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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251-260 订阅

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Error-driven Tracking control Design with Preset-input LMS Adaptive Filter 11

Error-driven Tracking Control Design with Preset-input LMS A...

引用

11th ieee data driven control and learning systems conference, ddcls 2022

作者： Liu, Qingquan Huo, Xin Liu, Kangzhi Zhao, Hui Wu, Aijing Control and Simulation Center Harbin Institute of Technology Harbin150080 China Chiba University Department of Electrical and Electronic Engineering Chiba263-8522 Japan

ISBN: (数字)9781665496759

ISBN: (纸本)9781665496759

there is a problem of insufficient reference tracking accuracy in servo system driven by permanent magnet synchronous motor (PMSM), which limits its application. this paper aims to propose a modified error-driven preset-input least mean square adaptive filter (EPLMS-AF) to improve the tracking accuracy. the reference tracking error is used to drive the update of adaptive weights in EPLMS-AF. the designed preset-inputs makes the reference tracking error converge to zero according to the form of reference. On the premise of choosing an appropriate iteration size to ensure the stability of the filter, the tracking error is able to reduce to zero along the gradient direction. At last, numerical simulations are provided to illustrate the high efficiency and wide applicability of the proposed method. © 2022 ieee.

关键词： Errors

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Robust State Estimation for Power systems Using Quasi-Newton Method 10

Robust State Estimation for Power Systems Using Quasi-Newton...

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

作者： Bai, Yu Li, Wenling Li, Xiaoming Beijing100191 China Research Institute for Smart Cities School of Architecture and Urban Planning Shenzhen University Guangdong Key Laboratory of Urban Informatics Shenzhen Key Laboratory of Spatial Smart Sensing and Services Shenzhen518060 China

ISBN: (纸本)9781665424233

this article addresses the state estimation problem in power system under non-Gaussian noise environment. this estimator is designed based on maximum correntropy criterion (MCC), which exhibits the robustness with reference to non-Gaussian noise. To avoid computing the inverse matrix of Hessian matrix, the quasi-Newton Broyden-Fletcher-Goldfarb-Shanno (BFGS) method is adopted to propose a centralized power system state estimation algorithm. For the reason of security, privacy and further reducing the computation burden, adapt-then-combine (ATC) strategy is applied to design a distributed state estimation algorithm based on the centralized algorithm. the effectiveness of the proposed method is verified by the numerical results of ieee 14-bus and ieee 118-bus systems under uniformly distributed noise, t-distribution noise and impulsive noise. © 2021 ieee.

关键词： State estimation

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Inverse-Free Tracking control of Continuum Robots with Unknown Models Based on Gradient Neural Networks 11

Inverse-Free Tracking Control of Continuum Robots with Unkno...

引用

11th ieee data driven control and learning systems conference, ddcls 2022

作者： Yu, Peng Tan, Ning Zhang, Mao Ni, Fenglei School of Computer Science and Engineering Sun Yat-sen University Guangzhou510006 China Harbin150001 China

ISBN: (数字)9781665496759

ISBN: (纸本)9781665496759

the inherent dexterity and compliance of continuum robots have endeared them to numerous researchers. However, the control of continuum robots remains a complicated problem worth studying as a result of their intricate structures. In this work, we present a scheme based on gradient neural networks (GNN) for the control of continuum robots with unknown models. the proposed scheme is composed of two GNN models, one of which is employed for the solution of inverse kinematics problem, and the other is used to estimate the Jacobian matrix of continuum robots. this design allows us to rely only on user-defined input and sensory output to achieve the tracking control of continuum robots, without knowing their models and internal structures. the convergence of the proposed scheme is proven by theoretical analysis. Finally, the feasibility and merits of the proposed scheme are revealed by simulation studies, including performance analysis and comparisons. © 2022 ieee.

关键词： Jacobian matrices

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Event-triggered Secure Group Consensus of Second-order Multi-agent systems under Periodic DoS Attacks 10

Event-triggered Secure Group Consensus of Second-order Multi...

