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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81-90 订阅

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Consensus Tracking of Second-Order Autonomous Unmanned systems with Nonlinear Dynamics Using Distributed Adaptive control 10

Consensus Tracking of Second-Order Autonomous Unmanned Syste...

引用

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

作者： Li, Yanzhou Wu, Yuanqing He, Shenghuang Lu, Yongkang Zhong, Wenjian Guangdong University of Technology Guanzhou510006 China

ISBN: (纸本)9781665424233

the consensus tracking of second-order autonomous unmanned systems with nonlinear dynamics by using distributed adaptive control is studied in this paper. First, the consensus tracking problem is converted to the stability problem of error dynamic system. Second, the obtained error dynamic system is proved to be asymptotically stable by constructing suitable Lyapunov function. third, the distributed consensus tracking controller and adaptive control law acting on the network topology are designed to achieve the consensus tracking of second-order autonomous unmanned systems. the theoretical results are verified by some simulations. © 2021 ieee.

关键词： Lyapunov functions

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Calculation Method of Available Transfer Capacity Based on Graph Convolutional Network 10

Calculation Method of Available Transfer Capacity Based on G...

引用

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

作者： Cai, Zhi Li, Yuxuan Dai, Sai Men, Deyue Beijing Key Laboratory of Research and System Evaluation of Power Dispatching Automation Technology China Electric Power Research Institute Beijing100192 China

ISBN: (纸本)9781665424233

Studying and evaluating the available transfer capacity (ATC) of transmission lines is of great significance when determining the reasonable operation mode of a power grid and optimizing the allocation of power grid resources. With the rapid development of deep learning technology, it is possible to quickly find the most ATC by mining historical data and prior knowledge without relying on physical model. therefore, an ATC calculation method based on graph convolutional network (GCN) is proposed. the topology information between the nodes of a power grid is represented by adjacency matrix. the proposed algorithm can effectively mine the correlation between the nodal injection power, and map the complex non-linear relationship between ATC and power data with deep graph convolutional architecture. the simulation results show that the calculation accuracy of ATC based on GCN can meet the needs of practical application of power grid. © 2021 ieee.

关键词： Deep learning

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Intelligent Built-in Test Design of controller Module by Improved Biologically Inspired Neural Network 10

Intelligent Built-in Test Design of Controller Module by Imp...

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

作者： Xie, Zhenmeng Hou, Guolian Zhang, Jianhua Huang, Congzhi School of Control and Computer Engineering North China Electric Power University Beijing102206 China Key Laboratory of Power Station Energy Transfer Conversion and System North China Electric Power University Beijing China

ISBN: (纸本)9781665424233

Built-in test (BIT) technology is widely employed in heavy-duty gas turbine control systems for fault recognition. However, it is difficult to obtain an excellent fault diagnostic ability by using the conventional BIT technology, and the false alarm rate is high. In this paper, a design of intelligent BIT based on improved biologically inspired neural network (BINN) is proposed to reduce false alarm. Firstly, massive historical measurement data of controller module is collected and used as training dataset and test dataset. Secondly, intelligent BIT based on improved BINN is designed to deal with the issue of module state identification and reduce false alarm rate. Finally, the effectiveness of proposed approach is validated by the given extensive numerical simulation results and experimental results. © 2021 ieee.

关键词： Statistical tests

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Real-time Reachable Set Estimation for Markov Jump systems with Time-varying Delay 10

Real-time Reachable Set Estimation for Markov Jump Systems w...

引用

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

作者： Zhang, Liang Cao, Yuning Feng, Zhiguang College of Intelligent Systems Science and Engineering Harbin Engineering University Harbin150001 China College of Control Science and Engineering Bohai University Jinzhou121013 China

ISBN: (纸本)9781665424233

the problem of real-time reachable set estimation (RSE) for a class of Markov jump systems is studied in this paper. A method is proposed for estimating real-time reachable set of the Markov jump systems with known inputs, bounded disturbances and non-zero initial conditions. By utilizing the linear matrix inequalities (LMIs) and the Lyapunov stability theory, sufficient conditions for estimating the real-time reachable set of Markov jump systems are given, and the reachable set of the resulting systems is mean-square (M-S) bounded by the prescribed ellipsoid. the validity of the theoretical analysis is verified by numerical simulation. © 2021 ieee.

关键词： Linear matrix inequalities

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Attitude Tracking control of 3-DOF Helicopter Based on Command Filter and Neural Network Techniques 10

Attitude Tracking Control of 3-DOF Helicopter Based on Comma...

