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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411-420 订阅

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Ride Quality Improvement for High-Speed Railway Based on D-Type Iteration learning control

Ride Quality Improvement for High-Speed Railway Based on D-T...

引用

data driven control and learning systems (ddcls)

作者： Lu-Ping Yang Qiao Zhu Jun Ni Southwest Jiaotong University Chengdu People’s Republic of China

ISBN: (数字)9781728159225

ISBN: (纸本)9781728159232

though differential-type (D-type) control method and iteration learning control (ILC) are both classic control strategy, ILC is rarely used for lateral control of trains. In this paper, a D-type ILC is proposed to suppress the lateral vibration of train body and improve ride quality. First, the dynamic model of lateral and yaw motions for train body is introduced, where external disturbances caused by bogies are considered. Based on the repeatability of train operation, a iteration learning controller in combination with D-type control method is employed to better suppress the lateral motions of train body. Unlike traditional PID controller, the D-type iterative learning controller can make good use of the train's repeatability to adjust the input force to the actuator. then, the convergence condition is analyzed to ensure the rationality of the proposed algorithm. In order to verify the effectiveness of proposed controller, the co-simulation of a full-scale train model with German high interference track irregularities in SIMPACK and the control flow in SIMULINK is established to evaluate the effectiveness by comparing with a open-loop system.

关键词： Automobiles Vibrations Mathematical model Trajectory Rail transportation Force Damping

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Observer-Based Adaptive Multi-dimensional Taylor Network control for Nonlinear systems with Time-Delay

Observer-Based Adaptive Multi-dimensional Taylor Network Con...

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data driven control and learning systems (ddcls)

作者： Lei Chu Shuhua Zhang Mingxin Wang Shanliang Zhu Yuqun Han School of Mathematics and Physics Qingdao University of Science and Technology Qingdao P. R. China The Research Institute for Mathematics and Interdisciplinary Sciences Qingdao University of Science and Technology Qingdao China College of Electromechanical Engineering Qingdao University of Science and Technology Qingdao P. R. China Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of Education Nanjing P. R. China

ISBN: (数字)9781728159225

ISBN: (纸本)9781728159232

In this paper, an observer-based adaptive Multi-dimensional Taylor network (MTN) controller is proposed for strictly feedback nonlinear systems with time-delay and unmeasurable states. MTNs are utilized to approximate the unknown and desired control input signals directly instead of the unknown nonlinear functions. Moreover, a linear state observer is designed for estimating the unmeasured states. Based on the backstepping technique, a novel adaptive MTN control strategy with simple structure and good real time property is proposed. the designed controller can guarantee all the signals of the closed-loop system are bounded and the tracking error converges to a small neighborhood of the origin. Simulation results are given to demonstrate the effectiveness of the proposed method.

关键词： Nonlinear systems Backstepping Observers Closed loop systems Artificial neural networks Adaptive control Stability analysis

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Nonsingular Terminal Sliding Mode control for PMLSM Based on Disturbance Observer

Nonsingular Terminal Sliding Mode Control for PMLSM Based on...

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data driven control and learning systems (ddcls)

作者： Bo Ding Dezhi Xu Weilin Yang Kaitao Bi Wenxu Yan School of Internet of Things Engineering Jiangnan University Wuxi Jiangsu P. R. China

ISBN: (数字)9781728159225

ISBN: (纸本)9781728159232

In order to achieve high precision speed control of the permanent magnet linear synchronous motor (PMLSM), this paper proposed a nonsingular terminal sliding mode control (NTSMC) with nonlinear disturbance observer (NDO). First, the model of PMLSM based on field oriented control (FOC) is given. then, a NTSMC is proposed via backstepping. the controller can make the states reach the manifold in finite time. With the motor moving, it will be disturbed by friction and load, which will affect the system stability. therefor, a NDO is introduced to estimate the unknown disturbance. the observed value is dynamically compensated to the NTSMC, which improves the control precision of the PMLSM system. Finally, the proposed algorithm is applied in dSPACE compared with PI, the experimental results show the proposed controller has better dynamic and static performance.

关键词： Synchronous motors Stators Permanent magnets Disturbance observers Heuristic algorithms Sliding mode control Friction

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Generative Independent Component thermography for Improved Defect Detection of Carbon Fiber Composites

Generative Independent Component Thermography for Improved D...

