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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101-110 订阅

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Adaptive Fuzzy control of Nonlinear systems Based on T-S fuzzy Hyperbolic Model 7

Adaptive Fuzzy Control of Nonlinear Systems Based on T-S fuz...

引用

ieee 7th data driven control and learning systems conference (ddcls)

作者： Shi, Naizheng Li, Junmin Li, Pei An, Haitao Wang, Chong Xian Modern Control Technol Res Inst Xian 710065 Shaanxi Peoples R China Xidian Univ Dept Operat Res & Cybernet Sch Math & Stat Xian 710071 Peoples R China

ISBN: (纸本)9781538626184

In this paper, a design scheme is proposed for a class of nonlinear systems, which can be modeled by a T-S fuzzy hyperbolic model of stable fuzzy control. Firstly, the parallel distributed compensation (PDC) method is employed to design the fuzzy controller for the system without considering the error caused by fuzzy modeling, and the sufficient conditions of stability are given by using linear matrix inequalities (LMI). then the error caused by fuzzy modeling is considered and the method of adaptive control is employed to reduce the effect of the modeling error. Simultaneously, the closed-loop system has better dynamic performance. According to Lyapunov stability theory, the resulting closed-loop system is proved to be asymptotically stable. Finally, an illustrative example is given to verify the effectiveness of the results proposed in this paper.

关键词： T-S fuzzy control linear matrix inequalities (LMI) fuzzy hyperbolic model (FHM) adaptive control parallel distributed compensation (PDC)

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Empirical Analysis of Arterial Fundamental Diagram and Optimization by Floating Car data 7

Empirical Analysis of Arterial Fundamental Diagram and Optim...

引用

ieee 7th data driven control and learning systems conference (ddcls)

作者： He, Zhonghe Chen, Ming Zhang, Haibo Wang, Li North China Univ Technol Beijing Key Lab Urban Intelligent Traff Control T Beijing 100144 Peoples R China

ISBN: (纸本)9781538626184

the fundamental diagram (FD) reflects the operational status of road network traffic and it is a model describing the relationship between traffic density (traffic volume and occupancy rate) at steady state. In this paper, an empirical study is carried out on the model of Nanhuan Road in Changping District. the basic data of flow rate, occupancy rate and driving speed are obtained from the data of geomagnetic detector and floating car, and then the current road status are analyzed. According to the existing data, the traffic flow of the arterial is simulated. Based on the geographical features of the arterial and the traffic flow data, the green wave band control scheme is calculated and the data after the coordinated control is extracted. In the study of arterial coordination control, the influence of the coordination control parameters on FD shape and its influence on the capacity are analyzed.

关键词： Fundamental diagram Arterial coordination Floating car

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Active Disturbance Rejection Based Iterative learning control for Variable Air Volume Central Air-Conditioning System 7

Active Disturbance Rejection Based Iterative Learning Contro...

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ieee 7th data driven control and learning systems conference (ddcls)

作者： Lu, Shiying Ai, Wei Li, Xiangyang South China Univ Technol Sch Automat Sci & Engn Minist Educ Key Lab Autonomous Syst & Networked Control Guangzhou 510641 Guangdong Peoples R China

ISBN: (纸本)9781538626184

the Variable Air Volume (VAV) Central Air-Conditioning system is a complicated system with non-linearity, large-time delay and strong inertia, thus it is difficult to design an effective controller. Iterative learning control (ILC) takes good effect in controlled process with repeatability and periodicity, but it cannot cope with uncertain disturbance explicitly. A creative algorithm, Active Disturbance Rejection based Iterative learning control (ADR-Based ILC), is proposed to improve ILC's performance in VAV control system. ADR-Based ILC compensates the disturbance explicitly caused by ambient temperature, heat from people and machines, and makes it higher control precision and higher energy-efficiency. An accurate model of VAV system is built in TRNSYS platform, and ADR-Based ILC is proved to be more effective than fuzzy PID and ILC.

关键词： Variable Air Volume Central Air-Conditioning system Active Disturbance Rejection based Iterative learning control TRNSYS

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A Unified Iterative learning Fault Detection and Fault-Tolerant control 7

A Unified Iterative Learning Fault Detection and Fault-Toler...

