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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241-250 订阅

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data-driven control of Event-triggered Linear systems 11

Data-driven Control of Event-triggered Linear Systems

引用

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

作者： Wang, Xin Sun, Jian Wang, Gang State Key Lab of Intelligent Control and Decision of Complex Systems Beijing Institute of Technology Beijing100081 China Beijing Institute of Technology Chongqing Innovation Center Chongqing401120 China

ISBN: (数字)9781665496759

ISBN: (纸本)9781665496759

the present paper considers the data-driven control of unknown linear time-invariant discrete-time systems under an event-triggering transmission scheme. To this end, we begin by presenting a dynamic event-triggering scheme (ETS) based on periodic sampling, and a discrete-time looped-functional (DLF) approach, through which a model-based stability condition is derived. Combining the model-based condition with a recent data-based system representation, a data-driven stability criterion in the form of linear matrix inequalities (LMIs) is established, which offers a way of co-designing the ETS matrix and the controller using pre-collected noisy input-state data. Finally, numerical simulations showcase the efficacy of ETS in reducing data transmissions as well as of the proposed co-design methods. © 2022 ieee.

关键词： Stability criteria

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Feature Extraction of Sequence data Based on LSTM and its Application to Fault Diagnosis of Industrial Process 11

Feature Extraction of Sequence Data Based on LSTM and its Ap...

引用

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

作者： Yang, Xiaojun Wan, Chuan Zhang, Tongshuai Xiong, Zhihua Tsinghua University Department of Automation Beijing100084 China

ISBN: (数字)9781665496759

ISBN: (纸本)9781665496759

data with sequential relationships such as text, speech, and sensor data, belong to sequence data. Many deep learning models are proposed to extract features and learn inherent information among the sequence data. We investigate the similarities and differences of the modeling methods of sequence data in various fields, and then use deep learning neural network to build a model for the fault diagnosis of sequence data in process industry. Long-short-term memory (LSTM) network and feature extraction of contrastive predictive coding (CPC) are used in this paper for building deep learning models. CPC can learn representations of data, and LSTM can learn the information contained in sequence data. For application to fault diagnosis, the whole model combining CPC and LSTM can appropriately distinguish normal data from various fault data. Experiments on the Tennessee-Eastman process show that the LSTM model combining with proper feature extraction methods can improve the performance of fault diagnosis. © 2022 ieee.

关键词： Failure analysis

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Interpretable Diagnosis of Glaucoma Based on Attention Mechanism and Embedded Class Activation Map 12

Interpretable Diagnosis of Glaucoma Based on Attention Mecha...

引用

ieee 12th data driven control and learning systems conference (ddcls)

作者： Liu, Bao Li, Shu-qi He, Rui-long Zhao, Yu-ge Xian Univ Sci & Technol Coll Elect & Control Engn Xian 710054 Peoples R China Xian Key Lab Elect Equipment Condit Monitoring & Xian 710054 Peoples R China

ISBN: (纸本)9798350321050

Glaucoma is a group of heterogeneous neurodegenerative diseases, which has become the main cause of irreversible blindness worldwide, so the early screening of glaucoma has become crucial. Although the methods of visualizing the feature maps of convolutional layers increase the transparency of deep learning models to a certain extent, these methods often embed high-level connectivity constraints, which leads to the fact that the interpretable diagnosis of glaucoma can only obtain interpretation results from high-dimensional information. the proposed GAMNet based on attention mechanism and embedded class activation map not only solve the problem that the feature information to be concerned under multi-scale cannot be accurately focused by attention, but also release the constraints of high convolutional layer features on the interpretation results of the model from the perspective of fusion features. Firstly, a dynamic receptive field module is introduced on the basis of the multi-scale feature pyramid structure, and the embedded coordinate attention mechanism is used to effectively capture long-distance dependencies, so as to construct a semantically rich dynamic multi-scale feature pyramid for accurate and efficient diagnosis of glaucoma fundus images. Secondly, the hierarchical gradient function of gradient-weighted class activation map is established, and the class activation map after feature fusion is used to provide a more detailed attention map, which provides a decision-making basis for glaucoma diagnosis. GAMNet provides the evidence activation map, compared with Garbor, Wavelet, GRI and Superpixel in the performance of optic disc segmentation, the results show that GAMNet classification is accurate, and provides the evidence activation map. It not only shows diagnostic results, but also has good interpretation performance without high-level connectivity constraints.

关键词： Glaucoma Coordinate attention mechanism Interpretability diagnosis Class activation map

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Tracking control of Two Virtually Coupled Trains via Prescribed Performance control Method 11

Tracking Control of Two Virtually Coupled Trains via Prescri...

引用

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

作者： Jia, Yuqi Huang, Deqing Li, Xuefang Institute of System Science and Technology Southwest Jiaotong University Chengdu610031 China School of Intelligent Systems Engineering Sun Yat-Sen University Guangzhou510275 China

ISBN: (数字)9781665496759

ISBN: (纸本)9781665496759

As a new concept in railways, virtual coupling has been proven that could reduce the separation between two adjacent trains, and thus increase the railway capacity effectively. However, the shorter separation distance will lead to the stricter requirements for the tracking accuracy. the prescribed performance control method could restrict the convergence rate and the convergence range of the tracking error, thus could improve the tracking accuracy effectively. Based on this, a novel tracking control scheme for two virtually coupled trains under the framework of the prescribed performance control is developed in this paper. To deal with the system uncertainties, a fuzzy logic system is applied to approximate the unknown resistance. At last, the convergence of the proposed controller is proved by virtue of the Lyapunov stability theorem and its effectiveness is analyzed through numerical simulations. © 2022 ieee.

