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检索条件"任意字段=10th IEEE Data Driven Control and Learning Systems Conference, DDCLS 2021"

共 359 条记录，以下是161-170 订阅

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Defect-sensitive testing data analysis method for industrial robots quality inspection 10

Defect-sensitive testing data analysis method for industrial...

引用

10th ieee data driven control and learning systems conference, ddcls 2021

作者： Sun, Heqing Liu, Zhanchi Zhang, Jiafan Research Team China Robotics and Discrete Automation Abb

ISBN: (纸本)9781665424233

Industrial robots play important parts in modern industries to increase factory capacity and guarantee final product quality. the qualified industrial robot product is critical to promise the OEE (overall equipment efficiency) at customer site. therefore, quality inspection is the indispensable part in robot manufacturing. To guarantee superior robot quality and excellent customer experience, the defect-sensitive testing method is proposed to detect robot defect issues, including defects of incoming materials and problems of robot assembly. Two main technologies applied here to improve defect sensitive are segmentation of running cycle and integration of physical model and data model. the results from the testing data analysis method could not only show the pass or not information of the detected robot, but also guide how to fix the problem if any defect issue is detected. this could sharply reduce dependence on expert knowledge and personal experience. © 2021 ieee.

关键词： Defects

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Structural Balance Preserving and Consensus of Uncertain Euler-Lagrange systems in Cooperation-Competition Networks 10

Structural Balance Preserving and Consensus of Uncertain Eul...

引用

10th ieee data driven control and learning systems conference, ddcls 2021

作者： Wang, Jia Zhou, Yingxia Hu, Hongxiang Hangzhou Dianzi University Hangzhou China

ISBN: (纸本)9781665424233

this paper studies the structural balance preserving and bipartite static consensus problem for multiple uncertain Euler-Lagrange systems in the state-dependent cooperation-competition network. the initial network is set to structural balance and connection, which implies that the network could be divided into two subnetworks, with cooperation internally while competition externally. A combination of the novel classification strategy and the distributed control protocol based on potential functions is given to solve this problem. Under this strategy and standard assumptions, the multiple uncertain Euler-Lagrange agents can maintain structural balance in cooperation-competition network and the bipartite static consensus can be reached in the evolution. Finally, the accuracy of the derived analytical results can be verified by a simulation example. © 2021 ieee.

关键词： Lagrange multipliers

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Dynamic Event-triggered Scheme and Output Feedback control for CPS under Multiple Cyber Attacks 10

Dynamic Event-triggered Scheme and Output Feedback Control f...

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10th ieee data driven control and learning systems conference, ddcls 2021

作者： Zhang, Zhigang Liu, Jinhai Zhang, Shuo Zhu, Hongfei Zhang, Baojin Collage of Information Science and Engineering Northeastern University Shenyang110819 China Yanqian Branch Office of Ansteel Mining Corporation Anshan114044 China

ISBN: (纸本)9781665424233

For a cyber physical system under multiple cyber attacks, including non-periodic denial-of-service (DoS) attack and stochastic deception attack, we design a dynamic output feedback controller with dynamic event-triggered strategy. We adopts a control strategy based on dynamic trigger conditions, which reduces the number of triggers and saves network resources. Besides, we establish a switched system model to describe the presence of multiple cyber attacks with dynamic event-triggered scheme. then, according to asymptotic stability theory, dynamic output feedback controller ensuring the switching system stable is designed by using a piecewise Lyapunov-Krasovskii function. Furthermore, the parameters of dynamc event-triggered and controller are derived in a unified framework and sufficient conditions for asymptotic stability can be obtained. © 2021 ieee.

关键词： Cyber Physical System

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On Dynamic Target Assignment Method of UAV Swarms Based on Cost Minimization 10

On Dynamic Target Assignment Method of UAV Swarms Based on C...

引用

10th ieee data driven control and learning systems conference, ddcls 2021

作者： Qiu, Xingye Miao, Boya Li, Yang Li, Linghao Liu, Geng Wu, Yanxia Liu, Sidong Systems Engineering Research Institute China State Shipbuilding Corporation Limited Beijing100094 China

ISBN: (纸本)9781665424233

For synergetic air interception targets of the unmanned aerial vehicle (UAV) swarms, this paper proposes a dynamic target assignment model of UAV swarms based on the cost minimization (DAMCM). Taking comprehensive consideration of factors such as interception feasibility, safely return, importance of targets and so on, we design and calculate the cost of UAVs intercepting different targets, and get the optimal assignment plan by minimizing total cost. the model allows assignment plan to be updated dynamically with good extensibility. In order to solve DAMCM model, an iterative genetic algorithm (iterGA) is designed based on genetic algorithm (GA). Simulation experiments show that DAMCM model can give a reasonable target assignment in line with expectations, and iterGA algorithm can calculate less cost than classical genetic algorithm. © 2021 ieee.

关键词： Integer programming

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Modeling and control for 3D Output Stochastic Distribution System Based on 2D PDF Models 10

Modeling and Control for 3D Output Stochastic Distribution S...

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10th ieee data driven control and learning systems conference, ddcls 2021

作者： Yang, Jingqi Zhou, Jinglin College of Information Science and Technology Beijing University of Chemical Technology 100029 China

ISBN: (纸本)9781665424233

this paper presents a novel modeling and control method for 3D output probability density function (PDF) of stochastic distributed systems. Firstly, considering the boundary constraints of the output PDF, linear B-spline functions are used to approximate the PDF of the system output variables, and the linear dynamic models of the X-axis and Y-axis are established respectively. Afterwards, on the basis of the 2D PDF models, a 3D PDF model is established. A controller is designed by optimizing the quadratic performance index, which makes the output PDF track the desired PDF effectively. Finally, an example is used to illustrate the effectiveness of the algorithm, and the expected results are achieved. © 2021 ieee.

