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Chinese automation Congress (CAC)

作者： Hongrui Wang Tao Zou Zhijia Yang Hongyu Zheng State Key Laboratory of Robotics Shenyang Institute of Automation Chinese Academy of Sciences Shenyang China Key Laboratory of Networked Control System Chinese Academy of Sciences Shenyang China Chinese Academy of Sciences Institutes for Robotics and Intelligent Manufacturing Shenyang China University of Chinese Academy of Sciences Beijing China Shenyang China College of Mechanical and Electrical Engineering Guangzhou University Guangzhou China Institute of Intelligent Manufacturing Huanghuai University Zhumadian China

ISBN: (数字)9781728176871

ISBN: (纸本)9781728176888

In the dual time-scale system, the time for the fast and slow model to reach steady-state values is inconsistent. Based on the "point" model strategy of integral variables, this paper compares and analyzes the slow model variables with the integral variables and designs a two-layer structure predictive controller for the dual time-scale system under the model truncation. The steady-state optimization layer uses the step response characteristics of slow model variables to derive the multi-step controlled variable prediction values after the truncation point. Based on the improved displacement matrix, the error correction coefficient is modified by introducing an error compensation factor in the dynamic control layer to reduce the accumulated error of model truncation. Under the truncated dual time-scale system model, the improved strategy proposed in the paper can not only maintain the amount of calculation of the predictive control but also solve the problem that the steady-state target calculation is not accurate enough due to model mismatch. Finally, the elfectiveness of the improvement strategy is verified by simulation comparison.

关键词： Robots Optimized production technology Manufacturing control systems automation Target tracking Steady-state

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Spherical interpolated convolutional network with distance-feature density for 3D semantic segmentation of point clouds

arXiv

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arXiv 2020年

作者： Wang, Guangming Yang, Yehui Zhang, Huixin Liu, Zhe Wang, Hesheng Department of Automation Insititue of Medical Robotics Key Laboratory of System Control and Information Processing of Ministry of Education Key Laboratory of Marine Intelligent Equipment and System of Ministry of Education Shanghai Jiao Tong University Shanghai200240 China Beijing Advanced Innovation Center for Intelligent Robots and Systems Beijing Institute of Technology China Department of Computer Science and Technology University of Cambridge United Kingdom

The semantic segmentation of point clouds is an important part of the environment perception for robots. However, it is difficult to directly adopt the traditional 3D convolution kernel to extract features from raw 3D point clouds because of the unstructured property of point clouds. In this paper, a spherical interpolated convolution operator is proposed to replace the traditional grid-shaped 3D convolution operator. This newly proposed feature extraction operator improves the accuracy of the network and reduces the parameters of the network. In addition, this paper analyzes the defect of point cloud interpolation methods based on the distance as the interpolation weight and proposes the self-learned distance-feature density by combining the distance and the feature correlation. The proposed method makes the feature extraction of spherical interpolated convolution network more rational and effective. The effectiveness of the proposed network is demonstrated on the 3D semantic segmentation task of point clouds. Experiments show that the proposed method achieves good performance on the ScanNet dataset and Paris-Lille-3D dataset. Copyright © 2020, The Authors. All rights reserved.

关键词： Semantic Segmentation

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Research of Cartographer Graph Optimization Algorithm Based on Indoor Mobile Robot

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Journal of Physics: Conference Series 2020年第1期1651卷

作者： Jiali Xu Di Wang Maosheng Liao Wenshuai Shen School of Mechatronics Engineering and Automation Shanghai Key Laboratory of Intelligent Manufacturing and Robotics Shanghai University Shanghai China

In the recent years, mobile robot technology has received widespread attention from all over the world, and Simultaneous Localization and Mapping (SLAM) technology has always been a hot topic for researchers. In this paper, the Cartographer graph optimization SLAM algorithm is studied. Aiming at the problem that loopback detection may have wrong loopback points in the presence of noise, similar and uniform characteristics, a delay judgment module is added to filter the wrong loopback points. The Cartographer algorithm before and after optimization is deployed to the mobile robot platform based on ROS, and a 2D laser scanner is used for the map of indoor building. The experiment results prove that after the algorithm is improved, the overall relative error of the map is reduced by 1.148%, and the accuracy of mapping is improved.

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Research on PMSM Vector control System Based on Fuzzy PI Parameter Self-Tuning

Research on PMSM Vector Control System Based on Fuzzy PI Par...

