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检索条件"任意字段=9th IEEE Data Driven Control and Learning Systems Conference, DDCLS 2020"

共 506 条记录，以下是441-450 订阅

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A Deep learning Method for Rolling Bearing Fault Diagnosis through Heterogeneous data

A Deep Learning Method for Rolling Bearing Fault Diagnosis t...

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data driven control and learning systems (ddcls)

作者： Wei Zhou Yandong Hou Institute of Computer and Information Engineering Henan University Kaifeng China

ISBN: (数字)9781728159225

ISBN: (纸本)9781728159232

Vibration signals of rolling bearing have multiple heterogeneous forms. Traditional fault diagnosis methods use 1D time-series signals or converted 2D signals for fault diagnosis. However, using the former will lose the spatial neighborhood features; using the latter will ignore time-series features, which caused information waste. In this paper, a new heterogeneous form of bearing vibration signals is proposed to address the problem. Our contributions of include: First, we proposed dynamic waveform sequences, which is a new heterogeneous form and can simultaneously reflect time-series features and spatial neighborhood features in vibration signals. Second, the CCLSTM (Conv-ConvLSTM) model is designed to extract the above two features layer by layer. Relying on the powerful feature extraction capability of CCLSTM, it is possible to simultaneously extract the time-series features and spatial neighborhood features in a single fault diagnosis network. the experimental verification through real bearing fault data sets shows that this method can effectively improve the diagnostic accuracy.

关键词： Feature extraction Fault diagnosis Vibrations Convolution Logic gates Two dimensional displays Rolling bearings

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Parameter Optimization of Load Frequency Active Disturbance Rejection control Based on Improved Particle Swarm Optimization

Parameter Optimization of Load Frequency Active Disturbance ...

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data driven control and learning systems (ddcls)

作者： Zhaoyang Huang Zengqiang Chen Mingwei Sun Qinglin Sun College of Artificial Intelligence Nankai University Tianjin China Key Laboratory of Intelligent Robotics of Tianjin Tianjin China

ISBN: (数字)9781728159225

ISBN: (纸本)9781728159232

In order to improve the control accuracy and response speed of the load frequency control(LFC) method, this paper studies the nonlinear active disturbance rejection control(ADRC) method of the LFC system, and adds the improved particle swarm optimization(PSO) to solve the problem that the ADRC has many parameters which make it difficult to be set. A design method of load frequency active disturbance rejection controller based on PSO is proposed. Firstly, based on the model of interconnected power system, the system state space and output equation are established. then the basic principles of the ADRC and PSO are introduced. After that the design flow of the ADRC based on PSO is given and incorporated into the LFC method. Finally, the method is applied to the two-area interconnected power system considering nonlinear governor's dead zone(GDZ) and generation rate constraint(GRC) factors under load disturbance and parameter perturbation. In different cases, the comparison with other algorithms shows a good control effect, which confirms the effectiveness and superiority of the load frequency active disturbance rejection control parameter optimization method based on improved PSO.

关键词： Frequency control Mathematical model Load modeling Turbines Power system stability Particle swarm optimization control systems

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Trajectory Tracking of a Quadrotor UAV based on High-Order Differential Feedback control

Trajectory Tracking of a Quadrotor UAV based on High-Order D...

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data driven control and learning systems (ddcls)

作者： Xia Li Guoyuan Qi Xitong Guo Shengli Ma School of Mechanical Engineering Tiangong University China Tianjin Key Laboratory of Advanced Technology of Electrical Engineering and Energy Tiangong University China School of Electrical Engineering and Automation Tiangong University China

ISBN: (数字)9781728159225

ISBN: (纸本)9781728159232

A high-order differential feedback control (HODFC), which is a model free control strategy, is applied to the trajectory tracking of a quadrotor unmanned aerial vehicle (UAV) with unknown uncertainties in this paper. Two virtual control variables are introduced to decouple the quadrotor flight system. the third-order HOD (high-order differentiator) is proposed and used to estimate the differentials of reference input and the states of system. the second-order HODFC is designed to ensure the closed system has desired poles. A controller filter is used to compensate the unknown function and disturbance in the quadrotor system. A comparison between the HODFC and active disturbance rejection control (ADRC) is conducted. the simulation results illustrate that the HODFC method can ensure that the quadrotor output track the desired trajectory asymptotically with a smaller tracking error than the linear ADRC, even with unknown function and disturbance.

关键词： Rotors Mathematical model Uncertainty Unmanned aerial vehicles Vehicle dynamics Trajectory tracking Torque

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Path-Following control for Unmanned Rollers: A Composite Disturbance Rejection-based Framework

Path-Following Control for Unmanned Rollers: A Composite Dis...

