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检索条件"任意字段=7th IEEE Data Driven Control and Learning Systems Conference, DDCLS 2018"

共 474 条记录，以下是411-420 订阅

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Performance Analysis of Marine Guidance Systematic Error Separation based on Linear Model

Performance Analysis of Marine Guidance Systematic Error Sep...

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data driven control and learning systems (ddcls)

作者： Xuanying Zhou Zhengming Wang Dong Li Jiongqi Wang Department of Mathematics Nation University of Defense and Technology Hunan P. R. China College of Systems Engineering Nation University of Defense and Technology Hunan P. R. China Unit 94 PLA 91550 Liaoning P. R. China

ISBN: (纸本)9781538626191

As the guidance systematic errors of inertial missiles directly determine the guidance accuracy, error separation is a vital data processing problem. the key point of error separation is to find out a good parameter estimation method and to design a suitable estimation strategy according to the errors' physical characteristics. Based on the linear regression model of the guidance systematic error separation, this study gives the comparisons of four parameter estimation methods, which are Least Square Estimation(LSE), Bayesian estimation, Principal Component Analysis(PCA) and regularization method, and gives the simulations of PCA and Regularization method. Moreover, combining with the initial errors of sea-based missiles, we design two estimation strategies named the sorting strategy and the iteration strategy. the results illustrate that these two new strategies can separate more errors than the traditional overall strategy.

关键词： Systematics Estimation Missiles Instruments Bayes methods Sorting Parameter estimation

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VISSIM Parameter Calibration Based on Traffic Characteristics Distribution at Signalized Intersections

VISSIM Parameter Calibration Based on Traffic Characteristic...

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data driven control and learning systems (ddcls)

作者： Ning Li Yujie Sun State Nuclear Electric Power Planning Design & Research Institute Co. Ltd Beijing

ISBN: (纸本)9781538626191

In order to increase the accuracy of traffic simulation and better reproduce the real traffic condition at signalized intersections, this paper proposed a parameter calibration method based on the traffic distribution rules at signalized intersections. First, after qualitatively analyzing the traffic condition at signalized intersections based on dynamic traffic features, this paper selected the key parameters that need to be calibrated. then, regarding the selected key parameters, this paper first designed and implemented the collecting method. then filtered and analyzed the data, and acquired the distribution pattern of each key parameter at signalized intersection. Finally, in order to validate the calibration process based on vehicle types through simulation, this paper chose travel time and number of stops as validation parameters. the results showed that there had been a great increase in the accuracy after calibration. the maximum inaccuracy among all evaluation parameters was 14.6%, which indicated that the calibration process based on traffic characteristics distribution at signalized intersections was effective.

关键词： Calibration Roads Acceleration data models Vehicle dynamics Global Positioning System Traffic control

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Improved Kernel Fisher Discriminant Analysis for Nonlinear Process Fault Pattern Recognition

Improved Kernel Fisher Discriminant Analysis for Nonlinear P...

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data driven control and learning systems (ddcls)

作者： Xiaogang Deng Baowei Sun Lei Wang College of Information and Control Engineering China University of Petroleum Qingdao P. R. China

ISBN: (纸本)9781538626191

Kernel Fisher discriminant analysis (KFDA) has emerged as an well-known nonlinear fault pattern recognition method. However, traditional KFDA method does not consider the utilization of the high order statistical information of process variables, and ignores the mining of the local data structure characteristic. To achieve better fault pattern recognition performance, this paper proposes an improved KFDA method, called statistics local KFDA(SLKFDA). In the proposed method, two techniques, including statistics pattern analysis (SPA) and local structure analysis (LSA), are combined to enhance the basic KFDA method. Firstly, SPA is applied to extract the original process variables' statistics with different orders. then the KFDA optimization objective is modified by considering the local structure preserving. Lastly, a fault classifier is developed to recognize fault pattern. Simulations on one benchmark process demonstrate that the proposed SLKFDA method has a superior fault pattern recognition performance.

关键词： Kernel Pattern recognition Optimization Pattern analysis data models data structures Fault diagnosis

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Fault-Tolerant Motion Planning of Redundant Manipulator with Initial Position Error

Fault-Tolerant Motion Planning of Redundant Manipulator with...