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data driven control and learning systems (ddcls)

作者： Kaixin Liu Meili Chen Zhiwen Wang Yuan Yao Jianguo Yang Yi Liu Institute of Process Equipment and Control Engineering Zhejiang University of Technology Hangzhou China Hangzhou Arcvideo Technology Co. Ltd. Hangzhou China College of Materials and Chemical Engineering Liming Vocational University Quanzhou China National Tsing Hua University Hsinchu Taiwan

ISBN: (数字)9781728159225

ISBN: (纸本)9781728159232

As one of the popular techniques for non-destructive evaluation, infrared thermography often requires the assistance of data analysis models to help defect detection and identification. A novel generative independent component thermography (GICT) framework for defect detection in polymer composites is proposed. It utilizes a deep convolutional generative adversarial network to generate more informative images, which enhances the diversity of thermography data. the defect detection performance of sequential ICT-based thermographic data analysis can be enhanced. the feasibility of GICT is illustrated with its application to the defect detection of a carbon fiber reinforced polymer specimen.

关键词： Gallium nitride data models Optical fiber networks data analysis Generative adversarial networks Convolution Polymers

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Intelligent Measurement Modeling Using a Novel Multi-nonlinear Mapping Based Extreme learning Machine Integrated with Partial Least Square Regression

Intelligent Measurement Modeling Using a Novel Multi-nonline...

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data driven control and learning systems (ddcls)

作者： Qunxiong Zhu Xiaohan Zhang Yuan Xu Yanlin He College of Information Science & Technology Beijing University of Chemical Technology Beijing China Engineering Research Center of Intelligent PSE Ministry of Education of China Beijing China

ISBN: (数字)9781728159225

ISBN: (纸本)9781728159232

Accurate intelligent measurement modeling plays a key role in complex process industries. However, establishing an accurate and robust measurement model tends to be more and more difficult because of the increasing complexity in terms of nonlinearity and collinearity of data. To solve this problem, a novel multi-nonlinear mapping based extreme learning machine integrated with partial least square regression is proposed in this paper. In the proposed model, two problems of nonlinearity and collinearity are effectively dealt with by using multi-nonlinear mapping and partial least square regression, respectively. For evaluating performance, empirical studies on a commonly used bench mark problem and a real-world application confirm that the presented method can obtain high accuracy and high stability performance for intelligent measurement.

关键词： Brain modeling Mathematical model Load modeling data models Analytical models Predictive models Testing

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data-driven Tie-line Scheduling Method

Data-Driven Tie-line Scheduling Method

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data driven control and learning systems (ddcls)

作者： Zhi Cai Sai Dai Yuhan Dai Guofang Zhang Yi Lu Beijing Key Laboratory of Research and System Evaluation of Power Dispatching Automation Technology China Electric Power Research Institute Beijing State Grid Sichuan Electric Power Supply Company Chengdu China

ISBN: (数字)9781728159225

ISBN: (纸本)9781728159232

Tie-line scheduling in an interconnected power grid is difficult to perform optimization by establishing a unified physical model due to the different scheduling methods in different dispatching centers. It may cause repeated coordination and iteration in multi-level dispatching centers. therefore, a data-driven tie-line scheduling method with self-learning ability is proposed. First, perform cluster pre-processing of historical dispatching data with K-means algorithm; secondly, establish a deep learning model of tie-line scheduling based on long short-term memory (LSTM), and build a mapping model among system load, clean energy output and tie-line schedule through historical data training; After that, the all above process is used as a foundation to do the tie-line scheduling; finally, continuously revise the model by accumulating historical data, so that it has the ability of self-evolution and self-learning. We make a case analysis based on the actual power grid data, and the calculation results show the effectiveness of the proposed method.

关键词： data models Load modeling Dispatching Power grids Deep learning Training Scheduling

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Root Cause Diagnosis Framework Based on Granger Causality with the Combination of Normal and Fault data

Root Cause Diagnosis Framework Based on Granger Causality wi...