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

作者： Liu, Peiming Huang, Zhizong Guo, Xianggui School of Beijing Engineering Research Center of Industrial Spectrum Imaging School of Automation and Electrical Engineering University of Science and Technology Beijing Beijing100083 China Shunde Graduate School of University of Science and Technology Beijing Foshan528000 China Lanzhou Fumei Electronic Technology Co. Ltd Lanzhou730030 China

ISBN: (纸本)9781665424233

In this paper, an event-triggered group consensus pinning control strategy is proposed for the second-order nonlinear multi-agent systems (MASs) with directed communication graph under periodic denial-of-service (DoS) attacks, which does not require the MASs to satisfy the in-degree balance condition. On this basis, the state error systems under periodic DoS attacks are established. In addition, it should be pointed out that the control strategy includes the selection method of pinning nodes under the group consensus framework. Furthermore, the sampled-data-based event-triggered mechanism (ETM) reduces the excessive consumption of system resources. Finally, simulation examples are given to verify the effectiveness of the control strategy under periodic DoS attacks with different duration. © 2021 ieee.

关键词： Multi agent systems

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Novel Multimode Process Soft Sensing Methods Based on the Dynamic Mixture Variational Autoencoder Regression Model 11

Novel Multimode Process Soft Sensing Methods Based on the Dy...

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11th ieee data driven control and learning systems conference, ddcls 2022

作者： Cui, Linlin Yao, Le Ge, Zhiqiang Song, Zhihuan Institute of Industrial Process Control College of Control Science and Engineering Zhejiang University State Key Laboratory of Industrial Control Technology Hangzhou310027 China

ISBN: (数字)9781665496759

ISBN: (纸本)9781665496759

Modern industrial processes with increasing complexity not only contain nonlinear and multi-mode characteristics, but also are commonly the dynamic processes, which brought challenging problems to soft sensor modeling. In order to solve these problems, a dynamic mixture variational autoencoder regression (DMVAER) model is proposed for the nonlinear multi-mode modeling, which is suitable for industrial process quality prediction with multiple complex process characteristics. Furthermore, in order to deal with the problem of semi-supervised data with a large number of unlabeled samples, a semi-supervised dynamic mixture variational autoencoder regression (ssDMVAER) model is proposed, and the corresponding semi-supervised data sequence division method is adopted to make full use of the information in both labeled data and unlabeled data. Finally, in order to verify the feasibility and effectiveness of the proposed methods, the two models are applied to an actual industrial process of methanation furnace. the results show that the proposed methods have superior soft sensing performance than existing methods. © 2022 ieee.

关键词： Mixtures

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Mitigating Online Microgrid controller Deployment Issues with learning Techniques: An Approximate MPC Approach 10

Mitigating Online Microgrid Controller Deployment Issues wit...

引用

10th International conference on Optimization and Applications, ICOA 2024

作者： Taïeb, Zinédinc Sandou, Guillaume Vasset, Nicolas Gaoua, Yacine Pichenot, Grégoire CentraleSupélec Liten Cea University of Paris-Saclay L2S Grenoble Gif-sur-Yvette France CentraleSupélec University of Paris-Saclay L2S Gif-sur-Yvette France Liten Cea University of Grenoble Alpes Grenoble France

ISBN: (纸本)9798350387353

Due to the increasing complexity of energy systems, standard rule-based controllers may become inadequate for Energy Management System (EMS) supervision. Advanced optimal techniques as Model Predictive control (MPC) can be too intensive in computational resources, especially for small agents with low-performance environment. therefore, control issues about reducing implementation complexity gains interest in literature. In particular, surrogate models integrating learning techniques come within this scope by mitigating online implementation issues with offline data-driven approximations. However, there is a lack of works dealing with the analysis of performance robustness of such approximate controllers, when any uncertainties occur in online control. In this paper, we propose a freestanding approximate explicit MPC method, mobilizing learning techniques that approximate an analytical control law derived from a formal multiparametric programming. We propose to characterize the performance of the so-called reduced controller on simulation of a PV-storage microgrid, with historical data and forecast mismatch. We found numerically that the reduced controller has very similar performances compared to the baseline MPC when the forecast error is approximatively limited to 20%, and globally presents a relative change of economical loss less than 4% in average on simulation tests. © 2024 ieee.

关键词： Predictive control systems

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Adaptive Sliding Mode Trajectory Tracking control of Automated Guided Vehicles with Sideslip Angle 10

Adaptive Sliding Mode Trajectory Tracking Control of Automat...