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data driven control and learning systems (ddcls)

作者： Kene Li Jin Yang Chengzhi Yuan Jianqin Xu Xisheng Dai Jiawei Luo Department of Mechanical Industrial and Systems Engineering University of Rhode Island Kingston RI USA School of Electrical and Information Engineering Guangxi University of Science and Technology Liuzhou China Engineering Training Center Guangxi University of Science and Technology Liuzhou China Jiangxi Vocational College of Industry and Engineering Pingxiang China

ISBN: (纸本)9781538626191

In the robotic manipulator operation practice, it is necessary to adjust the manipulator initial state to an accurate configuration for executing a given path tracking task. However, it is difficult to achieve a desired accurate configuration, which would lead to an unexpected initial position error of the end-effector. In this paper, based on a new neural-dynamic design method, i.e., Zhang dynamics, a fault-tolerant motion planning scheme is presented to diminish the initial position error arising in the manipulator state adjustment. Such a motion planning scheme of redundant manipulators can rapidly and smoothly diminish the initial position error during the task execution. Computer simulations are presented to illustrate the validity and advantages of the fault-tolerant motion planning scheme with an initial position error based on a four-link manipulator model.

关键词： End effectors Task analysis Planning Fault tolerance Fault tolerant systems

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On Closed - loop control of Matrix Converter with Double Voltage control

On Closed - loop Control of Matrix Converter with Double Vol...

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data driven control and learning systems (ddcls)

作者： Xinghe Ma Zhendong Zhang Dan Xu Kunchao Wang School of electrical Engineering and Automation Henan Polytechnic University Jiaozuo China

ISBN: (纸本)9781538626191

A novel closed-loop control strategy is designed and researched for a matrix converter with dual voltage synthesis control. the closed-loop control strategy is based on the dual-voltage control of the matrix converter duty cycle calculation characteristics, the deviation between the ideal input voltage duty cycle and the equivalent input voltage duty cycle is calculated, and the calculated deviation is added as the negative feedback variable of the closed loop system to the next duty cycle calculation period. In order to achieve the purpose of closed-loop control, the closed-loop control strategy proposed in this paper is used to solve the problem that the output side voltage performance of the matrix converter is affected by the input side voltage distortion and the performance of the internal components of the matrix converter is not ideal. To ensure that the actual output voltage of the matrix converter can better meet the desired voltage, improve its disturbance after the output voltage quality reduction and improve the output performance. the experimental results show that the output voltage of the matrix converter with this new closed - loop control method is closer to the desired voltage, the output current waveform is smoother and the output voltage quality is more ideal.

关键词： Voltage control Matrix converters Negative feedback Closed loop systems Modulation Uncertainty

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Tracking control Strategy of PMSLM with A Novel Observer-based Compensator and A RBFNN-based controller

Tracking Control Strategy of PMSLM with A Novel Observer-bas...

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data driven control and learning systems (ddcls)

作者： Zhentian Liu Guangsen Wang Zhiwei Wang National Key Laboratory of Science and Technology on Vessel Integrated Power System Naval Univ. of Engineering Wuhan

ISBN: (纸本)9781538626191

this paper is devoted to a high-precision tracking control strategy of permanent magnet synchronous linear motor (PMSLM). Firstly, the field-oriented control model of the PMSLM is established to calculate the electromagnetic thrust. To reconstruct the system states and reject the lump disturbance, a novel observer-based compensator is proposed, by taking the basic ideas of the frequency-domain disturbance observer and the time-domain one (the extended state observer in active disturbance rejection control, ADRC). A radial-basis-function neural network (RBFNN) controller with accurate approximation capability is utilized to tracking the desired motion trajectory. Contrasted to the RBFNN's parameters self-evolved, a simple and unique parameters tuning method is derived to guarantee the compensator performance. All the proposed algorithms are implemented in a rapid control prototype (RCP) real-time simulation platform and the simulation and experiment results validate the rightness of theoretical analysis and the feasibility of the proposed methods.

关键词： Uncertainty Mathematical model Permanent magnets Synchronous motors Tuning Robustness Frequency-domain analysis

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Balanced Levitation control of PEMS High Speed Maglev Train Considering System Model Non-Symmetry

Balanced Levitation Control of PEMS High Speed Maglev Train ...

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data driven control and learning systems (ddcls)

作者： Zhiqiang Wang Cuicui Huang Xiaolong Li Zhiqiang Long National University of Defense Technology Changsha P. R. China

ISBN: (纸本)9781538626191

PEMS (Permanent magnetic and Electro-Magnetic Suspension) high speed maglev train is proposed on the basis of normal EMS (Electro-Magnetic Suspension) high speed maglev train with permanent magnet added into the electromagnet for the purpose of reducing the levitation current. In high speed maglev train, joint structure which consists of two subsystems is the fundamental levitation unit. the advantage of this joint structure is: the two subsystems can share the burden of the load which makes the levitation current small. However, it is found in practical engineering that due to the system non-symmetry, especially the difference between two levitation gap signals, the burden of load is not equally shared which makes one of the subsystem bears almost the entire burden. Under this circumstance the over load subsystem is vulnerable to external disturbances and the other subsystem is not fully taken use of. To solve this problem, a balanced control method is proposed. Simulation results have verified the effectiveness of the balanced control method.

