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检索条件"任意字段=13th IEEE Data Driven Control and Learning Systems Conference, DDCLS 2024"

共 716 条记录，以下是421-430 订阅

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Disturbance Rejection for LCL-Type Inverter System Based on Equivalent Input Disturbance and Generalized State Observer Method

Disturbance Rejection for LCL-Type Inverter System Based on ...

引用

data driven control and learning systems (ddcls)

作者： Jiahe Ren Mengling Ma Ancai Zhang Guangyuan Pan Xinghui Zhang School of Automation and Electrical Engineering Linyi University Linyi China

ISBN: (数字)9798350361674

ISBN: (纸本)9798350361681

the inverter systems have been widely used in photovoltaic microgrids. they play an important role in ensuring the stable operation of the entire power grid system. this paper discusses the disturbance rejection problem for the LCL-type inverter system with an unknown disturbance. Based on the idea of equivalent input disturbance (EID) and generalized state observer (GSO), a disturbance rejection control method is developed. this method firstly defines an EID for the unknown disturbance and designs a GSO for the EID-based system. And then, a disturbance estimator is constructed to estimate the EID. After that, a feedback controller is designed by using the internal model principle and the quadratic optimal control theory. It guarantees the robust tracking control objective of the LCL-type inverter system to be realized. Simulation and comparison results demonstrate the validity of our developed control strategy.

关键词： Photovoltaic systems learning systems Optimal control Microgrids Observers Harmonic analysis Inverters

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A Bidirectional Attention-Based LSTM Model for Multi-Step Ahead Prediction in Dynamic Soft Sensors

A Bidirectional Attention-Based LSTM Model for Multi-Step Ah...

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data driven control and learning systems (ddcls)

作者： Yi Huang Longyan Wang Lingjian Ye Zeyu Yang Huzhou Key Laboratory of Intelligent Sensing and Optimal Control for Industrial Systems School of Engineering Huzhou University Huzhou China

ISBN: (数字)9798350361674

ISBN: (纸本)9798350361681

In the field of dynamic soft sensor developments, multi-step ahead prediction of key quality variables is becoming increasingly important, which can provide a long-term data changing trends in the future time period. However, multi-step ahead prediction is currently facing various issues such as data uncertainty and unstable forecasts. In order to improve these issues, this paper proposes a bidirectional spatiotemporal attention-based LSTM network model (Bi-STA-LSTM), in which spatiotemporal attention extracts the relevant features from historical time series, and the bidirectional structure captures more historical and future trends. It can continue to provide useful information when the prediction step increases. By comparing different methods on the debutanizer dataset, the superiority of the Bi-STA-LSTM is verified.

关键词： learning systems Uncertainty Soft sensors Fitting Time series analysis Predictive models Feature extraction

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Localization Strategy Based on Escape Sign in Low Feature Information Indoor Environment

Localization Strategy Based on Escape Sign in Low Feature In...

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data driven control and learning systems (ddcls)

作者： Yuefeng Mao Jianming Zhang Jituo Li Academy of Engineering Zhejiang University Hangzhou College of control science and engineering Zhejiang University Hangzhou School of Mechanical Engineering Zhejiang University Hangzhou

ISBN: (数字)9798350361674

ISBN: (纸本)9798350361681

Aiming at the localization problem of robots in indoor environment with less characteristic information (such as long corridors and foyers), a localization system based on natural vision labels is designed to achieve high precision real-time positioning. this method innovatively selects escape markers as positioning tags, improves the existing feature matching algorithm and propose a feature point filtrate algorithm to solve the problem of similar feature interference. Based on the recognition and matching of few visual labels, combined with the EKF algorithm, the real-time positioning of the robot can be realized Experimental tests are carried out under various environmental conditions, the error level is analyzed, and the effectiveness of the positioning system is proved.

关键词： Location awareness Visualization Service robots Navigation Real-time systems Robustness Path planning

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Iterative learning control with High-Order Internal Model for Singular Multiagent systems

Iterative Learning Control with High-Order Internal Model fo...

