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第40届中国控制会议

作者： Fenglan Sun Yunhao Shen Key Laboratory of Intelligent Analysis and Decision on Complex Systems School of ScienceChongqing University of Posts and Telecommunications Key Laboratory of Intelligent Air-Ground Cooperative Control for Universities in Chongqing College of AutomationChongqing University of Posts and Telecommunications Potsdam Institute for Climate Impact Research

This paper studies the consensus in multi-agent systems with multiple disturbances and time delay over fixed and switching topology via proportional-integral control. The controller that includes proportional and integral actions is *** adopting matrix theory, algebraic graph theory and Lyapunov stability theory, sufficient conditions for consensus are obtained. Several numerical simulations are given to verify the effectiveness of the algorithms.

关键词： consensus multiple disturbances switching topology proportional-integral control

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Dynamical primal-dual accelerated method with applications to network optimization

arXiv

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arXiv 2019年

作者： Zeng, Xianlin Lei, Jinlong Chen, Jie Key Laboratory of Intelligent Control and Decision of Complex Systems School of Automation Beijing Institute of Technology Beijing100081 China Department of control science and engineering Tongji University Shanghai China Shanghai Research Institute for Intelligent Autonomous Systems Shanghai China

This paper develops a continuous-time primal-dual accelerated method with an increasing damping coefficient for a class of convex optimization problems with affine equality constraints. We then analyze critical values for parameters in the proposed method and prove that the rate of convergence is O(t12 ) by choosing proper parameters. As far as we know, this is the first continuous-time primal-dual accelerated method that can obtain the optimal rate. Then this work applies the proposed method to two network optimization problems, a distributed optimization problem with consensus constraints and a distributed extended monotropic optimization problem, and obtains two variant distributed algorithms. Finally, numerical simulations are given to demonstrate the efficacy of the proposed method. Copyright © 2019, The Authors. All rights reserved.

关键词： Continuous time systems

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Evaluation of continuity effectiveness for networked space based information system

Evaluation of continuity effectiveness for networked space b...

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作者： Chen, Chen Chen, Jie Zhang, Juan Department of Automation Tsinghua University Beijing 100084 China School of Automation Beijing Institute of Technology Beijing 100081 China Key Laboratory of Complex System Intelligent Control and Decision Ministry of Education Beijing 100081 China

ISBN: (纸本)9787894631046

As the trend of future space based information system, networked space based information system can acquire and process all sorts of information more quickly and continuously under the user task scheduling. In the background of earthquake relief for applications, working flow and main tasks of the system are analyzed, and continuity effectiveness evaluation index system is constructed in multi-task level. The continuity effectiveness is a measurement of intensive degree of information returned. Evaluation model based on grey evaluation method is constructed and different reference indexes are used for different tasks. Examples are given by using statistical data in simulation, and the integrative evaluation results are obtained. Detailed evaluation results in every specific task level are offered, which are helpful to find out the effectiveness bottleneck factors meanwhile provide guidance in system design and development.

关键词： Information use

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Data-driven control of consensus tracking for discrete-time multi-agent systems

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Journal of the Franklin Institute 2023年第7期360卷 4661-4674页

作者： Xiufeng Zhang Gang Wang Jian Sun Faculty of Mechanical and Electrical Engineering Kunming University of Science and Technology Kunming 650504 China Yunnan International Joint Laboratory of Intelligent Control and Application of Advanced Equipment Kunming 650504 China School of Automation Beijing Institute of Technology Beijing 100081 China Key Lab of Intelligent Control and Decision of Complex Systems Beijing 100081 China

This paper investigates the consensus tracking problem of leader-follower multi-agent systems. Different from most existing works, dynamics of all the agents are assumed completely unknown, whereas some input-output data about the agents are available. It is well known from the Willems et al. Fundamental Lemma that when inputs of a linear time-invariant (LTI) system are persistently exciting, all possible trajectories of the system can be represented in terms of a finite set of measured input-output data. Building on this idea, the present paper proposes a purely data-driven distributed consensus control policy which allows all the follower agents to track the leader agent’s trajectory. It is shown that for a linear discrete-time multi-agent system, the corresponding controller can be designed to ensure the global synchronization with local data. Even if the data are corrupted by noises, the proposed approach is still applicable under certain conditions. Numerical examples corroborate the practical merits of the theoretical results.