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

作者： Gao, Jian Wang, Heng Quan, Wei Li, Qing Wang, Xuanzhi School of Automation and Electrical Engineering Key Laboratory of Knowledge Automation for Industrial Processes University of Science and Technology Beijing Ministry of Education Beijing100083 China Shunde Graduate School of University of Science and Technology Beijing Foshan Guangdong 528300 China

ISBN: (纸本)9781665424233

this paper studies the problem of trajectory tracking control for automated guided vehicles (AGVs). A novel control framework is proposed to deal with the nonlinear nonholonomic constrained systems with uncertainties. Firstly, an adaptive backstepping control law based on the AGV trajectory tracking model is designed to estimate sideslip angle online, which guarantees the kinematics system stability. Secondly, torque control inputs are designed by sliding mode strategy such that the linear velocity and angular velocity follow desired values accurately. the stability analysis shows that trajectory tracking errors are convergent and bounded. Finally, a simulation example is presented which demonstrates the effectiveness of the proposed approach. © 2021 ieee.

关键词： Sliding mode control

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LiNet: A Neural Network with data Augmentation for Liquor Quality Classification 10

LiNet: A Neural Network with Data Augmentation for Liquor Qu...

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

作者： Tang, Hao Tian, Kexin Zhu, Haijiang College of Information Science and Technology Beijing University of Chemical Technology Beijing100020 China College of Letters and Science University of Wisconsin-Madison Wisconsin53706 United States

ISBN: (纸本)9781665424233

In recent years, Chinese Liquor is widely loved by consumers in the world. therefore, the quality of Chinese Liquor has also attracted much attention. In order to establish a fast and accurate method for identifying the quality of liquor, this paper proposes LiNet which is a novel neural network to classify different quality liquors. For the purpose of extracting the characteristics of different quality liquors, we used Ion Mobility Spectroscopy (IMS) to analyze different quality liquors and constructed a feature vector based on the ion mobility spectrum signal of liquor. Due to industrial production reasons, the dataset is small, LiNet leverages the Generative Adversarial Networks to enhance the dataset. Considering that IMS data is time series data and has temporal and spatial correlation, LiNet uses 1-D CNN and 1-D transposed CNN to implement the Stacked Auto Encoder to learn the feature of IMS data of Chinese Liquor. Experimental results show that our proposed model achieves premium performance to other state-of-the-art approaches. © 2021 ieee.

关键词： Generative adversarial networks

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Building Health State Recognition Method Based on Multi-channel Convolution Neural Network Fusion 10

Building Health State Recognition Method Based on Multi-chan...

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

作者： Su, Tingli Li, Jian Yang, Aiqiang Jin, Xuebo Kong, Jianlei Bai, Yuting School of Artificial Intelligence Beijing Technology and Business University 100048 China China Light Industry Key Laboratory of Industrial Internet and Big Data Beijing Technology and Business University 100048 China

ISBN: (纸本)9781665424233

the identification of the health status of buildings has been paid more and more attention by all sectors of the society. the early warning of catastrophes or the assessment of the damage degree and residual life of building structures after catastrophes has become a hot topic for scholars from all over the world. In order to improve the performance of building health state recognition, a novel framework based on multi-channel convolution neural network fusion is proposed in this paper. By combining the output results of different convolution neural networks, temporal information and spatial information are used to achieve the accurate classification of building health status. Eventually, with the data collected by the sensor during the earthquake, the proposed framework is proved to be effective and superior. © 2021 ieee.

关键词： Buildings

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Distributed Cooperative control of High-Speed Trains With Input Saturation and Unmodeled Dynamics 11

Distributed Cooperative Control of High-Speed Trains With In...

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11th ieee data driven control and learning systems conference, ddcls 2022

作者： Zhu, Lei Huang, Deqing Li, Xuefang School of Electrical Engineering Southwest Jiaotong University Chengdu610031 China School of Intelligent Systems Engineering Sun Yat-Sen University Guangzhou510275 China

ISBN: (数字)9781665496759

ISBN: (纸本)9781665496759

In this work, a distributed cooperative control strategy is proposed for multiple high-speed trains (MHSTs) subject to input saturation and unmodeled dynamics. To facilitate the distributed controller design, the dynamics of trains are firstly modeled as a muti-agent system (MAS) with a state-dependent directed graph. then, the distributed control laws that are equipped with a command filter and a robust adaptive neural network are developed to achieve the consensus task among the MHSTs with predefined displacement and speed trajectories. It should be highlighted that, to reduce the complexity of the control algorithms and computational burden, we propose adaptive estimation laws to estimate the upper bound of the norm of the neural network weight vectors, instead of the weights themselves. Further, an auxiliary dynamical system (ADS) is introduced to compensate the influence of the input saturation. the convergence of the proposed controllers are analyzed rigorously by applying Lyapunov theorem, and the effectiveness of the proposed control approach is demonstrated by numerical simulations. © 2022 ieee.

关键词： Multi agent systems

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