segmentation for Mobile Manipulati...

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IEEE International Conference on Robotics and Biomimetics

作者： Yingying Yu Zhiqiang Cao Shuang Liang Zhicheng Liu Junzhi Yu Xuechao Chen State Key Laboratory of Management and Control for Complex Systems Chinese Academy of Sciences Beijing China Beijing Advanced Innovation Center for Intelligent Robots and Systems Intelligent Robotics Institute BIT Beijing China

This paper presents a grasping convolutional neural network with image segmentation for mobile manipulating robot. The proposed method is cascaded by a feature pyramid network FPN and a grasping network DrGNet. The FPN network combined with point cloud clustering is used to obtain the mask of the target object. Then, the grayscale map and the depth map corresponding to the target object are combined and sent to the DrGNet network for providing multi-scale images. On this basis, depthwise separable convolution is used for encoding. The results of encoders are refined according to the light-weight RefineNet as well as sSE, which can achieve a better grasp detection. The proposed method is verified by the experiments on mobile manipulating robot.

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Traffic Congestion Prediction using Decision Tree, Logistic Regression and Neural Networks

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IFAC-PapersOnLine 2020年第5期53卷 512-517页

作者： Tariku Sinshaw Tamir Gang Xiong Zhishuai Li Hao Tao Zhen Shen Bin Hu Heruye Mulugeta Menkir State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences Beijing 100190 China School of Artificial Intelligence University of Chinese Academy of Sciences Beijing 100049 China Guangdong Engineering Research Center of 3D Printing and Intelligent Manufacturing Cloud Computing Center Chinese Academy of Sciences Dongguan 523808 China China Ship Development and Design Center Wuhan 430064 China Beijing Engineering Research Center of Intelligent Systems and Technology Institute of Automation Chinese Academy of Sciences Beijing 100190 China Debremarkos University Institute of Technology Debremarkos 269 Ethiopia

Traffic congestion is a serious problem around the world and to a great extent influences urban communities in various manners including increased stress levels, delayed deliveries, fuel wastage, and monetary losses. Therefore, an accurate congestion prediction algorithm to limit these misfortunes is fundamental. This paper presents a comparative study of traffic congestion prediction systems including decision tree, logistic regression, and neural networks. Five days of traffic information (1,231,200 samples) are utilized to drive the prediction model. The TensorFlow and the Clementine machine learning platforms are used for data preprocessing, training, and testing of the model. The confusion matrix clears that decision tree has better prediction performance and leads the other two methods with accuracy (97%), macro-average precision (95%), macro-average recall (96%), and macro-average F1_score (96%) in the python programming environment. Moreover, performance of the three prediction models is verified in Clementine environment and decision tree outperforms all other models with an accuracy of 97.65%.

关键词： Confusion matrix Data preprocessing Python TensorFlow Clementine environment ITS

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