引用

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

作者： Liu, Liang Su, Yibo Lu, Jiaming Institute of Automation Qufu Normal University Qufu273165 China College of Control Science and Engineering Bohai University Jinzhou121013 China

ISBN: (纸本)9781665424233

this paper proposes the attitude tracking controller design scheme for the 3-DOF helicopter with unknown model uncertainties and external disturbances by combining command filter with neural network techniques. On the basis of backstepping design method, the unknown model uncertainties and external disturbances are firstly reconstructed by adopting command filter. then, neural network technique is used to approximate the reconstructed model uncertainties and external disturbances to realize the attitude tracking control of the 3-DOF helicopter. Finally, the stability of the closed-loop tracking control system of the 3-DOF helicopter is verified by Lyapunov stability theory. Simulation results verify the effectiveness of the proposed method. © 2021 ieee.

关键词： controllers

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A New Integrated Health Management for Quadrotors Based on Deep learning 10

A New Integrated Health Management for Quadrotors Based on D...

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

作者： Kong, Weiyi Bian, Shaobo Li, Xiaoyan Wang, Chunyan Wang, Jianan School of Mechatronical Engineering Beijing Institute of Techonology Beijing100081 China School of Areospace Engineering Beijing Institute of Technology Beijing100081 China

ISBN: (纸本)9781665424233

In this paper, the fault diagnosis and health monitoring techniques based on machine learning are investigated before the research. then, the dynamic model of the quadrotor is analyzed and the most important relationship between the IMU and motor outputs are given. Afterwards, the high-coupling and nonlinear link between units are mapped into an implicit network APN based on LSTM neural network. Predictions and train are made based on the data set collected from the practical and HITL flight log. Finally, the feasibility of the health management is verified during the fault simulation and the a possible solution is given against the common error. © 2021 ieee.

关键词： Failure analysis

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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...

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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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A Gradient Neural Network for online Solving the Time-varying Inverse Kinematics Problem of Four-wheel Mobile Robotic Arm 10

A Gradient Neural Network for online Solving the Time-varyin...

引用

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

作者： Zhou, Yanpeng Liu, Keping Li, Chunxu Wang, Gang Liu, Yongbai Sun, Zhongbo Changchun University of Technology Department of Control Engineering Changchun130012 China University of Plymouth Centre for Robotics and Neural Systems PlymouthPL48AA United Kingdom

ISBN: (纸本)9781665424233

In this paper, a gradient neural network (GNN) is presented, analyzed and discussed to solve the time-varying inverse kinematics solution of the four-wheel mobile robotic arm, which can approximate the time varying inverse kinematics solution. A monolithic kinematics model of mobile robotic arm is established, and the inverse kinematics solution can synchronously coordinate the control of the mobile platform and the robotic arm to accomplish the task of the end-executor. Besides, the computer numerical results are provided to attest validity and high exactitude of GNN model in settling the time-varying inverse kinematics of a four-wheel mobile robotic arm. © 2021 ieee.

关键词： Robotic arms

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Unsupervised Feature learning with data Augmentation for control Valve Stiction Detection 10

Unsupervised Feature Learning with Data Augmentation for Con...

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

作者： Zhang, Kexin Liu, Yong Institute of Intelligent Systems and Control Zhejiang University Hangzhou310027 China

ISBN: (纸本)9781665424233

this paper proposes an unsupervised feature learning approach on industrial time series data for detection of valve stiction. Considering the commonly existed characteristics of industrial time series signals and the condition that sometimes massive reliable labeled-data are not available, a new time series data transformation and augmentation method is developed. the transformation stage converts the raw time series signals to 2-D matrices and the augmentation stage increases the diversity of the matrices by performing transformation on different timescales. then a convolutional autoencoder is used to extract the representative features on the augmented data, these new features are taken as the inputs of the traditional clustering algorithms. Unlike the traditional approaches using hand-crafted features or requiring labeled-data, the proposed strategy can automatically learn features on the time series data collected from industrial control loops without supervision. the effectiveness of the proposed approach is evaluated through the International Stiction data Base (ISDB). Compared with the traditional machine learning methods and deep learning based methods, the experimental results demonstrate that the proposed strategy outperforms the other methods. Besides performance evaluation, we provide a visualization process of feature learning via principal component analysis. © 2021 ieee.

关键词： Matrix algebra

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