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data driven control and learning systems (ddcls)

作者： Kang Song Hui Xie Stake Key Laboratory of Engines Tianjin University Tianjin

ISBN: (数字)9781728159225

ISBN: (纸本)9781728159232

the drum roller, as a widely used engineering vehicle, has higher degree of freedom in motion relative to conventional passenger vehicles. the special operating condition that has large rocks on road for compaction introduces severe disturbances in path-following. In this paper, a composite disturbance rejection-based framework, for the path-following control of rollers, is proposed. the external disturbances caused by rocks on road are rejected by correcting the coordinates of rollers from Global Position System (GPS) using measured attitude information. the nonlinearities from the complex articulation structure are compensated using a kinematic model-based feedforward control. All other uncertainties, internal and external, are lumped as an augmented state - "total disturbance", estimated hence rejected in real-time via the extended state observer (ESO). As compliment to ESO with the limited performance due to low sampling rate of GPS, a model parameters self-learning algorithm is added. the proposed solution is validated both in simulation and experiments, showing satisfactory performance. the maximum lateral error is ~0.1m for unmanned rollers, out-performing the average level of human driven rollers when working on road with maximum diameter of rocks up to 1m.

关键词： Global Positioning System Roads Antenna measurements Uncertainty Steering systems Coordinate measuring machines Rocks

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the Application of Adaptive A-star Algorithm in Layout of Spatial Pipeline

The Application of Adaptive A-star Algorithm in Layout of Sp...

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data driven control and learning systems (ddcls)

作者： Xinyu Zhao Na Qin Jie Huang Yongchen Miao the Institute of Systems Science and Technology Southwest Jiaotong University Chengdu China Science and Technology on Reactor System Design Technology Laboratory Nuclear Power Institute of China Chengdu China Harbin Shipping School Harbin China

ISBN: (数字)9781728159225

ISBN: (纸本)9781728159232

In order to settle the complex problem of layout of spatial pipeline, the diameter of pipeline is taken as the distance between adjacent scattered points, and the spatial layout including obstacles is approximated as a three-dimensional diagram of scattered points. A-star algorithm is used to search an optimal pipelines path from the three-dimensional diagram of scattered points. However, when traditional A-star algorithm addresses pre-selected points that are the same cost, it selects pre-selected points randomly. In view of the disadvantages, an adaptive A-star algorithm is proposed based on the traditional A-star algorithm, in this paper. the adaptive A-star algorithm selects more reasonable pre-selected point according to the surrounding environment and situation of pre-selected point , so as to achieve the requirements of the shortest pipeline and the fewest inflection point. Finally, the experimental simulation shows that the adaptive A-star algorithm has better performance than the traditional A-star algorithm.

关键词： Pipelines Path planning Layout Robots Heuristic algorithms Robot kinematics Optimization

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Classification Method of Indoor Environmental data based on Internet of things

Classification Method of Indoor Environmental Data based on ...

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data driven control and learning systems (ddcls)

作者： JiaWei Fan Haijiang Zhu Xuejing Wang College of Information & Technology Beijing University of Chemical Technology Beijing China Center for Information Technology Beijing University of Chemical Technology Beijing CO China

ISBN: (数字)9781728159225

ISBN: (纸本)9781728159232

Aiming at the multi-source heterogeneous characteristics of data collected by Internet of things wireless sensors, we investigate an IoT data classification method based on Improved Multi-Kernel learning Support Vector Machine (IMKL-SVM). Kernel function types and parameters are selected mainly according to rule of thumb in traditional multi-kernel learning method. Our improved method has two steps in determining types and parameters of the kernel function. First, the primarily types and parameters of the kernel function is determined by a cross-validation method. And then the types and parameters of the multi-kernel function is optimized by SVM. the experiment designed two sets of data including four sensor data: temperature, humidity, light and atmospheric pressure. One group data was labeled as four types: morning, mid-afternoon, evening and night. And another group data was labeled as three types: day, evening and night. the classification accuracy are presented using the improved method, single-kernel SVM and traditional MKL-SVM for the two group data. Experiment results indicate that the IMKL-SVM method achieves higher classification accuracy for IoT data with multi-source heterogeneous characteristics.

关键词： Kernel Support vector machines Internet of things Sensors learning systems Classification algorithms data mining

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Online Optimal Event-triggered Tracking control with Actuator Saturation via ADP

Online Optimal Event-triggered Tracking Control with Actuato...

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data driven control and learning systems (ddcls)

作者： Lu Liu Ruizhuo Song Yi Ren Yong Zhao Biao Song School of Automation and Electrical Engineering University of Science and Technology Beijing Beijing P. R. China China communications construction intelligence transportation company limited Tianjin P. R. China Beijing Institute of Computer Application University of Science and Technology Beijing Beijing P. R. China

ISBN: (数字)9781728159225

ISBN: (纸本)9781728159232

this paper comes up with an optimal tracking control mechanism with saturation controller for some continuous nonlinear systems based on the event-triggered (ET) mechanism via adaptive dynamic programming (ADP). To begin with, the constrained tracking control problems are able to be transformed into common event-triggered optimal control through designing an appropriate cost function. Next, a single critic network is to estimate the cost function. Moreover, taking the appropriate triggering condition as the prerequisite, the stability proof of the system is proved according to Lyapunov theory. In addition, the simulations verify the excellent performance of ET-ADP algorithm because the optimal control law is only updated at the required times influenced by the devised triggering condition, which can accomplish high efficiency of resources and energy consumption of the system.