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data driven control and learning systems (ddcls)

作者： Gang He Meiyu Zhang Senping Tian Xiangyang Li Guang Zhou Institute of Modern Industrial Technology South China University of Technology Guangzhou China School of Automation Science and Engineering South China University of Technology Guangzhou China

ISBN: (数字)9798350361674

ISBN: (纸本)9798350361681

In this paper, the problem of iterative learning consensus control is studied for singular multi agent systems with the iteration-varying desired trajectories described by a high-order internal model (HOIM). Utilizing the equivalent constraint decomposition form of the singular system, we generalize and apply the P-type ILC algorithm based on the Higher Order Iterative Methods (HOIM) to achieve consensus control of the singular multi agent systems with iteration-varying desired trajectories. Additionally, we provide the corresponding convergence conditions. theoretical analyses show that the output of the singular multi agent systems under the action of the algorithm can achieve complete consensus tracking for the iteration-varying desired trajectories over the whole time interval. Finally, a numerical example was used to verify the correctness of the main results and the effectiveness of the methodology.

关键词： learning systems Consensus control control systems Numerical simulation data models Trajectory Numerical models

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

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

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data driven control and learning systems (ddcls)

作者： Jinrui Ren Jiaxi Du Yu Che Quan Quan School of Automation Xi'an University of Posts & Telecommunications Xi'an P. R. China Xi'an Microelectronics Technology Institute Xi'an P. R. China School of Automation Science and Electrical Engineering Beihang University Beijing P. R. China

ISBN: (数字)9798350361674

ISBN: (纸本)9798350361681

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.

关键词： Linear systems learning systems State feedback Additives Delay effects control systems Robustness

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Robust Stability and Stabilization of Switched Parametric Uncertain Linear systems with Infinite Subsystems

Robust Stability and Stabilization of Switched Parametric Un...

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data driven control and learning systems (ddcls)

作者： Jing Zhang Kaili Liang Liying Zhu School of Information and Automation Qilu University of Technology (Shandong Academy of Sciences) Jinan P.R. China

ISBN: (数字)9798350361674

ISBN: (纸本)9798350361681

this paper investigates the robust stability issues of switched parametric uncertain linear systems with infinite subsystems and their constrained dwell times. Firstly, a switching Lyapunov function is obtained by solving a family of differential Lyapunov equations and algebraic Lyapunov equations. Secondly, based on the obtained switching Lyapunov function, this paper proposes some sufficient conditions for robust globally asymptotic stability of such kind of switched systems. thirdly, by means of the proposed robust stability results, robust globally asymptotically stabilized state feedback controllers are designed for switched parameter uncertain linear systems with infinite subsystems. Finally, an illustrative example and its numerical simulations demonstrate the effectiveness of the new results given in this paper.

关键词： Linear systems Switched systems Asymptotic stability Sufficient conditions State feedback Robust stability Switches

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Tracking control of Autonomous Underwater Vehicle Based on Sliding Mode Adaptive Optimal Feedback

Tracking Control of Autonomous Underwater Vehicle Based on S...

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data driven control and learning systems (ddcls)

作者： Baixue Miao Chenle Lv Yongfeng Lv College of Electrical and Power Engineering Taiyuan University of Technology Taiyuan China

ISBN: (数字)9798350361674

ISBN: (纸本)9798350361681

To improve the tracking control of autonomous underwater vehicles, an adaptive sliding mode optimal tracking control method for linear continuous systems is proposed by combining adaptive control and sliding mode technology. the vertical state equation of an autonomous underwater vehicle is simplified according to the actual situation. Firstly, the linear sliding mode surface is designed and the state optimal feedback controller is established. then, considering the performance index function and linear quadratic value function that meet the system requirements, the Riccati equation is linearly parameterized, and an online learning algorithm driven by parameter estimation error is introduced to approximate the optimal solution of the equation. Finally, the stability of the closed-loop system and the convergence of the error are proved by Lyapunov theory. Simulation results demonstrate that the proposed method effectively achieves depth tracking control with dynamic response, high tracking accuracy, and robustness.

