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检索条件"任意字段=10th IEEE Data Driven Control and Learning Systems Conference, DDCLS 2021"

共 359 条记录，以下是41-50 订阅

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Deep neural network classification of EEG data in schizophrenia 10

Deep neural network classification of EEG data in schizophre...

引用

10th ieee data driven control and learning systems conference, ddcls 2021

作者： Guo, Zhifen Wu, Lezhou Li, Yun Li, Beilin College of Information Science and Engineering Northeastern University Shenyang110819 China

ISBN: (纸本)9781665424233

Schizophrenia(SZ) is a disease of unknown etiology and pathogenesis and is ranked by the World Health Organization as one of the top ten diseases contributing to the global burden of disease. Studying the internal physiological differences between EEG of schizophrenia patients and normal individuals is important for diagnosing and treating schizophrenia in order to determine objective physiological diagnostic criteria. the EEG data of patients with schizophrenia were preprocessed and markers were extracted. the convolutional neural network was used to characterize the difference of distributed structure of data for classification and the classification results were given. the accuracy of the classification was 92%, and the disease classification was effectively performed using deep learning networks. © 2021 ieee.

关键词： Diseases

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Leader-follower consensus control of linear multi-agent systems with unknown disturbances 10

Leader-follower consensus control of linear multi-agent syst...

引用

10th ieee data driven control and learning systems conference, ddcls 2021

作者： Ma, Li Zhu, Fanglai Tongji University Shanghai China

ISBN: (纸本)9781665424233

In this paper, a leader-follower asymptotic consensus control strategy is designed for a class of linear multi-agent systems with unknown external disturbances. In such a design, a reduced-order observer which can produce the asymptotical system state estimation by decoupling the unknown disturbances is presented firstly. Meanwhile, with the help of a state equivalent transformation, an interval observer is set up, and then a novel disturbances reconstruction is obtained, which is decoupled from the control input and can approach the unknown disturbances asymptotically. Subsequently, in terms of the relative estimated states between neighbors and its own disturbance reconstruction information, the consensus control protocol is proposed for each agent. It is displayed that the leader-follower consensus issue can be tackled under the developed control protocol. Finally, a simulation example is given to verify the feasibility of the presented control method. © 2021 ieee.

关键词： Multi agent systems

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A Novel Framework for Hierarchical Fault Detection under the Supervision of Key Performance Indicators 10

A Novel Framework for Hierarchical Fault Detection under the...

引用

10th ieee data driven control and learning systems conference, ddcls 2021

作者： Liu, Ruijie Yang, Ying State Key Laboratory for Turbulence and Complex Systems College of Engineering Peking University Department of Mechanics and Engineering Science Beijing100871 China

ISBN: (纸本)9781665424233

this paper proposes a novel key performance indicators (KPIs) supervised hierarchical fault detection framework for model-free linear dynamic systems. In the ground process layer, a bank of residual generators are constructed with the aid of the subspace identification method, based on which the faulty process variables are isolated and the corresponding fault distribution matrix is derived. In the upper KPIs supervised layer, the dynamic relationship between the high level KPIs and the low level process variables is identified and the fault detection is realized by checking the geometric property of the fault distribution matrix transmitted from the ground layer. A numerical example is provided in the end to demonstrate the effectiveness of the proposed fault detection framework. © 2021 ieee.

关键词： Fault detection

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A Discrete-time Integral Sliding Mode control Method Based on Iterative Dynamic Linearization data Model 10

A Discrete-time Integral Sliding Mode Control Method Based o...

引用

10th ieee data driven control and learning systems conference, ddcls 2021

作者： Hou, Mingdong Tian, Jie School of Information Science and Electrical Engineering Shandong Jiaotong University Jinan250357 China School of Data and Computer Science Shandong Women's University Jinan250300 China

ISBN: (纸本)9781665424233

A data-driven integral sliding mode control scheme is proposed for a class discrete-time repetitive nonlinear system in this article. First, an iterative dynamic linearization data model is established in the iterative domain based on iterative dynamic linearization technology, which is only related to the online data of the system. then, considering the time and iterative domain at the same time, an iterative integral sliding mode surface is designed, and an iterative integral sliding mode controller is proposed. Moreover, the stability of the proposed scheme is proved by theoretical analysis. Finally, the effectiveness of the proposed scheme is verified by simulation research. Comparative results with optimal iterative learning controller demonstrate that the proposed controller can provide the minimize the control effort and less tracking error. © 2021 ieee.

关键词： Nonlinear systems

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Trajectory Tracking control of High-Altitude Wind Power Parafoil 10

Trajectory Tracking Control of High-Altitude Wind Power Para...

引用

10th ieee data driven control and learning systems conference, ddcls 2021

作者： Long, Xinyu Sun, Mingwei Piao, Minnan Liu, Shengfei Chen, Zengqiang College of Artificial Intelligence Nankai University Tianjin300350 China

ISBN: (纸本)9781665424233

In order to attenuate the influence of the uncertainties of high altitude parafoil and environment on trajectory tracking control, active disturbance rejection control (ADRC) is used to regulate the trajectory of the high-altitude wind power parafoil. Linear extended state observer (LESO) is designed to estimate and compensate for nonlinear disturbances of the system. the simulation results show that this method has good control precision and fast-tracking velocity. © 2021 ieee.

关键词： Disturbance rejection

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On optimal scheduling of semiconductor manufacturing system based on characteristic Petri net model 10

On optimal scheduling of semiconductor manufacturing system ...

