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43rd Chinese control Conference, CCC 2024

作者： Zhang, Xinyao Cao, Junzhe Dalian University of Technology Control Science and Engineering Dalian116024 China Dalian University of Technology Key Lab. of Intelligent Control and Optimization for Industrial Equipment of Ministry of Education Dalian116024 China

ISBN: (纸本)9789887581581

Airfoil flow field is of great significance to the design optimization of aircraft shape. Convolutional Neural Networks (CNNs) have been applied in flow field prediction studies, which greatly reduce the cost of computation time in traditional Computational Fluid Dynamics (CFD) methods. However, such CNN-based approaches normally bring extra errors due to the procedure of interpolating irregular grid data into Cartesian grids, which may lead to underperforming prediction accuracy. In this paper, an improved PointNet++ point cloud deep learning method was proposed for the airfoil flow field prediction. The unstructured grid vertices in the CFD domain were treated as point cloud as well as the input of neural network, and only the original information of the data was adopted to avoid interpolation. Neighborhood relationship features were defined and spliced with neighborhood point features, and the features were integrated by simultaneously solving for the maximum, minimum, sum, and average values to extract the local or global features of the system to enhance the performance of deep network for extracting the information. Finally, the proposed network was trained and tested by using 549 sets of flow field data formed by 61 airfoils under 9 angle-of-attack conditions, and further predicted for an additional 32 datasets with different airfoils and conditions. The simulation results show that the method proposed in this study gets lower prediction errors in terms of pressure, Mach number, x-direction velocity, and y-direction velocity than the two other current point cloud deep networks, PointNet and PointNet++. At the same time, the computation speed of this method is about 14 times faster than the traditional CFD method. © 2024 Technical Committee on control Theory, Chinese Association of Automation.

关键词： Computational fluid dynamics

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Scheduling Optimization of Coal Washing Process Based on Two-Stage Fuzzy Heuristic Algorithm 13

Scheduling Optimization of Coal Washing Process Based on Two...

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13th IEEE Data Driven control and Learning systems Conference, DDCLS 2024

作者： Zhou, Hui Wang, Zhiyuan Han, Zhongyang Wang, Linqing Zhao, Jun Wang, Wei Dalian University of Technology Key Lab. of Intelligent Control and Optimization for Industrial Equipment of Ministry of Education Dalian116024 China School of Control Science and Engineering Dalian University of Technology Dalian116024 China

ISBN: (纸本)9798350361674

Coal washing and sorting plays a vital role for coal production. Reasonable scheduling of washing and sorting equipment, crushers and various coal silos in the coal washing and sorting process can effectively increase the total value of product coal and reduce the total energy consumption of equipment. In this paper, a multi-constrained nonlinear mixed integer programming model considering the start and stop of each equipment in the washing process is established to realize the specific regulation of the washing process. In order to overcome the problem such as local convergence when solving high-dimensional space problems, a two-stage fuzzy heuristic algorithm is proposed in this paper. In the first stage, a variety of population genetic algorithms based on fuzzy adaptive strategy control are designed for global search to establish an elite population bank to preserve high-quality individuals produced by various groups. In the second stage, an improved simulated annealing algorithm is used to perform a local search with a backtracking process on specific neighbors of individuals in the elite population bank, which can improve the stability of the algorithm and reduce the situation of falling into local convergence. Then the real data of a Chinese coal mining company was used for experiments, where the proposed algorithm is compared with other commonly used methods. The experimental results demonstrate that the proposed algorithm can increase the total coal value of the washed product by at least 1.7% compared to other algorithms, and the average total number of starts and stops of the equipment is reduced by about 7.3%, which are effectively beneficial for the related enterprise. © 2024 IEEE.

关键词： Coal mines

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Fault identification of rotating machinery based on multiscale convolution and deep residual networks 9

Fault identification of rotating machinery based on multisca...

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9th International Symposium on Sensors, Mechatronics, and Automation System, ISSMAS 2023

作者： Wang, Huihui Fan, Tianqi Qiu, Sen Liu, Long School of Intelligence and Electronic Engineering Dalian Neusoft University of Information Dalian116023 China Key Lab. of Intelligent Control and Optimization for Industrial Equipment of Ministry of Education The School of Control Science and Engineering Dalian University of Technology Dalian116024 China

ISBN: (纸本)9781510672802

Rotating machinery and equipment have a wide range of industrial applications, and it is important to perform intelligent fault detection during their operation. In order to fully capture the multi-scale features of mechanical fault data while solving the degradation problem of deep networks, we propose an intelligent fault detection model combining multi-scale convolution and deep residual networks. We validate it on four publicly availab.e datasets, and the results demonstrate the excellent performance of the proposed method. © 2024 SPIE.

关键词： Convolution Feature extraction Deep learning Machine learning Vibration Data modeling Lithium Convolutional neural networks Data centers Signal processing

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Trajectory Tracking of AUV under Current Disturbance Based on Adaptive LADRC 2

Trajectory Tracking of AUV under Current Disturbance Based o...

