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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191-200 订阅

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Modeling of Aggregation Process Based on Feature Selection Extreme learning Machine of Atomic Search Algorithm 10

Modeling of Aggregation Process Based on Feature Selection E...

引用

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

作者： Wu, Chao Ren, Lihong Hao, Kuangrong College of Information Science and Technology Donghua University Shanghai201620 China

ISBN: (纸本)9781665424233

Polymerization process is a process in the production of polyester fiber, and its reaction parameter intrinsic viscosity has an important influence on the properties of the final polyester fiber. In this paper, a feature selection extreme learning machine model based on binary encoding Atom Search Optimization algorithm is proposed and applied to the polymerization process of polyester fiber production. Firstly, the distance measure of K-NearestNeighbor algorithm, combined with binary coding, and Atom Search Optimization algorithm are used to select features of industrial data to obtain the optimal data set. According to the data set, atom search optimization algorithm is used to optimize the weight and threshold of extreme learning machine and the activation function of the improved extreme learning machine. A prediction model with root mean square error as fitness function was established and applied to polyester production process. the simulation results show that the model has good prediction accuracy, which can be used for reference in the follow-up industrial production. © 2021 ieee.

关键词： Atoms

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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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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...

引用

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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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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Cluster-based Generative Adversarial Network Imbalanced data Generation Method 10

Cluster-based Generative Adversarial Network Imbalanced Data...

引用

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

作者： Fan, Min Yang, Qing Zhang, Bo Zhou, Meng Zhang, Ke Xia, Jialu School of Automation Chongqing University Chongqing400044 China China Electric Power Research Institute Beijing100192 China

ISBN: (纸本)9781665424233

data imbalance is widespread in data mining. However, the imbalance of data makes the minority recognition rate low but the cost of misclassification is high. In order to improve the minority recognition rate, this paper proposed a data generation method for balance data set. First, the minority data is clustered and divided into sub-categories, next it built a GAN model to generate each sub-category data separately to improve the quality of data generation and balance the number of majority and minority categories, and also alleviate the imbalance within the minority category. then, this paper built a cost-sensitive convolutional neural network model for classification. Finally, experiments on the UCI public data set show that the method has a good classification performance. © 2021 ieee.

关键词： Generative adversarial networks

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control of Robotic Teleoperation System with Time Delay based on Force Estimation 11

Control of Robotic Teleoperation System with Time Delay base...

引用

11th ieee data driven control and learning systems conference, ddcls 2022

作者： Sheng, Hao Liu, Xia Chen, Shini Jiang, Wenbo Guo, Yi School of Electrical Engineering and Electronic Information Xihua University Chengdu610039 China

ISBN: (数字)9781665496759

ISBN: (纸本)9781665496759

For nonlinear teleoperation system with time delay, a control scheme based on force estimation is proposed. Since the force signals in teleoperation system are difficult to measure, a force estimation algorithm is designed to estimate the force signals. Using the estimated force signals and time delayed position error signals, two backstepping controllers are designed for teleoperation system. the performance of position tracking and force reflection of the system is analyzed, and the effectiveness of the proposed method is verified by simulations. the results show that the proposed control method can accurately estimate the force signals, and improve the performance of position tracking and force reflection while maintaining the stabilization of the robotic system. © 2022 ieee.

关键词： Time delay

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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...

引用

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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Generalized-reduced-order-extended-state-observer-based Adaptive Repetitive control for Brushless DC Motor 10

Generalized-reduced-order-extended-state-observer-based Adap...

引用

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

作者： Gao, Dongxu Zhou, Lan Pan, Changzhong Liao, Changchao School of Information and Electrical Engineering Hunan University of Science and Technology Xiangtan411201 China

ISBN: (纸本)9781665424233

this paper presents a method of designing a generalized-reduced-order-extended-state-observer-based (GROESO-based) adaptive repetitive control system for a brushless DC motor with time-varying parameter uncertainty and unknown nonlinear dynamics. In this system, GROESO-based control is used to estimate and compensate the mismatched nonlinear dynamics, model-reference adaptive control (MRAC) is used to suppress the matched parameter uncertainty, and repetitive controller undertakes the task of tracking periodic reference input signal. By appropriately choosing a disturbance compensation gain for GROESO-based control and adaptive parameters for MRAC, the influences of the nonlinear dynamics and parameter uncertainty are effectively attenuated. A feedforward compensator for repetitive control is also designed. Simulation results show that the designed brushless DC motor servo system achieves both satisfactory disturbance-rejection and tracking performances. © 2021 ieee.

关键词： Model reference adaptive control

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A 2D UAV Path Planning Method Based on Reinforcement learning in the Presence of Dense Obstacles and Kinematic Constraints 11

A 2D UAV Path Planning Method Based on Reinforcement Learnin...

引用

11th ieee data driven control and learning systems conference, ddcls 2022

作者： Tang, Xinming Chai, Yi Liu, Qie School of Automation Chongqing University Chongqing400044 China

ISBN: (数字)9781665496759

ISBN: (纸本)9781665496759

the complex kinematic constraints and dense obstacles are always the huge challenge in the UAV path planning. To effectively deal with dense obstacles and kinematic constraints, a novel two-level optimization algorithm for unmanned aerial vehicles (UAVs) in 2D maps, called as Spherical Expansion-Proximal Policy Optimization (SE-PPO), is proposed in this paper. this method is a combination of SE and PPO algorithms. In the first level, SE algorithm is used to generate the initial path, and sub-goals are selected from this path in the first level. these sub-goals are optimized by the local path optimizer based on PPO algorithm to obtain the final path. the effectiveness of this method to deal with the kinematic constraints and dense obstacles is demonstrated by the results of the simulation experiments. © 2022 ieee.

关键词： Reinforcement learning

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