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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131-140 订阅

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A data-driven Optimal ILC Method Incorporated with Extended State Observer for Nonlinear Discrete-time Repetitive systems

A Data-driven Optimal ILC Method Incorporated with Extended ...

引用

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

作者： Hui Yu Zhang Shuhua Chi Ronghu Qingdao Univ Sci & Technol Sch Automat & Elect Engn Qingdao 266061 Peoples R China

ISBN: (纸本)9781538626184

In this work, a novel data-driven optimal ILC with an extended state observer for a class of nonlinear non-affine discrete-time repetitive system has been proposed. the main feature of the approach is that the controller design depends merely on the I/O data, and an ESO has been introduced for the estimation of disturbance and uncertainty. the final simulation results verify the effectiveness of the proposed method.

关键词： uncertainty disturbance data driven extended state observer dynamic linearization iterative learning control

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Overview of Image Segmentation and Its Application on Free Space Detection 7

Overview of Image Segmentation and Its Application on Free S...

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

作者： Zhao, Xiaodong Zhang, Qichao Zhao, Dongbin Pang, Zhonghua Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China North China Univ Technol Beijing 100144 Peoples R China Univ Chinese Acad Sci Beijing 100049 Peoples R China

ISBN: (纸本)9781538626184

With the development of deep learning technique, image segmentation has received spreading attention in the computer vision field. It has a wide range of applications such as scene understanding, autonomous driving and so on. For the image segmentation, we can divide it into the semantic segmentation and instance segmentation, where a high-quality segmentation label for each instance is required for the latter method. In this paper, we sort out the popular structures of semantic segmentation and introduce the instance segmentation briefly. In the experiments, three main semantic segmentation methods are tested and analyzed based on the opened CamVid dataset, and the experiments for free space detection based on two popular segmentation methods are given.

关键词： Image segmentation semantic segmentation instance segmentation free space detection

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Sliding mode control of the penicillin fermentation system based on nonlinear disturbance observer 7

Sliding mode control of the penicillin fermentation system b...

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7th ieee data driven control and learning systems conference, ddcls 2018

作者： Tengfei, Zhang Xing, Fang Fei, Liu Key Laboratory of Advanced Process Control for Light Industry Jiangnan University Institute of Automation Wuxi Jiangsu214122 China

ISBN: (纸本)9781538626184

A feed-forward compensation strategy for the disturbance is proposed for the control problem of the bacteria in the fermentation process of the penicillin. Firstly, the nonlinear disturbance observer is designed to estimate the lumped disturbance of the system. then, a sliding mode control law is designed for the system. the design of the control law guarantees the closed-loop system is asymptotically stable and achieves the purpose of tracking control for the bacteria's concentration in the system. the simulation results show that this method can enhance the anti-disturbance capability and improve the control performance of the system. © 2018 ieee.

关键词： Sliding mode control

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A Novel Scalable Semi-supervised GMM and Its Application for Multimode Process Quality Prediction with Big data 7

A Novel Scalable Semi-supervised GMM and Its Application for...

引用

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

作者： Yao, Le Ge, Zhiqiang Shao, Weiming Song, Zhihuan Zhejiang Univ Inst Ind Proc Control Coll Control Sci & Engn State Key Lab Ind Control Technol Hangzhou 310027 Zhejiang Peoples R China

ISBN: (纸本)9781538626184

In this paper, a novel variational inference semi-supervised GMM (VI-S(2)GMM) model is firstly proposed for multimode process predictive modeling with semi-supervised data. Since all the labeled and unlabeled data samples are involved in each iteration of parameter updating, an intractable computing problem occurs when facing a high-dimension and large-scale dataset. To tack this problem, a scalable Stochastic Variational Inference semi-supervised GMM (SVI-S(2)GMM) is further proposed for massive semi-supervised data. through taking advantage of stochastic gradient optimization algorithm to maximize the Evidence of Lower Bound (ELBO), the VI-based algorithm becomes scalable. In SVI-S(2)GMM, only one or a mini-batch of samples is randomly selected to update parameters in each iteration, which is more efficient than VI-S(2)GMM. In this way, a large number of unlabeled process data can be useful in the modeling, which will benefit the parameter identification. the SVI-S(2)GMM is then exploited for the prediction of quality-related key performance index (KPI). Two modeling cases with large scale of semi-supervised datasets demonstrate the feasibility and effectiveness of the proposed algorithms.