关键词： Optimal control Dynamic programming Cost function Nonlinear systems Mathematical model Heuristic algorithms Feeds

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data-driven Reconstruction for Massive Buildings within Urban Scenarios: A Case Study

Data-driven Reconstruction for Massive Buildings within Urba...

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data driven control and learning systems (ddcls)

作者： Ziye Chen Xiaojia Xiang Han Zhou Tianjiang Hu College of Intelligence Science and Technology National University of Defense Technology Changsha China Sun Yat-sen University Guangzhou China

ISBN: (数字)9781728159225

ISBN: (纸本)9781728159232

the reconstruction and visualization of urban environments is widely used in immersive simulation, but the massive data of buildings bring huge burden to the modelling and rendering process. this paper proposes an improved fast approach to build and visualize the scene graph based on the level of details (LOD). Firstly, an automatic reconstruction scheme of urban buildings is designed by using the public vector data set. Secondly, a tree-type organization and management structure is constructed for urban scenarios inspired by the idea of recursive division. thirdly, a dynamic data scheduling strategy is developed with concerns of self-organizing the generated 3D massive model data. the developed approach is called Detail Segmentation of Scene Tree Method, namely DSSTM. Finally, experiments are conducted for performance comparison between the proposed DSSTM and a conventional method, while both are realized by using the open source library, OpenSceneGraph, http://***/. the experimental results of reconstructing Changsha City have verified that the proposed method is effective and efficient for practical applications of visualization on large-scale urban with massive buildings. the rendering speed is increased to 4~7 times of the conventional method, the dynamic loading time is shortened by 90%.

关键词： Buildings Floors Urban areas three-dimensional displays data models Rendering (computer graphics) Loading

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Feature Extraction of Rolling Bearing Faults Based on VMD and FRFT

Feature Extraction of Rolling Bearing Faults Based on VMD an...

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data driven control and learning systems (ddcls)

作者： Lei Jiao Jie Ma School of Automation Beijing University of Information Science and Technology Beijing School of Mechanical and Electrical Engineering Beijing Information Science and Technology University Beijing

ISBN: (数字)9781728159225

ISBN: (纸本)9781728159232

the fault signal of rolling bearing is non-stationary nonlinear signal, and it is difficult to extract the feature of weak fault under strong background noise. this paper uses a new filtering method-Fractional Fourier Transform (FRFT). Compared with the traditional Fourier transform (FFT), it can make the time-frequency characteristics of unstable fault signals better displayed and suppress cross-interference. In this paper, the method of feature extraction of rolling bearings combined with Variational Mode Decomposition (VMD) and Fractional Fourier Transform (FRFT) is used. First, the original vibration signal is decomposed by VMD to obtain several intrinsic mode component functions (IMF). the component with the largest correlation coefficient is selected as the optimal component for filtering in the fractional order domain. the 1.5-dimensional envelope spectrum of the filtered signal is analyzed , the frequency value corresponding to the maximum amplitude can be obtained. this frequency value is the fault characteristic frequency of the rolling bearing. the simulation results show that the method can effectively extract the fault characteristic information of the rolling bearing.

关键词： Fourier transforms Rolling bearings Process control Interference Feature extraction Information filters data mining

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Power-sum Activated Neural Dynamics for Lower Limb Motion Intention Recognition

Power-sum Activated Neural Dynamics for Lower Limb Motion In...

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data driven control and learning systems (ddcls)

作者： Jiachang Li Yimeng Qi Mei Liu Shuai Li Zhongbo Sun Long Jin School of Information Science and Engineering Lanzhou University Lanzhou China Changchun University of Technology Changchun China

ISBN: (数字)9781728159225

ISBN: (纸本)9781728159232

It can be found that the exact solution of the equations has quite similarity to the pursuing of the minimum error in the field of control. therefore, many control problems can be explained from the perspective of solving equations. As a powerful computing tool, neural dynamics model has been widely applied in addressing time-varying equations and optimization problems because of its remarkable convergence and robustness. For neural dynamics model, the selection of activation function plays a significant role in its performance. In this paper, a special nonlinearly activated (i.e. power-sum activation function) neural dynamics model is exploited to the experimental simulation of intention recognition of lower limb motion. Compared with the linear activation function, the corresponding analyses of convergence and robustness are given respectively, demonstrating the superior characteristics of the neural dynamics model with power-sum activation function.

关键词： Computational modeling Dynamics Tools Robustness data models Mathematical model Convergence

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