关键词： Levitation Electromagnets Periodic structures Sensors Electromagnetic forces Springs Windings

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Modeling and Analysis of the Novel Stator Excitation Brushless Motor Based on Active Disturbance Rejection control

Modeling and Analysis of the Novel Stator Excitation Brushle...

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data driven control and learning systems (ddcls)

作者： Kelei Wang Zengqiang Chen Mingwei Sun Qinglin Sun College of Computer and Control Engineering Nankai University Tianjin P R. China

ISBN: (纸本)9781538626191

A novel stator excitation brushless motor having no windings and magnets in the rotor is proposed in this paper. the stator of the motor contains two sets of windings which named three-phase power windings and excitation windings, respectively. In the grasp of the working principles of the stator excitation brushless motor, the mathematical model in the rotor rotating coordinate system is deduced and the characteristics of the model with strongly coupling and strongly nonlinear are verified by simulation. A speed regulating system based on the first-order linear active disturbance rejection control is designed. the linear extended state observer can estimate and compensate the general disturbances, making the flux and torque components decoupled. the simulation results show that the linear active disturbance rejection control not only has better dynamic and static characteristics than traditional PI algorithm, but also has stronger robustness to the load mutation and parameter variations of the motor.

关键词： Brushless motors Mathematical model Permanent magnet motors Stator windings Windings Hysteresis motors

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Ensemble of Extreme learning Machines for Regression

Ensemble of Extreme Learning Machines for Regression

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data driven control and learning systems (ddcls)

作者： Atmane Khellal Hongbin Ma Qing Fei School of Automation Beijing Institute of Technology Beijing China State Key Laboratory of Intelligent Control and Decision of Complex Systems Beijing Institute of Technology China

ISBN: (纸本)9781538626191

Regression, as a particular task of machine learning, performs a vital part in data-driven modeling, by finding the connections between the system state variables without any explicit knowledge about the system, using a collection of input-output data. To enhance the prediction performance and maximize the training speed, we propose a fully learnable ensemble of Extreme learning Machines (ELMs) for regression. the developed approach learns the combination of different individual models, using the ELM algorithm, which is applied to minimize both the prediction error and the norm of the network parameters, which leads to higher generalization performance under Bartlett's theory. Moreover, the average based ELM ensemble may be viewed as a particular case of our model. Extensive experiments on many standard regression benchmark datasets have been carried out, and comparison with different models has been performed. the experimental findings confirm that the proposed ensemble can reach competitive results in term of the generalization performance, and the training speed. Furthermore, the influence of different hyper parameters on the performance, in term of the prediction error and the training speed, of the developed model has been investigated to provide a meaningful guideline to practical applications.

关键词： Computational modeling Training Mathematical model Predictive models Neural networks Prediction algorithms Approximation algorithms

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Subordinate based Cluster Center Identification in Density Peak Clustering

Subordinate based Cluster Center Identification in Density P...

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data driven control and learning systems (ddcls)

作者： Jian Hou Aihua Zhang Lv Chengcong E Xu College of Engineering Institute of Automation Bohai University Jinzhou China College of Engineering Bohai University Jinzhou China College of Information Science Bohai University Jinzhou China

ISBN: (纸本)9781538626191

Recently, a clustering algorithm is proposed by treating local density peaks as cluster centers. this algorithm proposes to describe the data to be clustered with local density and the distance of one data to the nearest data of larger local density. this description highlights the uniqueness of cluster centers and is utilized to determine cluster centers. With the assumption that one data and the nearest data of larger local density are in the same cluster, the non-center data are assigned labels efficiently. By studying the clustering process of this algorithm in depth, we find that the local density is not very effective in highlighting the uniqueness of cluster centers. As a result, this algorithm is dependent on the parameters in local density calculation. We discuss this problem and find that it is the role of density peaks, but not the absolute local density, that highlights the uniqueness of cluster centers. Based on this observation, we introduce the concept of subordinate and use the amount of subordinates to replace the local density in cluster center identification. Together with a new density kernel, this new criterion is shown to be effective in experiments and comparisons.

关键词： Clustering algorithms Kernel Fires Spirals Iris Distributed databases control systems

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