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2nd International Conference on Automation, Robotics and Computer engineering, ICARCE 2023

作者： Ji, Daxiong Wang, Xinwei Xu, Minghui Zhang, Qiang Sharma, Sanjay Sutton, Robert Institute of Marine Electronics and Intelligent Systems Ocean Collage Zhejiang University Donghai Lab Zhoushan China School of Engineering Computing and Mathematics University of Plymouth Intelligent Autonomous Control Systems Plymouth United Kingdom

ISBN: (纸本)9798350308341

In consideration of the trajectory tracking problem of autonomous underwater vehicle (AUV) under external current disturbance, an adaptive linear active disturbance rejection control (LADRC) method is proposed. Unlike the conditional LADRC using fixed parameters for extended state observer, the proposed controller makes use of the error of estimation and automatically adjusts the parameter online. As shown in the simulation, the adaptive LADRC estimates the external current disturbance effectively, and tracks the 3D trajectory better compared with traditional LADRC. © 2023 IEEE.

关键词： Disturbance rejection

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control Method for Desorption Tower Liquid Level in Fluidized Catalytic Cracking Based on LESO-DMC

Control Method for Desorption Tower Liquid Level in Fluidize...

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2023 China Automation Congress, CAC 2023

作者： Xiao, Jinkun Zhu, Haibo Zhou, Fan Sun, Kaibiao Zhao, Jun Dalian University of Technology Key Lab. of Intelligent Control and Optimization for Industrial Equipment Ministry of Education China School of Control Science and Engineering Dalian University of Technology Dalian China

ISBN: (纸本)9798350303759

Fluidized catalytic cracking (FCC) units are crucial equipment in the petroleum refining process, and the level of control directly impacts product quality and yield. To improve the control level of the desorption tower in FCC, this paper proposes a dynamic matrix control (DMC) approach based on the active disturbance rejection strategy. Based on DMC, a linear extended state observer (LESO) is constructed to compensate for model plant mismatch (MPM) and unknown disturbances. The observer bandwidth parameters are determined to optimize robustness, enabling the implementation of active disturbance rejection model predictive control in FCC units. A comparative experimental study is conducted, comparing the proposed method with the traditional DMC approach that does not account for uncertainties and disturbances. The impact of different disturbances on the control strategy is discussed. Experimental results demonstrate that the proposed method exhibits superior control performance and stronger disturbance rejection capability for the liquid level in the desorption tower of the FCC unit. © 2023 IEEE.

关键词： Fluidization

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Distributed data-driven consensus control of multi-agent systems under switched uncertainties

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control Theory and Technology 2023年第3期21卷 478-487页

作者： Wenjie Liu Yifei Li Gang Wang Jian Sun Jie Chen National Key Lab of Autonomous Intelligent Unmanned Systems Beijing Institute of TechnologyBeijing 100081China Beijing Institute of Technology Chongqing Innovation Center Chongqing 401120China Department of Control Science and Engineering Tongji UniversityShanghai 201804China

Cooperative control of multi-agent systems(MASs),particularly consensus control,has gained significant attention in the last two decades,thanks to the rapid and sustained development of distributed and networked *** this paper,we present some new results focused on consensus control of a set of unknown linear MASs(whose system matrices are unknown)under unknown switched uncertainties,with an emphasis on distributed data-driven *** proposed controller is end-to-end,designed by solving two data-based semi-definite programs(SDPs),which adjust to the changes of the uncertainty *** approach achieves asymptotic consensus of the MAS provided that the switching is slow enough and the uncertainty is *** illustrate the effectiveness of our proposed method through a numerical example.

关键词： Data-driven control Multi-agent systems Consensus-Switched uncertainties

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Impact Time Constrained Guidance with Bounded Field-of-View and Input

Impact Time Constrained Guidance with Bounded Field-of-View ...

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AIAA Science and Technology Forum and Exposition, AIAA SciTech Forum 2025

作者： Singh, Swati Kumar, Shashi Ranjan Mukherjee, Dwaipayan Intelligent Systems and Control Lab Department of Aerospace Engineering Indian Institute of Technology Bombay Powai Mumbai400 076 India Department of Electrical Engineering Indian Institute of Technology Bombay Powai Mumbai400 076 India

ISBN: (数字)9781624107238

ISBN: (纸本)9781624107238

This article proposes a guidance scheme for stationary targets to achieve time-constrained interception at a desired time, considering the bounded field-of-view capability of seekerequipped interceptors and the physical limits of the actuator. Actuator saturation is a crucial problem in physical systems, often resulting in performance degradation. However, the actuator limitations are usually known to the designer, therefore, augmenting these constraints into the guidance design may enhance performance. The time-to-go error-based approach is used to satisfy the field-of-view constraint;further, the engagement kinematics are augmented with an input saturation model to incorporate the lateral acceleration constraints. Subsequently, the guidance strategy that constrains the lateral acceleration and the time-to-go values within their respective bounds is derived using Lyapunov stability concepts and the backstepping technique. Furthermore, a multi-stage approach is suggested to expand the achievable range of impact time. Numerical simulations are performed to validate the efficacy of the proposed scheme for different initial engagement geometries. © 2025, American Institute of Aeronautics and Astronautics Inc, AIAA. All rights reserved.

