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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261-270 订阅

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A Cascade Active Disturbance Rejection control Method Based on SPSA Online Optimization for SCR Denitration System 13

A Cascade Active Disturbance Rejection Control Method Based ...

引用

13th ieee data driven control and learning systems conference, ddcls 2024

作者： Ai, Wei Wang, Yang Zhang, Tong Li, Xiangyang College of Automation Science and Technology South China University of Technology Guangzhou510640 China

ISBN: (纸本)9798350361674

Due to the characteristics of large inertia, significant lag, and model uncertainty in the Selective Catalytic Reduction (SCR) denitrification system, traditional control schemes encounter challenges in achieving precise control. To tackle this issue, this paper proposes an IMC cascade self-adaptive disturbance rejection controller based on SPSA online parameter optimization and applies it to the SCR denitrification control system. this approach effectively addresses the challenges of precise setpoint tracking, disturbance suppression due to model variations, and difficulties in controlling significant lag. the introduction of the SPSA algorithm allows the controller to update optimal parameters in real-time as the operating conditions change over time. Experimental results from setpoint tracking and disturbance rejection demonstrate that the proposed approach outperforms traditional PID control and linear self-adaptive disturbance rejection control. © 2024 ieee.

关键词： Disturbance rejection

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Bipartite Consensus Problems of Heterogeneous Multi-Agent systems with Cooperative and Antagonistic Interactions 13

Bipartite Consensus Problems of Heterogeneous Multi-Agent Sy...

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13th ieee data driven control and learning systems conference, ddcls 2024

作者： Zhang, Yi Li, Jinchao Du, Mingjun Pang, Shaopeng Ji, Peng Shandong Province Jinan250353 China

ISBN: (纸本)9798350361674

this article explores the distributed control issue within a heterogeneous signed multi-agent system, comprising both first-order and second-order integrator agents. It devises consensus protocols for an undirected communication topology within structurally balanced networks. through the application of graph theory and Lyapunov methods, it establishes the necessary conditions for achieving consensus in networks characterized by both cooperative and competitive interactions. the theoretical results are corroborated by simulation experiments. © 2024 ieee.

关键词： bipartite consensus heterogeneous signed multi-agent system Structural balance

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Affine Ultra-Local Based Model-Free Predictive control on PMSM Drives with ϵ varepsilon-Approximation State Compensation 13

Affine Ultra-Local Based Model-Free Predictive Control on PM...

引用

13th ieee data driven control and learning systems conference, ddcls 2024

作者： Wei, Yao Ma, Weiyuan Xie, Haotian Ke, Dongliang Wang, Fengxiang Quanzhou Institute of Equipment Manufacturing Haixi Institutes Chinese Academy of Sciences Jinjiang362200 China College of Mechanical and Electronic Engineering Fujian Agriculture and Forestry University Fuzhou350002 China

ISBN: (纸本)9798350361674

the accuracy of a data-driven model directly affects the control performance of model-free predictive control (MFPC). To take into account the affine motion of the plant, a model-free predictive current control (MF-PCC) strategy is proposed in this paper and applied to a permanent magnet synchronous motor (PMSM) driving system. To enhance model accuracy, we adopt an affine ultra-local model, which is designed to accurately represent the system. Online estimation of all affine operators is performed using the recursive least squares (RLS) algorithm to ensure a robust fit. To improve control performance, we integrate a state compensation technique with the model. the compensation gain is selected using the ϵ varepsilon-approximation approach to meet terminal constraints. the stability of the proposed method is theoretically analyzed, and its effectiveness is verified through experimental results. the proposed method offers improved model accuracy and current quality, along with enhanced robustness compared to conventional methods. © 2024 ieee.

关键词： Predictive control systems

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Model-Free Adaptive Sliding Mode control for Discrete-Time Nonlinear systems With Sensor Fault and Prescribed Performance 11

Model-Free Adaptive Sliding Mode Control for Discrete-Time N...

引用

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

作者： Hao, Li-Ying Yang, Sen Liu, Dong College of Marine Electrical Engineering Dalian Maritime University Dalian China School of Automation Shenyang Aerospace Universtiy Shenyang China

ISBN: (数字)9781665496759

ISBN: (纸本)9781665496759

this paper concentrates on the issue of a model-free adaptive sliding mode control approach for a family of discrete-time single-input and single-output (SISO) nonlinear systems with sensor fault and prescribed performance. First of all, we set out to build an approximator that is based on radial basis function neural network to estimate the sensor fault offline, and for the purpose of restricting the tracking error in a prescribed region when the sensor fault occurs, the prescribed performance control (PPC) method is adopted. then, we consider sliding mode control strategy to achieve the stability and ensure the convergence of tracking error within PPC scheme. We have to point out that all of these works above are carried in model-free adaptive control (MFAC) framework. Finally, a simulation example result is given to validate the effectiveness of the algorithm proposed in this article. © 2022 ieee.

关键词： Sliding mode control

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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...

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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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data-driven Anomaly Monitoring for Nonstationary Industrial Process 13

Data-Driven Anomaly Monitoring for Nonstationary Industrial ...