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ieee 7th data driven control and learning systems conference (ddcls)

作者： Yan, Qiuzhen Yu, Youfang Cai, Jianping Zhou, Qingping Zhejiang Univ Water Resources & Elect Power Coll Informat Engn Hangzhou 310018 Peoples R China Zhejiang Business Coll Appl Engn Coll Hangzhou 310053 Zhejiang Peoples R China Zhejiang Univ Water Resources & Elect Power Sch Elect Engn Hangzhou 310018 Peoples R China Tangshan Normal Univ Appl Engn Coll Tangshan 063000 Peoples R China

ISBN: (纸本)9781538626184

In this paper, a unified iterative learning based fault detection and fault-tolerant control scheme is proposed. A system fault detector is constructed by using contraction mapping technique, and LMI technique is applied in the design of Lyapnov-based iterative controller, responsible for solving the state tracking problem no matter whether faults occur or not. Numerical results demonstrate the effectiveness of the proposed unified fault detection and control scheme.

关键词： Fault Detection Iterative learning control Fault-Tolerant control Contraction Mapping Lyapunov-Based Synthesis

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A KNN-SVR data Mending Method for Insufficient data of Magnetic Flux Leakage Detection 7

A KNN-SVR Data Mending Method for Insufficient Data of Magne...

引用

ieee 7th data driven control and learning systems conference (ddcls)

作者： Zhang, Xinbo Feng, Jian Yao, Zhiqiang Liu, Jinhai Zhang, Huaguang Northeastern Univ Sch Informat Sci & Engn Shenyang 110819 Liaoning Peoples R China China Acad Safety Sci & Technol Beijing 100012 Peoples R China

ISBN: (纸本)9781538626184

In magnetic flux leakage (MFL) detection, transient fault appears unavoidably on individual sensor when we collect magnetic flux leakage signals, which makes MFL data insufficient. data mending for insufficient data concerns the accuracy of the defects inversion. A precise data mending method based on K Nearest Neighbor-Support Vector Regression (KNN-SVR) is introduced, which effectively reduces the training cost of SVR and greatly improves the accuracy of the algorithm. the method is tested by experiment data obtained. the results demonstrate that the proposed method can improve the accuracy rate of data mending of insufficient data with an acceptable time cost.

关键词： data mending K Nearest Neighbor Support Vector Regression magnetic flux leakage detection insufficient data

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Ensemble of Extreme learning Machines for Regression 7

Ensemble of Extreme Learning Machines for Regression

引用

ieee 7th data driven control and learning systems conference (ddcls)

作者： Khellal, Atmane Ma, Hongbin Fei, Qing Beijing Inst Technol Sch Automat Beijing 100081 Peoples R China Beijing Inst Technol State Key Lab Intelligent Control & Decis Complex Beijing 100081 Peoples R China

ISBN: (纸本)9781538626184

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 inputoutput 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 hyperparameters 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： data-driven approach Ensemble Extreme learning Machine Regression

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Unknown Input and Measurement Noise Estimations for Switched Nonlinear systems 7

Unknown Input and Measurement Noise Estimations for Switched...

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ieee 7th data driven control and learning systems conference (ddcls)

作者： Zhu, Fanglai Zhang, Jiancheng Wang, Fengning Guo, Shenghui Tongji Univ Coll Elect & Informat Engn Shanghai 201804 Peoples R China Suzhou Univ Sci & Technol Coll Elect & Informat Engn Suzhou 215009 Peoples R China

ISBN: (纸本)9781538626184

the problem of unknown input and measurement noise estimations for a class of switched Lipschitz nonlinear systems is investigated in this paper. An augmented state is used to construct a new descriptor system to deal with the measurement noise in output vector, and then the descriptor system does not contain measurement noise in form. the main results are for the constructed descriptor system, a new Lyapunov-type precondition is developed in detail to present a sliding mode observer, which can estimate both the original system states and unknown inputs simultaneously. And the sliding model term is introduced to deal with the system nonlinearity and the unknown input. Finally, a simulation example of an electric circuit system is considered to show the effectiveness of the proposed methods.