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data driven control and learning systems (ddcls)

作者： Xiang-Yun Ye Jian-Guo Wang Fei Wang Yuan Yao Jun-Jiang Liu School of Mechatronical Engineering and Automation Shanghai University Shanghai Key Lab of Power Station Automation Technology Shanghai China National Tsing-Hua University Hsin-Chu Taiwan Tube Pipe and Bar Business Unit Baoshan Iron and Steel Co Ltd Shanghai China

ISBN: (数字)9781728159225

ISBN: (纸本)9781728159232

Granger causality analysis is one of the most widely used methods in root cause diagnosis. this method can get effective results in many cases, but there are still some problems and underutilization of data is one of them. Granger causality analysis only used the fault relate data segment. this paper proposes a novel root cause diagnosis framework based on Granger causality analysis, and attempts to combine the normal and fault data to make the result more accurate. the main ideal is to test the change of causality intensity before and after the fault to optimize the result of the fault propagation paths. Tennessee Eastman(TE) process data and TE data was used to verify the effectiveness of the method.

关键词： Predictive models Feedback loop Time series analysis data models Reactive power Interference Fault diagnosis

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An Edge-Cloud Synergy Integrated Security Decision-Making Method for Industrial Cyber-Physical systems

An Edge-Cloud Synergy Integrated Security Decision-Making Me...

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data driven control and learning systems (ddcls)

作者： Hang Xing Chunjie Zhou Xinhao Ye Meipan Zhu School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan China

ISBN: (数字)9781728159225

ISBN: (纸本)9781728159232

With the introduction of new technologies such as cloud computing and big data, the security issues of industrial cyber-physical systems (ICPSs) have become more complicated. Meanwhile, a lot of current security research lacks adaptation to industrial system upgrades. In this paper, an edge-cloud synergy framework for security decision-making is proposed, which takes advantage of the huge convenience and advantages brought by cloud computing and edge computing, and can make security decisions on a global perspective. Under this framework, a combination of Bayesian network-based risk assessment and stochastic game model-based security decision-making is proposed to generate an optimal defense strategy to minimize system losses. this method trains models in the clouds and infers at the edge computing nodes to achieve rapid defense strategy generation. Finally, a case study on the hardware-in-the-loop simulation platform proves the feasibility of the approach.

关键词： Security Cloud computing Games Bayes methods Computational modeling Risk management Image edge detection

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Experiment Verification of High Order Differential Feedback control for Quadrotor UAV

Experiment Verification of High Order Differential Feedback ...

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data driven control and learning systems (ddcls)

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

ISBN: (数字)9781728159225

ISBN: (纸本)9781728159232

An outer-loop model-free controller called high order differential feedback control (HODFC) that can compute the velocity of position and yaw is applied to accomplish the flight tasks based on indoor optical positioning system. A high precision observer called high order differentiator (HOD) is introduced to observe the differential states of the system output and given input. A filtering signal of control output is incorporated in the control law to estimate the information related to system model. the remarkable features of the HODFC includes model-free performance, pole assignment based on theory, the disturbance suppression. Comparison results of the HODFC and the proportional-integral-derivative (PID) trajectory tracking experiment in height, yaw and x - y plane, and the disturbance experiments are presented to illustrate the superior performance of the HODFC method over the PID method.

关键词： Mathematical model Trajectory tracking Filtering Feedback control Atmospheric modeling Task analysis Gravity

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Root Cause Diagnosis of Plant Wide Oscillations using Kernel Granger Causality

Root Cause Diagnosis of Plant Wide Oscillations using Kernel...

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data driven control and learning systems (ddcls)

作者： Long-Fei Deng Jian-Guo Wang Jing-Ru Su Yuan Yao Jian-Long Liu School of Mechatronical Engineering and Automation Shanghai University Shanghai Key Lab of Power Station Automation Technology Shanghai China National Tsing-Hua University Hsin-Chu Taiwan Shanghai Minghua Electric Power Science &Technology Co. Ltd. Shanghai China

ISBN: (数字)9781728159225

ISBN: (纸本)9781728159232

Plant wide oscillations are very common in industrial processes. When a control unit oscillates in the process, the oscillation will propagate through the connectivity between units, which will cause problems such as poor product quality and higher energy consumption. therefore, it is very important to diagnose the root cause of plant wide oscillations [1]. In this dissertation, the kernel Granger causality is proposed for root cause diagnosis, which effectively solves the problem that the linear Granger causality cannot deal with nonlinear data. A small nonlinear numerical example shows the validity of kernel Granger causality. In the root cause diagnosis of industrial cases, the method also successfully detected the correct root point (LC2).

关键词： Kernel Oscillators Process control Aerospace electronics Time series analysis Indexes Companies

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