引用

13th ieee data driven control and learning systems conference, ddcls 2024

作者： Zhang, Xue Zhang, Lu College of Electrical Engineering and Automation Shandong University of Science and Technology Qingdao266590 China

ISBN: (纸本)9798350361674

With the changes of set points, external environment, and other factors, industrial processes are always operating under multiple conditions. the variation of the operating conditions easily causes the nonstationary characteristics, which is not conductive to the anomaly monitoring. To solve this problem, data-driven anomaly monitoring method, with the aid of kernel slow feature analysis and Gaussian mixture model, is proposed in this paper. Firstly, a data reorganization strategy is used to identify the changing operating conditions according to the statistical characteristics. then the static and dynamic data slices and segments can be divided. Secondly, kernel slow feature analysis is introduced to extract the features of these nonlinear data segments. then both slow and fast features can be obtained. thirdly, Gaussian mixture model is presented to approximate the non-Gaussian distribution of the extracted slow and fast features. then the evaluating index of these features, based on the Mahalanobis distance, is utilized to realize the anomaly monitoring. Finally, Tennessee-Eastman benchmark data are applied to evaluate the effectiveness of the proposed anomaly monitoring method. the results demonstrate that the proposed method can accurately monitor the anomalies under nonstationary operating process. © 2024 ieee.

关键词： Gaussian distribution

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Distributed control Problems of Second-Order Directed Signed Networks Based on Sampled data 13

Distributed Control Problems of Second-Order Directed Signed...

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13th ieee data driven control and learning systems conference, ddcls 2024

作者： Lil, Jinchao Zhang, Yi Du, Mingjun Pang, Shaopeng Ji, Peng Shandong Province Jinan250353 China

ISBN: (纸本)9798350361674

In this study, the bipartite consensus problem of directed signed network based on sampled data is studied. A control protocol for second-order signed network is proposed, which is based on sampling data. By leveraging principles of the Laplacian matrix and graph theory, the protocol is derived, and the necessary and sufficient conditions for achieving bipartite consensus and state stability are identified. the research findings suggest that bipartite consensus can be reached in structurally balanced network topologies with the correct choice of sampling period and parameters, whereas state stability is achievable in structurally unbalanced configurations. In addition, simulation examples are used to verify the accuracy of the derived results. © 2024 ieee.

关键词： bipartite consensus Sampled data signed network structural balance

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Bearing Fault Diagnosis Based on Multiple Feature Transfer learning Network 11

Bearing Fault Diagnosis Based on Multiple Feature Transfer L...

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

作者： Qiao, Huanzhang Liu, Lida Tian, Xincheng Liu, Yan Center for Robotics School of Control Science and Engineering Shandong University Jinan250061 China Engineering Research Center of Intelligent Unmanned System Ministry of Education Jinan250061 China Shandong Raone Technology Co. LTD Jinan250012 China

ISBN: (数字)9781665496759

ISBN: (纸本)9781665496759

the intelligent fault diagnosis methods have gained great success, they usually depend on large amounts of labeled data. Unfortunately, it is a realistic problem to collected massive labeled data in actual industrial scene. In addition, even labeled data is easy to collect, the methods trained by these labeled data may not be able to classify unlabeled data obtained under different working states. Inspired by transfer learning, in the paper we propose a deep learning-based fault diagnosis method named multiple feature transfer learning network (MFTLN) to resolve the data distribution difference due to changed working conditions. MFTLN employee multiple extractors to get domain-invariant features and align source domain and target domain by maximum mean discrepancy (MMD) metric. We performed six bearing fault diagnosis transfer learning experiments to verify the superiority of the proposed method, the experimental results indicated that the recognition accuracy of the proposed method is obviously higher than those of the other 5 popular domain adaptive fault diagnosis methods when the target domain data is unlabeled. © 2022 ieee.

关键词： Failure analysis

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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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Robustness Performance Improvement for Input-Delay systems: An Additive-State- Decomposition- Based control Method 13

Robustness Performance Improvement for Input-Delay Systems: ...

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13th ieee data driven control and learning systems conference, ddcls 2024

作者： Ren, Jinrui Du, Jiaxi Che, Yu Quan, Quan School of Automation Xi'an University of Posts & Telecommunications Xi'an710121 China Xi'an Microelectronics Technology Institute Xi'an710000 China School of Automation Science and Electrical Engineering Beihang University Beijing100191 China

ISBN: (纸本)9798350361674

Time delays exist in a large number of practical systems, and it is meaningful to study the robust performance improvement of time-delay systems. this paper proposes an additive-state-decomposition-based robust performance improvement control method for time-delay linear systems. First, additive state decomposition is performed on the studied time-delay system. then, a state feedback controller is designed based on the decomposed primary system. In addition, the small-gain theorem is also used to prove that the additive-state-decomposition-based control method can enhance the robustness of time-delay systems compared with the traditional state feedback control method. Finally, simulations verify that the method based on additive state decomposition outperforms the traditional state feedback control method in terms of robustness performance improvement. © 2024 ieee.

关键词： Delay control systems

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