关键词： Backstepping

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Evolution of Cooperation in Snowdrift Game with Multi-Memory Mechanism 42

Evolution of Cooperation in Snowdrift Game with Multi-Memory...

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42nd Chinese control Conference, CCC 2023

作者： Liu, Yaojun Liu, Xingwen Tashi, Nyima College of Electrical Engineering Southwest Minzu University Lab. of Complex Systems & Intelligent Control Key Lab. of Electronic Information of State Ethnic Affairs Commission Sichuan Chengdu610225 China Engineering Research Center for Tibetan Information Processing School of Information Science and Technology Tibet University Lhasa850000 China

ISBN: (纸本)9789887581543

Cooperative behavior has been of great concern in evolutionary game researches because of its important role in social life and natural evolution. Since memory can greatly influence the emergence of cooperative behavior, memory-based research on evolutionary games has proliferated in recent years. In the present paper, inspired by the well-known 'Stanford marshmallow experiment' (SME), we proposed a multi-memory mechanism in Snowdrift game (SDG) on spatial lattice networks whose core lies in what follows: Players are divided into two groups according to individual self-control;different memory lengths are set by group and by time in a single realization. The simulation results show that our proposed mechanism promotes cooperation level of the considered evolutionary game. More specifically, as memory length increases to an intermediate length, the cooperation level increases as well. For mechanism with short memory length, the effect of population structure is obvious and small values of adjusting factor can intensively facilitate high-frequency cooperative behavior. These findings may be helpful to understand the connection between population heterogeneity and the emergence of high cooperation. © 2023 Technical Committee on control Theory, Chinese Association of Automation.

关键词： Behavioral research

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EMHDRL: Adversarial Attacks on Graph Neural Networks via Hierarchical Reinforcement Learning 42

EMHDRL: Adversarial Attacks on Graph Neural Networks via Hie...

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42nd Chinese control Conference, CCC 2023

作者： Sun, Guowei Ding, Jie Li, Xiang School of Information Science and Engineering Fudan University Adaptive Networks and Control Lab Department of Electronic Engineering Shanghai200433 China Institute of Complex Networks and Intelligent Systems Shanghai Research Institute for Intelligent Autonomous Systems Tongji University Shanghai201210 China

ISBN: (纸本)9789887581543

Graph neural networks (GNNs), with its powerful ability to explore the topological structure of graph data, have achieved great success in various downstream tasks. However, recent studies have shown that due to the lack of robustness, GNNs are easily fooled by perturbations. For node classification tasks, the current attack methods mainly focus on manipulating edges, modifying features and adding fake nodes. Although these attacks are effective, the changes to the original graph data are easy to be detected. Therefore, to misclassify the target nodes, we propose an Edge Manipulation method via Hierarchical Deep Reinforcement Learning (EMHDRL) by taking accounting to the imperceptibility of perturbation on graph data. Through modeling the topology perturbation as Finite Horizon Markov Decision Process, EMHDRL adopts a hierarchical Q-function network to manipulate edges of graph, while trying to ensure that the degree of the attacked node and the total number of edges of the whole graph remain unchanged. Our experimental results show that the proposed method can effectively reduce the performance of GNNs in classifying the target nodes and has strong transferability. © 2023 Technical Committee on control Theory, Chinese Association of Automation.

关键词： Reinforcement learning

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Influence of the load variations on the PSS performance during steady state and transient state 20

Influence of the load variations on the PSS performance duri...

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20th International Multi-Conference on systems, Signals and Devices, SSD 2023

作者： Hashim, Wassan Adnan Hamad Hameed, Jawad Derbel, Nabil Al-Qalam University College Department of Technical Computer Engineering Kirkuk Iraq University of Sfax National Engineering School of Sfax Control & Energy Management Lab. Tunisia College of Petroleum Processes Engineering Tikrit University Department of Petroleum Systems Control Engineering Iraq University of Sfax National Engineering School of Sfax Control & Energy Management Lab. Sfax Tunisia

ISBN: (纸本)9798350332568

The load on a power station varies from time to time due to uncertain demands of the consumers and is known as variable load on the station. Also it is known to have an effect on the performance of a power system stabilizer while damping inter-area oscillation. In this paper conventional lead lag PSS and PSS2B are assessed on real case studies;Peerdawod Gas power station in north of Iraq- Erbil. The PSS utilizes the local rotor speed and power as its input. To investigate the effects of dynamic loads on power systems stability, the effectiveness of PSS is tested and compared with the variation of loads during and after sadden faults. The simulation software that used Matlab.M/Simulink;and results of this paper contribute to the understanding of how load variations influence the system damping based on PSS. © 2023 IEEE.

关键词： Voltage regulators

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