关键词： Stochastic Variational Inference semi-supervised modeling Gaussian Mixture Model big data multimode process modeling quality prediction

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Active Disturbance Rejection control for Active Suspension System of Nonlinear Full Car 7

Active Disturbance Rejection Control for Active Suspension S...

引用

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

作者： Lu, Yeqing Wang, Haoping Tian, Yang Nanjing Univ Sci & Technol Sch Automat LaFCAS Nanjing 210094 Jiangsu Peoples R China

ISBN: (纸本)9781538626184

In this paper, a full car model with seven degrees of freedom is established for the research of active suspension system, and a virtual prototype is built in Adams to compare and validate it, in particular. then active disturbance rejection control (ADRC) is applied for the control system. the suspension system is complex for its nonlinearities from the springs, dampers, and irregular excitations from road surface. this paper considers the nonlinear characteristics and complex behavior of real vehicles, and uses extended state observer (ESO) to estimate and eliminate them from the controlled system. After decoupling the full car system, three ADRC controllers are designed to balance the vertical vibration, rolling and pitching movement, respectively, thus producing four active control forces of each suspension by matrix transformation. the simulation results are compared with fuzzy PID controlled system, which show that ADRC has good performance over nonlinearities, perturbations and bounded uncertainties.

关键词： Nonlinear full car active suspension system active disturbance rejection control (ADRC)

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An Intelligent Car Temperature control System 7

An Intelligent Car Temperature Control System

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

作者： He, Xiongnan Jiang, Songchen Sun, Qiuye Northeastern Univ Coll Informat Sci & Engn Shenyang 110819 Peoples R China

ISBN: (纸本)9781538626184

Nowadays, more and more residential cars apply various of services of energy saving to help themselves improve performances and decrease cost. As for the car air conditioning, some put forward ideas that using neuron-fuzzy method can precisely control the cooling capacity, the other hold the view that power line communication based photovoltaic (PV) system can effectively manage the energy. In this paper, it aims to deal with the shortcomings that aforementioned do not take the realistic environment and the neuron-fuzzy method's disadvantages into consideration. As a result, this paper comes up an intelligent car temperature control system(ICTCS), which, comparing with conventional temperature control systems, has two main advantages-one is using three criterions, namely light intensity outside cars(I), temperature inside cars(T) and sunshine incident angle(alpha), to judge what kind of environment the car is in on earth and decide car cooling capacity over, the other is applying neuron-fuzzy system to train the comprehensive temperature to try its best to decrease faster. It will refrigerate in different stalls in the standard of difference between temperature inside cars and calculated most suitable temperature. Applying the above system into actual experiments, we can find under the premise that cooling effect stays nearly the same, the energy consumption gets decreased, which is to say, the ICTCS gets good results.

关键词： intelligent car temperature control system (ICTCS) neuron-fuzzy system Photovoltaic (PV) criterions regulation

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An EMD-RF Based Short-term Wind Power Forecasting Method 7

An EMD-RF Based Short-term Wind Power Forecasting Method

引用

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

作者： Shen, Weizhou Jiang, Na Li, Ning Shanghai Jiao Tong Univ Dept Automat Shanghai 200240 Peoples R China Shanghai Jiao Tong Univ Key Lab Syst Control & Informat Proc Shanghai 200240 Peoples R China

ISBN: (纸本)9781538626184

Wind power forecasting of wind field is a common problem recently. Forecasting wind power accurately is a challenge for dispatchers who need to establish dispatching strategies due to the randomness and volatility of wind power. this paper presents a kind of wind power forecasting method based on empirical mode decomposition (EMD) and random forest (RF). EMD is applied to this method to decompose wind power sequence into several intrinsic mode functions (IMF) and a residual component, and then RF is used to train each component. Finally, the predicting results of each component are summed together to obtain the wind power forecasting values. the proposed method is tested on actual data from a wind farm in America. the results show that the EMD-RF method reduces the forecasting error and tracks the change of wind power more accurately compared with the traditional forecasting model.