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Local entropy difference-fast marching method for leak diagnosis and localization

Local entropy difference-fast marching method for leak diagn...

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IEEE/ASME (AIM) International Conference on Advanced intelligent Mechatronics

作者： Ling Zhao Tao Wang Wei Fan Nan Ge Tong Zhao Guangzheng Peng School of Automation Complicated System of Intelligent Control and Decision Key Laboratory of Education Ministry Beijing Institute of Technology Beijing China Department of Engineering Physics Tsinghua University Beijing China

In this paper, an effective technique for leak diagnosis and localization which couples the local entropy difference method (LEDM) to the fast marching method (FMM) is proposed. A computer-based system is described to combine a specific pneumatic circuit with an on-line infrared camera which is used to test the air-tightness of a test vessel. The test object is charged with cryogenic compressed air and its temperature field is acquired by infrared thermography in this system. Then, both the leakage point and its edge can be identified by means of the designed LED-FM method. An experiment is conducted that confirms that the leak diagnosis and localization method presented in this paper is effective in diagnosing leakage points and in extracting accurate leakage edges, in spite of the presence of surface defects.

关键词： Entropy Pixel Image edge detection Level set Cameras Equations Probes

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A kind of online self-tuning smith predictor

A kind of online self-tuning smith predictor

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IEEE International Conference on Mechatronics and Automation

作者： Xu Chen Fei Meng Xiaopeng Chen Qiang Huang Intelligent Robotics Institute Beijing Institute of Technology Beijing China Beijing Advanced Innovation Center for Intelligent Robots and System Beijing Institute of Technology Beijing China Key Laboratory of Biomimetic Robots and Systems Beijing Institute of Technology Beijing China International Joint Research Laboratory of Biomimetic Key Laboratory of Intelligent Control and Decision of Comp lex System Beijing China

ISBN: (纸本)9781509023974

This paper presents an online self-tuning Smith Predictor for the First Order Plus Dead Time Model (FOPDT). It can tune time delay through oscillated input and output. Realtime phase difference detection is used to obtain the phase difference between the input and the output. An online tuner is used to minimize the phase difference. Once the phase difference is minimized, the exact time delay is completely compensated. Our method is suitable for both systems with constant time delay or variant time delay.

关键词： Delay effects Temperature control Delays Predictive models Oscillators Robots

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EMOST: A dual-branch hybrid network for medical image fusion via efficient model module and sparse transformer

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Computers in Biology and Medicine 2024年 179卷 108771-108771页

作者： Wang, Wenqing He, Ji Liu, Han School of Automation and Information Engineering Xi'an University of Technology Xi'an710048 China Shaanxi Key Laboratory of Complex System Control and Intelligent Information Processing Xi'an University of Technology Xi'an710048 China

Multimodal medical image fusion fuses images with different modalities and provides more comprehensive and integrated diagnostic information. However, current multimodal image fusion methods cannot effectively model non-local contextual feature relationships, and due to direct aggregation of the extracted features, they introduce unnecessary implicit noise into the fused images. To solve the above problems, this paper proposes a novel dual-branch hybrid fusion network called EMOST for medical image fusion that combines a convolutional neural network (CNN) and a transformer. First, to extract more comprehensive feature information, an effective feature extraction module is proposed, which consists of an efficient dense block (EDB), an attention module (AM), a multiscale convolution block (MCB), and three sparse transformer blocks (STB). Meanwhile, a lightweight efficient model (EMO) is used in the feature extraction module to exploit the efficiency of the CNN with the dynamic modeling capability of the transformer. Additionally, the STB is incorporated to adaptively maintain the most useful self-attention values and remove as much redundant noise as possible by developing the top-k selection operator. Moreover, a novel feature fusion rule is designed to efficiently integrate the features. Experiments are conducted on four types of multimodal medical images. The proposed method shows higher performance than the art-of-the-state methods in terms of quantitative and qualitative evaluations. The code of the proposed method is available at https://***/XUTauto/EMOST. © 2024 Elsevier Ltd

关键词： Feature extraction

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Scheduling optimization of silicon single crystal production process based on adaptive improved particle swarm optimization

Scheduling optimization of silicon single crystal production...