关键词： Fuzzy logic

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Optimal Second-Order Integral Sliding Mode control for Underactuated Robotic System 11

Optimal Second-Order Integral Sliding Mode Control for Under...

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

作者： Hu, Pan Liu, Xia Jiang, Wenbo Guo, Yi School of Electrical Engineering and Electronic Information Xihua University Chengdu610039 China

ISBN: (数字)9781665496759

ISBN: (纸本)9781665496759

An optimal second-order integral sliding mode control (OSISMC) strategy is proposed for the stabilization control of underactuated robotic system. the proposed control strategy consists of an optimal control law and a second-order integral sliding mode (SISM) control law. the optimal control law is designed for the nominal system by the state-dependent Riccati equation (SDRE), making the system reach the optimal performance. then, the optimal control law is introduced into the integral sliding mode surface, ensuring the robustness to the disturbance. In order to weaken the chattering, a SISM sliding surface is further designed and the OSISMC is obtained. the stabilization of the system is proved by Lyapunov theory. the simulation shows that the proposed OSISMC can improve the control performance and reduce the chattering. © 2022 ieee.

关键词： Stabilization

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data-driven Model-Free Adaptive control for Motion control systems with Saturation Constraints 13

Data-Driven Model-Free Adaptive Control for Motion Control S...

引用

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

作者： Mi, Baohan Huo, Xin Li, Rongmei Xu, Fan Zhao, Hui Control and Simulation Center Harbin Institute of Technology Harbin150080 China

ISBN: (纸本)9798350361674

In actual motion control systems, saturation constraints are generally encountered, restricting the tracking performance seriously. this paper aims at devising an anti-windup scheme for motion control systems controlled by full form dynamic linearization based model-free adaptive control (FFDL-MFAC). the proposed anti-windup scheme includes a designed saturation identifier and a modified control algorithm. With the proposed hysteresis-deadband characteristic modules, the identifier exhibits robust tolerance on signal noise. For the modified control algorithm, the cumulative term of error will be cut off by the output of the identifier when the actuator falls saturated, supporting exiting saturation constraint once reaches. Besides, the performance and the BIBO stability of the system controlled by the proposed scheme are substantiated. Finally, numerical simulations are presented to demonstrate the superiority of the proposed scheme, even in the presence of signal noise and disturbances. © 2024 ieee.

关键词： Adaptive control systems

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Robust Stability and Stabilization of Switched Parametric Uncertain Linear systems with Infinite Subsystems 13

Robust Stability and Stabilization of Switched Parametric Un...

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

作者： Zhang, Jing Liang, Kaili Zhu, Liying Jinan250353 China

ISBN: (纸本)9798350361674

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. © 2024 ieee.

关键词： State feedback

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Observer Design for Two-Dimensional Switched Discrete-Time Linear systems with Multiple Equilibrium Points 13

Observer Design for Two-Dimensional Switched Discrete-Time L...

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

作者： Liang, Kaili Zhang, Jing Zhu, Liying School of Information and Automation Qilu University of Technology Shandong Academy of Sciences Jinan250353 China

ISBN: (纸本)9798350361674

this paper investigates the observer design problem for two-dimensional switched discrete-time linear systems with multiple equilibrium points. Firstly, a distributed state observer is constructed by considering that all subsystems are observable. then, a sufficient condition for the existence of a state observer is given by analyzing the nature of the solution of the error switching system via an iterative method. the results obtained indicate that the constructed state observer can quickly follow the state of the original system over time. In the end, the validity and correctness of the designed state observer are proved by numerical simulation examples. © 2024 ieee.

关键词： State estimation

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data-driven Multi-Objective Optimization of Power Consumption and Quality for Finishing Rolling Process 13

Data-Driven Multi-Objective Optimization of Power Consumptio...

引用

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

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

ISBN: (纸本)9798350361674

this paper investigates the operational optimization in the context of the steel hot rolling production and finishing facilities. Optimizing the operational variables of the finishing rolling process can improve the rolling quality and reduce the unit power consumption. However, there are too many operational variables in the process and their interactions are strongly coupled, making it difficult to develop an effective model for in-depth study. therefore, this paper adopts a data-driven approach, using neural network algorithms to construct the data prediction models between performance and set values respectively. Multi-objective optimization of the set values is performed by combining the actual mechanism and equipment constraints. In the obtained Pareto optimal solutions, the conflicting choices are further balanced among multiple objectives to obtain the most suitable operating parameters. Experimentally verified using production line production data, the method proposed in this paper significantly improves the performance indexes. © 2024 ieee.

关键词： Multiobjective optimization

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Parameter Inversion of High-Dimensional Chaotic systems Using Neural Ordinary Differential Equations 13

Parameter Inversion of High-Dimensional Chaotic Systems Usin...

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

作者： Bian, Hanlin Zhu, Wei Chen, Zhang Jingsui, L. Pei, Chao Key Laboratory of Intelligent Analysis and Decision on Complex Systems Chongqing University of Posts and Telecommunications Chongqing400065 China School of Computer Science and Technology Chongqing University of Posts and Telecommunications Chongqing400065 China

ISBN: (纸本)9798350361674

High-dimensional chaotic systems possess complex structural characteristics, which have potential research value in fields like encrypted communication and chaos control. the inverse problem of parameters in high-dimensional chaotic systems is of significant importance for a profound understanding of the dynamic properties of such systems. this article takes a departure from neural ordinary differential equations and embeds the structural dynamics of the dynamical systems to the parameter inversion of high-dimensional chaotic systems. Numerical experiments on different high-dimensional chaotic systems indicate that neural ordinary differential equations perform exceptionally well in solving these inversion problems, with low errors and strong robustness. © 2024 ieee.

关键词： Inverse problems

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