关键词： Wind Power Forecasting Short-term Empirical Mode Decomposition (EMD) Random Forest (RF) algorithm

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Fault Diagnosis Method Based on Improved Deep Boltzmann Machines 7

Fault Diagnosis Method Based on Improved Deep Boltzmann Mach...

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

作者： Liu, Dan Wang, Qin Tao, Jiaojiao Li, Guang Wu, Jie Xi An Jiao Tong Univ Sch Mech Engn Xian 710000 Shaanxi Peoples R China

ISBN: (纸本)9781538626184

With the increasing complexity of mechanical equipment, the traditional signal-based fault diagnosis methods cannot meet the current needs of fast, accurate and intelligent fault diagnosis due to its low efficiency and over-reliance on experience and subjective judgment of diagnosticians. Deep learning has powerful feature extraction and pattern recognition ability, and once the model is established, it can perform rapid pattern recognition. Based on this, a fault diagnosis method based on deep Boltzmann machines is proposed in this paper. Firstly, to solve the problem that DBMs can only deal with binary data, the Gaussian units are used to replace the binary visible units of the deep Boltzmann machines to construct the improved deep Boltzmann machines model, enabled the deep Boltzmann machines to process real-valued data. After the model is constructed, it is applied to process vibration signals for fault diagnosis. We present result on the CWRU bearing datasets, which shows that the improved DBMs learn generative models well and are good at fault recognition tasks.

关键词： Fault Diagnosis Deep learning Deep Boltzmann Machines

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A data-driven Voxel-wise White Matter Fiber Clustering Model Based on Priori Anatomical data 7

A Data-driven Voxel-wise White Matter Fiber Clustering Model...

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

作者： Cao, Zhewen Jin, Er Zhou, Siqi Wu, Ye Li, Yongqiang Feng, Yuanjing Zhejiang Univ Technol Inst Informat Proc & Automat Hangzhou 310023 Zhejiang Peoples R China

ISBN: (纸本)9781538626184

Whole-brain fiber imaging allows nondestructive detection of human brain structural connections. the clinical application of this method is often classified as a series of fiber bundle structures of certain significance (function, structure, shape, etc.). Due to the lack of edge structure information of fiber bundles and the high variability of complex white matter structures in individual samples, fiber clustering based on anatomical information is still an open problem. In this paper, a new fiber clustering technique is proposed, which combines spatial features of whole-brain fibers and prior anatomical information as fiber similarity matching and feature extraction. In this work, we focus on the coverage of highly consistent fiber bundles in white matter structures to match anatomic features. the method is based on multiple tests of simulated data and in vivol data. the experimental results show that this method not only improves the highly consistent coverage of fiber bundles and prior anatomical knowledge, but also simplifies the fiber data space to improve the fiber clustering similarity measured population consistency. Finally, we also discuss the application of this method in clinical research.

关键词： Tractography White matter fiber Fiber bundle Clustering Anatomical feature

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Continuous Multivariable Integral Sliding Mode control of Rigid Spacecraft with Actuator Faults 7

Continuous Multivariable Integral Sliding Mode Control of Ri...

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

作者： Zhang, Xiuyun Zong, Qun Liu, Wenjing Wang, Jie Tianjin Univ Sch Elect Engn & Informat Tianjin 300072 Peoples R China Beijing Inst Control Engn Sci & Technol Space Intelligent Control Lab Beijing 100000 Peoples R China Hebei Univ Technol Sch Control Sci & Energineering Tianjin 300130 Peoples R China

ISBN: (纸本)9781538626184

this paper investigates the fault-tolerant control (FTC) for the rigid spacecraft. A continuous multivariable integral sliding mode (CMISM) FTC is developed, which is capable of ensuring the finite-time stability of the closed-loop system in the presence of actuator malfunctions and external disturbances. Firstly, a smooth second order controller is designed for the finite time convergence of nominal system. then, the conventional discontinuous part of ISM to reject faults and disturbances is modified by a continuous multivariable twisting control, which could obtain a better dynamic response and anti-disturbance performance. A rigorous proof of the finite time stability of closed-loop system is derived by utilizing Lyapunov method. Finally, the efficiency of the proposed method is illustrated by numerical simulations.

关键词： Fault-Tolerant control (FTC) Rigid Spacecraft Continuous Multivariable Integral Sliding Mode (CMISM) Finite-Time control

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