关键词： Autonomous underwater vehicles Technological innovation Accuracy Heuristic algorithms Simulation Mathematical models Trajectory

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data-driven Anomaly Monitoring for Nonstationary Industrial Process

Data-Driven Anomaly Monitoring for Nonstationary Industrial ...

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data driven control and learning systems (ddcls)

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

ISBN: (数字)9798350361674

ISBN: (纸本)9798350361681

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.

关键词： Employee welfare learning systems Process control Mixture models Benchmark testing Feature extraction data mining

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Enhanced DDoS Defense in SDN: Double-Layered Strategy with Blockchain Integration 13

Enhanced DDoS Defense in SDN: Double-Layered Strategy with B...

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13th ieee International conference on Communications, Circuits and systems (ICCCAS)

作者： Tian, Jialin Shu, Zhaogang Chen, Shuwu Xie, Haihui Liu, Xiaolong Qiu, Caiyu Fujian Agr & Forestry Univ Comp & Informat Coll Fuzhou Peoples R China

ISBN: (纸本)9798350386288;9798350386271

With the development of technologies such as cloud computing, big data, and the Internet of things (IoT), Software-Defined Networking (SDN) has emerged as a novel network architecture in today's Internet era. It can separate the control plane from the data plane, allowing rapid packet forwarding in the Internet through a centralized controller. However, SDN environments are vulnerable to traditional Distributed Denial of Service (DDoS) attacks. this paper proposes a new dual layer strategy to try to mitigate the question. First, by using blockchain technology and smart contract in the northbound interface to store the flow tables required for SDN networks, security is increased. then, we use the Token Bucket algorithm and Time Window algorithm to build the first-tier strategy to defend against obvious DDoS attacks. To detect unobvious DDoS attacks, we design the second-tier strategy that uses a composite data feature correlation coefficient calculation method and the Isolation Forest algorithm to perform binary classification on data, thereby identifying abnormal traffic. We use the currently publicly available DDoS dataset CIC-DDoS2019 for experimental verification. the results show that using this strategy in SDN networks results in an average deviation of data Round-Rip Time (RTT) approximately 38.86% lower than in the original SDN networks without this strategy. Additionally, the accuracy of DDoS attack identification reaches 91.29%. this means that with the implementation of this strategy, DDoS attacks can be effectively identified without compromising the stability of data transmission in SDN network environments.

关键词： Software Defined Networks Distributed Denial of Service Blockchain Machine learning

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Cloud-Edge-End Collaboration for the Finishing Rolling System: An Accelerated data-driven Cooperative Distributed Predictive control Approach

Cloud-Edge-End Collaboration for the Finishing Rolling Syste...

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data driven control and learning systems (ddcls)

作者： Zhichen He Dingguo Liang Ying Yang Department of Mechanics and Engineering Science State Key Lab for Turbulence and Complex Systems College of Engineering Peking University Beijing P. R. China

ISBN: (数字)9798350361674

ISBN: (纸本)9798350361681

In this brief, an accelerated data-driven cooperative distributed predictive control approach with the cloud-edge-end collaboration implemented is proposed for the finishing rolling system(FRS) of the hot strip mill process. Specifically, in the end device, the operating data of the rolling mill stands and looper systems are collected and uploaded to the cloud server, which contributes to satisfying the interconnected information communication requirements for optimization problem-solving. then, the control optimization problem is quickly solved in the cloud server, where the cooperative distributed solving method is applied, and an accelerated iteration strategy is adopted. Moreover, a regularized optimization technique is used to suppress the interference of disturbance and noise. thus, the edge device can set the input of the bottom-level control to realize the control of the finishing rolling system with the optimal control sequences sent from the cloud server. Finally, simulations on a real finishing rolling system are conducted to illustrate the effectiveness of the proposed method.

关键词： Strips Noise Collaboration Predictive models Real-time systems Servers Problem-solving

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