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IEEE International Conference on Power and Energy Systems (ICPES)

作者： Jianhua Ding Tao Zhang Ran Tao Anna Wang Northeastern University Shenyang China Key Laboratory of Networked Control Systems Chinese Academy of Sciences Shenyang China Shenyang Institute of Automation Chinese Academy of Sciences Shenyang China Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Shenyang China Dalian University of Technology Dalian China

ISBN: (数字)9781665404945

ISBN: (纸本)9781665404952

Based on the analysis of the permanent magnet synchronous motor (PMSM) mathematical model, a PMSM rotor field-oriented double closed-loop vector control system is constructed. Aiming at the defects of slow speed response, large overshoot, severe torque ripple, and poor anti-disturbance ability in the traditional PI controller used in the system speed loop, a fuzzy PI parameter self-tuning controller is proposed. The controller can set the two parameters of proportional and integral online according to fuzzy control rules. The system simulation model is built based on Matlab/Simulink. The simulation results show that the fuzzy PI parameter self-tuning controller has the advantages of fast dynamic response, high steady-state precision and strong anti-disturbance capability.

关键词： PI control Systems simulation Steady-state Mathematical model Torque measurement Machine vector control Matlab

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Design of Overall Framework of Self-Service Big Data Governance for Power Grid 4th

Design of Overall Framework of Self-Service Big Data Governa...

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4th International Conference on Machine Learning and intelligent Communications, MLICOM 2019

作者： Qiao, Lin Zhou, Qiaoni Song, Chunhe Wu, Hao Liu, Biqi Yu, Shimao State Grid Liaoning Electric Power Co. Ltd. Shenyang110000 China Key Laboratory of Networked Control Systems Shenyang Institute of Automation Chinese Academy of Sciences Shenyang110016 China Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Shenyang110016 China

ISBN: (纸本)9783030323875

At present, power grid companies have not formed a complete data quality control system and a comprehensive and effective data quality assurance mechanism, which restricts the deep mining of data value. In this paper, based on the full-service unified data center of power Grid Company, a general framework of self-service power grid big data governance is presented. Firstly, the related work of big data governance is reviewed;secondly, the architecture and characteristics of the grid company’s full-service unified data center are analyzed;thirdly, the related requirements of self-service grid big data governance are analyzed;fourthly, the overall framework of self-service grid big data governance is proposed. Compared with other general big data governance framework, the proposed framework considers the specification of power grid as well as the features of self-service, making the framework more feasible. © 2019, ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering.

关键词： Big data

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A Data Quality Improvement Method Based on the Greedy Algorithm 4th

A Data Quality Improvement Method Based on the Greedy Algori...

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4th International Conference on Machine Learning and intelligent Communications, MLICOM 2019

作者： Wang, Zhongfeng Fu, Yatong Song, Chunhe Ge, Weichun Qiao, Lin Zhang, Hongyu Key Laboratory of Networked Control Systems Shenyang Institute of Automation Chinese Academy of Sciences Shenyang110016 China Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Shenyang110016 China State Grid Liaoning Electric Power Co. Ltd. 110000 Shenyang China

ISBN: (纸本)9783030323875

High-quality data is very important for data analysis and mining. Data quality can be indicated by many indicators, and some methods have been proposed for data quality improvement by improving one or more data quality indicators. However, there is few work to discuss the impact of the processing order of data quality indicators on the overall data quality. In this paper, first, some data quality indicators and their improvement methods are given;second, the impact of the processing order of data quality indicators on the overall data quality is discussed, and then a novel data quality improvement method based on the greedy algorithm is proposed. Experiments have been shown that the proposed method can improves the data quality while reducing the time and computational costs. © 2019, ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering.

关键词： Data reduction

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Research on optimization method of routing buffer linkage based on Q-learning

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Journal of Physics: Conference Series 2022年第1期2187卷

作者： Zhonghua Han Hongfeng Qi Daliang Chang Wei Gong Shilong Dai School of Information and Control Engineering Shenyang Jianzhu University Shenyang 110168 China. Key Laboratory of Networked Control Systems Chinese Academy of Sciences Shenyang 110016 China. Shenyang Institute of Automation Chinese Academy of Sciences Shenyang 110016 China. Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Shenyang 110169 China.