引用

10th ieee data driven control and learning systems conference, ddcls 2021

作者： Guo-Jun, S.U. TianJin Keylaboratory of Information SensingIntelligent Control Tianjin University of Technology and Education Tianjin300222 China

ISBN: (纸本)9781665424233

the production process of semiconductor manufacturing system is very complex. To establish a reliable scheduling model of semiconductor manufacturing system is very important for testing the whole production line system, evaluating different scheduling schemes, and optimizing production resource scheduling. In this paper, a model of semiconductor manufacturing system based on characteristic Petri net is proposed. By simplifying the non main characteristic elements of Petri net model of semiconductor manufacturing system, the model can effectively overcome the danger of model scale expansion brought by basic Petri net in the case of complex system. Finally, the modeling of semiconductor manufacturing system and its calculation example are realized by the above modeling method. © 2021 ieee.

关键词： Petri nets

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learning-based Event-triggered Adaptive Optimal Output Regulation of Linear Discrete-time systems 10

Learning-based Event-triggered Adaptive Optimal Output Regul...

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10th ieee data driven control and learning systems conference, ddcls 2021

作者： Zhao, Fuyu Gao, Weinan Liu, Tengfei Jiang, Zhong-Ping State Key Laboratory of Synthetical Automation for Process Industries Northeastern University Shenyang110819 China College of Engineering and Science Florida Institute of Technology Department of Mechanical and Civil Engineering MelbourneFL32901 United States New York University Six MetroTech Center Department of Electrical and Computer Engineering BrooklynNY11201 United States

ISBN: (纸本)9781665424233

In this paper, a data-driven event-triggered output-feedback control approach is proposed to solve the problem of adaptive optimal output regulation for uncertain discrete-time linear systems when only the output information is available. A crucial strategy is to develop a novel co-design scheme for the event-triggering mechanism and the data-driven optimal controller. theoretical analysis and an application to a LCL coupled inverter-based distributed generation system demonstrate the effectiveness of the proposed learning-based, event-triggered, adaptive optimal controller design with output-feedback. © 2021 ieee.

关键词： Digital control systems

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A Heavy-tailed Distribution data Generation Method Based on Generative Adversarial Network 10

A Heavy-tailed Distribution Data Generation Method Based on ...

引用

10th ieee data driven control and learning systems conference, ddcls 2021

作者： Zhang, Xiaoyu Zhou, Jinglin College of Information Science Technology Beijing University of Chemical Technology Beijing100029 China

ISBN: (纸本)9781665424233

Heavy-tailed distribution widely exists in economic, financial, industrial and other data. the tail of heavy-tailed distribution is thicker than that of Gaussian distribution. Generative adversarial networks (GAN) is a data generation model, which is mainly used for image generation. the traditional GANs method usually assumes that the samples follow Gaussian distribution, so it is often not used directly in the heavy-tailed distribution data. In this paper, a generative adversarial network HTGAN for industrial heavy-tailed data is proposed. By improving the method of data preprocessing, the distribution of hidden variable Z is changed to T distribution. this method is applied to the fault data of TE process data. Compared with the traditional GAN method, the superiority of this method in industrial heavy tail data generation is proved. © 2021 ieee.

关键词： Generative adversarial networks

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Adaptive Iterative learning control for Hybrid Parameterized System with Input Saturation and Time-Delay 10

Adaptive Iterative Learning Control for Hybrid Parameterized...

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10th ieee data driven control and learning systems conference, ddcls 2021

作者： Wang, Yanjie Zhang, Ruikun School of Mathematics and Physics Qingdao University of Science and Technology Qingdao266061 China

ISBN: (纸本)9781665424233

In this paper, an adaptive iterative learning control (AILC) method is proposed for a class of nonlinear hybrid parameterized systems with input saturation term and state delay term. the hybrid parameterized system contains the time-invariant and time-varying parameters simultaneously. According to the properties of input saturation and state delay in the SISO nonlinear hybrid parameterized systems, an AILC is designed to address the nonlinear problems caused by input saturation term and state delay term. Meanwhile, a Lyapunov-Krasovskii type composite energy function (CEF) is constructed to prove the convergence property of system tracking error. Finally, the correctness and effectiveness of the proposed control strategy is verified by a numerical simulation. © 2021 ieee.

关键词： Time delay

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Design of Nonlinear Iterative learning control Based on Deep Reinforcement learning Algorithm 10

Design of Nonlinear Iterative Learning Control Based on Deep...

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10th ieee data driven control and learning systems conference, ddcls 2021

作者： Shi, Jia Wen, Kechao Xu, Xinghai Hu, Xiongzhe Luo, Dijun School of Chemistry and Chemical Engineering Xiamen University Department of Chemical and Biochemical Engineering Xiamen Fujian361005 China TencentAILab Guangdong Shenzhen China

ISBN: (纸本)9781665424233

Iterative learning control (ILC) is an advanced control method which has been studied and widely used in periodical, repetitive or batch processes. However, there is still a lack of an effective method for the design of nonlinear iterative learning control (NILC). In view of the excellent performance of deep reinforcement learning (DRL) in dealing with the decision-making problems for the complex dynamical processes, in this paper, we propose an intelligent design method for NILC system by using deep deterministic policy gradient (DDPG), a typical DRL algorithm. By properly designing the state information and the instant reward, the design algorithm can gradually realize the optimal NILC law through the interaction without any requirement of prior knowledge of the processes. the numerical simulation based on a nonlinear model illustrates the effectiveness and applicability of the proposed design method. © 2021 ieee.

关键词： Deep neural networks

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