关键词： Probability density function

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A Hybrid-driven Soft Sensor with Complex Process data Based on DAE and Mechanism-introduced GRU 10

A Hybrid-driven Soft Sensor with Complex Process Data Based ...

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10th ieee data driven control and learning systems conference, ddcls 2021

作者： Guo, Runyuan Liu, Han Wang, Wenqing Xie, Guo Zhang, Youmin School of Automation and Information Engineering Xi'an University of Technology Xi'an710048 China Concordia University Department of Mechanical Industrial and Aerospace Engineering MontrealQCH3G 1M8 Canada

ISBN: (纸本)9781665424233

With the increasing complexity of industrial processes, process big data inevitably has strong nonlinear, dynamic and noise problems, which restrict the accurate establishment of data-driven or mechanism-driven soft sensor models. therefore, a soft sensor based on the denoising autoencoder and mechanism-introduced gated recurrent units is proposed. the denoising autoencoder is used to denoise the data. the mechanism-introduced gated recurrent units are used to introduce the information contained in the mechanism model and extract the dynamic characteristics of the process data by deep learning. this soft sensing method can deal with various problems of complex process data and introduce mechanism model for hybrid-driven modeling, which improves the prediction performance of the soft sensor. the effectiveness and superiority of the method are verified by the industrial case of predicting the thermal deformation of an air preheater rotor. © 2021 ieee.

关键词： Deep learning

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Model-Free-Adaptive-control for Moving Object Detection in RGB Video Sequence 10

Model-Free-Adaptive-Control for Moving Object Detection in R...

引用

10th ieee data driven control and learning systems conference, ddcls 2021

作者： Sun, Guoqing Hou, Zhongsheng School of Automation Qingdao University Qingdao266071 China

ISBN: (纸本)9781665424233

Moving object detection in video sequences is an important topic in the field of computer vision. the video background image extraction is the key step in most moving object detection methods. the traditional background image extraction methods are easy to cause model distortion when the video scene cannot meet its assumptions or model conditions. In this paper, the background image extraction method is proposed using model free adaptive control. the method combines RGB three-channel history and current data of pixels to represent and update background image. the proposed method that under different video sequences is compared with the traditional background image extraction methods. the results show that the method can extract the color background image and separated the color foreground image, and the foreground image is closer to the ground truth image of the video target. © 2021 ieee.

关键词： Extraction

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Mask-based Object Pose Estimation with Domain Transfer 10

Mask-based Object Pose Estimation with Domain Transfer

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10th ieee data driven control and learning systems conference, ddcls 2021

作者： Ying, Yongkang Liu, Shan State Key Laboratory of Industrial Control Technology College of Control Science and Engineering Zhejiang University Hangzhou310027 China

ISBN: (纸本)9781665424233

Object pose estimation is important for robots to understand and interact with the real world. this problem is challenging because the various objects, clutter and occlusions between objects in the scene. Deep learning methods show better performances than traditional problems in this problem but training a convolutional neural network needs lots of annotated data which is expensive to obtain. this paper proposes a general method by using domain transfer technology to efficiently solve object pose estimation problem. Besides, the proposed method obtains mask to achieve high quality performance by combing an instance segmentation framework, Mask R-CNN. We present the results of our experiments with the LineMOD dataset. We also deploy our method to robotic grasp object based on the estimated pose. © 2021 ieee.

关键词： Convolutional neural networks

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Accurate Homing control of Parafoil Delivery System Based on Active Disturbance Rejection control 10

Accurate Homing Control of Parafoil Delivery System Based on...

引用

10th ieee data driven control and learning systems conference, ddcls 2021

作者： Sun, Hao Sun, Qinglin Tao, Jin Tan, Panlong Chen, Zengqiang College of Artificial Intelligence Nankai University Tianjin300350 China

ISBN: (纸本)9781665424233

In order to realize the accurate homing control of the parafoil delivery system, in this paper, firstly an eight-degree of freedom model of the parafoil delivery system is built. then, an active disturbance rejection controller is designed for the accurate trajectory tracking. the control system, including the embedded controller, ground station, steering engine and power plant, is built for the flight test. the detailed simulation results and the flight experiments prove the correctness of the proposed controller. the tracking and landing errors are all less than 20m. © 2021 ieee.

关键词： controllers

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Improving the Generalization Performance of data-driven Predictive Model for Dynamic Process systems 10

Improving the Generalization Performance of Data-driven Pred...

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10th ieee data driven control and learning systems conference, ddcls 2021

作者： Shao, Weiming Li, Yougao Zhao, Dongya Ge, Zhiqiang Department of Chemical Equipment and Control Engineering Qingdao266580 China College of Control Science and Engineering Zhejiang University Hangzhou310007 China

ISBN: (纸本)9781665424233

data-driven dynamic predictive models have been extensively applied in dynamic process systems for online predicting difficult-to-measure key variables. However, due to the limitations of industrial data in quantity, quality and representativity, the generalization performance of the developed dynamic models are not reliable. In other words, based on certain unique historical dataset, high-accuracy of the predictive model for online unseen data cannot be guaranteed. To deal with this issue, this paper proposes a switching model-based dynamic modeling framework for improving the generalization accuracy and reliability of data-driven predictive models, which is composed of two stages, i.e., the offline model library construction and online model switch. the performance of the proposed scheme is assessed by a real-life industrial dynamic process using switching dynamic partial least squares models, and its application potentials are verified through comparisons among the state-of-the-art methods. © 2021 ieee.

关键词： Least squares approximations

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