关键词： Unknown inputs Measurement noise Sliding model Switched nonlinear system

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Resilient Consensus with Switching Networks and Double-Integrator Agents 7

Resilient Consensus with Switching Networks and Double-Integ...

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ieee 7th data driven control and learning systems conference (ddcls)

作者： Huang, Jinbo Wu, Yiming Chang, Liping He, Xiongxiong Li, Sheng Zhejiang Univ Technol Coll Informat Engn Hangzhou 310023 Zhejiang Peoples R China Hangzhou Dianzi Univ Sch Cyberspace Hangzhou 310018 Zhejiang Peoples R China

ISBN: (纸本)9781538626184

In this paper, we investigate the resilient consensus problem for the second-order multi-agent system communicating via switching networks. the term resilient means that the control protocols should consider the presence of attacks by some malicious agents. Assuming that the maximum number of malicious agents in the neighborhood of each agent is bounded and known, we propose a local neighbors' information-based on distributed consensus protocol suitable for time-varying topologies to deal with the malicious attacks. It is shown that if the union of communication graphs over a bounded period satisfies certain network robustness property, the states of all normal agents can be guaranteed to reach an agreement resiliently. Numerical simulations are provided to illustrate the effectiveness of the theoretical results.

关键词： Multi-agent systems Resilient consensus Double-integrator systems Time-varying

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Iterative Identification for A Class of Closed-loop systems Based on A Greedy Algorithm 7

Iterative Identification for A Class of Closed-loop Systems ...

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ieee 7th data driven control and learning systems conference (ddcls)

作者： You, Junyao Xu, Huan Liu, Yanjun Chen, Jing Jiangnan Univ Sch Internet Things Engn Wuxi 214122 Peoples R China Jiangnan Univ Lab Adv Proc Control Light Ind Minist Educ Wuxi 214122 Peoples R China

ISBN: (纸本)9781538626184

A compressive sampling matching pursuit (CoSaMP) iterative algorithm is proposed in this paper to identify parameters and time-delays of a class of closed-loop systems where the forward channel is a CARMA model. Due to the unknown time-delays of both the feedback controller and the controlled plant, a high dimensional identification model with a sparse parameter vector is derived by using an overparameterized method. then combining the CoSaMP algorithm with the iterative idea, the parameter vector is estimated and the unmeasurable noise items are updated in each iteration. Finally, the parameters of the feedback controller are extracted based on the model equivalence principle and time-delays are estimated according to the sparse characteristic of the parameter vector. the proposed method can simultaneously estimate the parameters and time-delays from a small number of sampled data. the simulation results illustrate that the proposed algorithm is effective.

关键词： Closed-loop System Parameter Identification Time-delay Estimation Compressive Sampling Matching Pursuit Algorithm Iteration

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Wavelons-constructed Autoencoder-based Deep Neural Network for Fault Detection in Chemical Processes 7

Wavelons-constructed Autoencoder-based Deep Neural Network f...

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ieee 7th data driven control and learning systems conference (ddcls)

作者： Jin, Miao Yang, Weidong Wang, Yan Zhang, Hong Huazhong Univ Sci & Technol Wuhan 430074 Hubei Peoples R China Zhengzhou Univ Light Ind Zhengzhou 450002 Henan Peoples R China

ISBN: (纸本)9781538626184

Considering the co-existence of the measured variables and the control variables in chemical process, a wavelons-constructed autoencoder-based deep neural network (WA-DNN) method is proposed in this paper. Firstly, an autoencoder is constructed with wavelons (neurons with wavelet activation function) to analyze the input signal. then, the deep learning structure with ELUs is adopted to extract the deep complex features of the fault to be fed into the softmax classifier, the binary output of which represents whether the fault occurs or not. In our algorithm, the ELUs are able to code the degree of the presence of particular phenomena in the frequency-domain features that wavelons acquire from measured signals. At the same time, neurons are capable of memorizing the numerical value of temporal and spatial information of variables. Experiment results on Tennessee Eastman Process (TEP) show that the proposed method improves the accuracy of fault diagnosis compared with the existing algorithms.

关键词： wavelons autoencoder deep learning softmax classifier fault diagnosis

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