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Chinese Automation Congress (CAC)

作者： Lu Kang Ding Liu Yali Wu Guozheng Ping School of Automation and Information Engineering Xi’an University of Technology National and Local Joint Engineering Research Center of Crystal Growth Equipment and System Integration Shaanxi Key Laboratory of Complex System Control and Intelligent Information Processing Xi’an China School of Automation and Information Engineering Xi’an University of Technology National and Local Joint Engineering Research Center of Crystal Growth Xi’an University of Technology Shaanxi Key Laboratory of Complex System Control and Intelligent Information Processing Xi’an China School of Automation and Information Engineering Xi’an University of Technology Shaanxi Key Laboratory of Complex System Control and Intelligent Information Processing Xi’an China

ISBN: (纸本)9781665426480

Aiming at the potential safety hazards of silicon single crystal production enterprises in the process of multi-furnace silicon single crystal production, and considering the maximum power load requirements in actual enterprises, a silicon single crystal production process scheduling model with the goal of minimizing the maximum completion time was established. Based on this model, an adaptive improved particle swarm optimization algorithm (Adaptive GPSO) is proposed to solve it. The algorithm retains the individual optimal value and global optimal value of the particle swarm algorithm, and introduces the crossover operation and mutation operation in the genetic algorithm to improve the new individual generation mechanism. At the same time, it performs dynamic iterative calculations on the mutation probability and crossover probability to avoid the algorithm fall into the local optimum, and improve the diversity and convergence of the algorithm. By solving different scales scheduling problems of silicon single crystal production process, the experiments on real-world data shows that the feasibility and effectiveness of the proposed algorithm.

关键词： Adaptation models Heuristic algorithms Crystals Production Probability Silicon Scheduling

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Power system Events Classification Technology Based on Deep-Learning

Power System Events Classification Technology Based on Deep-...

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IEEE International Symposium on Circuits and systems (ISCAS)

作者： Xin Lei Hongwei Ma Bin Liu Zhen Li School of Automation and Key Laboratory for Intelligent Control & Decision on Complex Systems Beijing Institute of Technology Beijing China College of Information Science and Engineering China University of Petroleum (Beijing) Beijing China

ISBN: (数字)9798350330991

ISBN: (纸本)9798350331004

As the complexity of the power system continues to increase, the frequency of the power system anomalies is on the rise. These anomalies have significant and widespread impacts on the stability of the power grid. Therefore, the rapid and accurate classification of these anomalies is crucial in preventing their further propagation and mitigating potential economic losses. This study presents an algorithm based on Phasor Measurement Unit (PMU) data for monitoring the state of power systems and identifying the types of anomalies. First, a dataset for anomaly event classification is created based on PMU data, which is used to train and validate the anomaly event classification model. Subsequently, a robust anomaly event classification model is constructed, consisting of a residual module with one-dimensional Convolutional Neural Networks (CNN) and a cascaded fully connected neural network classifier. This algorithm has undergone rigorous testing in the IEEE New England 39 bus test system, demonstrating exceptional event recognition accuracy.

关键词： Accuracy Power system stability Propagation losses Phasor measurement units Data models Stability analysis Classification algorithms

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Establishment and Optimization of Gas Flow Prediction Model for Annealing Furnace Based on GA-SVM

Establishment and Optimization of Gas Flow Prediction Model ...

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第37届中国控制会议

作者： Qilin Qu Yan Yuan Jin Yali Yongyue Zhang School of Automation China University of Geosciences Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex System School of Information Science and Engineering Central South University

The annealing furnace is an important equipment for annealing in the production process of cold rolled strip steel. The temperature accuracy of the steel coil is directly affected the production quality of the steel plate. In the annealing furnace temperature control system, strip temperature control is mainly decided by various furnace annealing furnace gas flow in the area, so setting up a precise model between gas flow and temperature has become an important problem of the control system of annealing furnace. In this paper, SVM is applied to the prediction model of the gas flow in the annealing furnace since it’s suitable for nonlinear and high dimensional problems as a machine learning method. In view of that the choice of C, G and the model parameters of the SVM directly affect the prediction precision of the model, this paper uses GA to optimize these two parameters. Finally, we use the data of field production as training and testing samples to establish and verify the model. The experimental results showed that the established model well achieved the requirements of the accuracy of the target.

关键词： Annealing furnace Gas flow Model Support Vector Machine Genetic Algorithm

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