According to the characteristics of the painting process of passenger car manufacturing enterprises, by formulating the routing buffer linkage rules based on the total renewal cost, the linkage process of the bus in the routing buffer is controlled, and an improved Q-learning (Q- The routing buffer of learning) algorithm quickly finds the optimal path method. According to the actual production situation, this method improves the dynamic parameters of the algorithm on the basis of the traditional Q-learning algorithm, and improves the optimization speed and accuracy of the algorithm by establishing the correlation between the work-in-process and its neighboring work-in-process in the current state. Through multiple sets of example simulation tests, the effectiveness of the Q-learning algorithm in solving the optimization problem of routing buffer linkage is verified.

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Application of Iterative Learning control in X-Y Precision Planar Motion Platform

Application of Iterative Learning Control in X-Y Precision P...

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Chinese automation Congress (CAC)

作者： Hongyu Yan Danping Jia Tao Zhang Hualiang Zhang Yingjie Xiong Xu Lu Yang Liu School of Artificial Intelligence Shenyang University of Technology Shenyang China School of Information Science and Engineering Shenyang University of Technology Shenyang China Key Laboratory of Networked Control Systems Chinese Academy of Sciences Shenyang China Shenyang Institute of Automation Chinese Academy of Sciences Shenyang China Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Shenyang China School of Electrical Engineering Dalian University of Technology Dalian China College Of Information Science and Engineering Northeastern University Shenyang China

ISBN: (纸本)9781665426480

The size of the tracking error is one of the important indicators to measure the control system of the X-Y Precision Planar Motion Platform. Iterative learning control (ILC) can obtain higher tracking accuracy through multiple iterations. However, it is found in experiments that the tracking error of the XY precision motion stage using ordinary PD-type iterative learning will eventually fluctuate in a relatively range, and some high-frequency Noise will seriously affect the stability of the system after many iterations. Aiming at the above problems, a PD-type variable gain open-closed loop ILC with a low-pass filter is proposed. This control scheme can effectively remove high-frequency noise and reduce tracking errors. Finally, a hardware-in-the-loop real-time simulator is used to conduct a comparative experiment. Through comparison with PID controller and ordinary PD-type ILC, the experimental consequence show that this arithmetic has a smaller tracking error than the other two algorithms.

关键词： Tracking loops Tracking Low-pass filters control systems Size measurement Stability analysis Real-time systems

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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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Industrial robot rotate vector reducer fault detection based on hidden markov models

Industrial robot rotate vector reducer fault detection based...

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2019 IEEE International Conference on robotics and Biomimetics, ROBIO 2019

作者： Zhang, Yinlong An, Haibo Ding, Xiaojian Liang, Wei Yuan, Mingze Ji, Chunyang Tan, Jindong Shenyang Institute of Automation Guangzhou Chinese Academy of Sciences Guangzhou511548 China Key Laboratory of Networked Control Systems Shenyang Institute of Automation Chinese Academy of Sciences Shenyang110016 China Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Shenyang110016 China State Key Laboratory of Robotics Shenyang Institute of Automation Chinese Academy of Sciences Shenyang110016 China University of Chinese Academy of Sciences Beijing100049 China Fifth Electronic Instituteof MIIT Guangzhou510610 China Department of Mechanical Aerospace and Biomedical Engineering University of Tennessee KonxvilleTN37996 United States

ISBN: (纸本)9781728163215

Reliable fault detection of rotate vector (RV) reducer is of paramount importance for the long-term maintenance of high-precision industrial robots. This paper proposes a Hidden Markov Model (HMM) based RV reducer fault detection using Acoustic Emission (AE) measurements. Compared with the conventional faults from the common rotating machinery (such as bearings and gears), the fault from the RV reducer is more complicated and undetectable due to its inherent inline and two-stage meshing structure. To this end, this work modifies the HMM model by taking into account not only the current observations and previous states, but also the subsequent series of observations within the posteriori probability framework. Through this way, the random and unknown disturbance, which is common in the industrial scenarios, could be reduced. Besides, the HMM is also applied to separate the AE signal bulks within one cycle that has 39 subcycles, which is a critical step for AE signal pre-processings. The proposed method has been evaluated on our collected AE signal dataset from the RV reducer in the industrial robotic platform. The experimental results and analysis validate that the proposed HMM based RV Reducer fault detection model can reliably and accurately detect reducer faults. © 2019 IEEE.

关键词： Hidden Markov models

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