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第43届中国控制会议

作者： Wenqi Hao Yiming Fang Le Liu Xiaonan Liu Key Lab of Industrial Computer Control Engineering of Hebei Province Yanshan University Engineering Research Center of the Education Ministry for Intelligent Control System and Intelligent Equipment Yanshan University

ISBN: (数字)9789887581581

ISBN: (纸本)9798350366907

In this paper,a fault detection observer and an adaptive sliding mode fault-tolerant controller are designed for the vibration displacement system of a continuous casting mold with actuator faults and load torque ***,a mathematical model of a continuous casting mold vibration displacement system driven by a servo motor with actuator faults and load disturbance uncertainties are ***,a fault detection observer is designed to obtain information on actuator faults,load disturbances,and system state ***,based on the information from the fault detection observer,an adaptive sliding mode fault-tolerant controller is designed,and the stability of the designed system is proved using Lyapunov stability ***,the effectiveness of the designed adaptive sliding mode fault-tolerant controller based on fault observer is verified through simulation.

关键词： Continuous casting mold Fault detection observer Fault tolerant control Actuator fault Vibration displacement system Driven by a servo motor

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Event-Triggered Robust Model Predictive control for Multi-Agent Systems Under Hybrid Attacks

Event-Triggered Robust Model Predictive Control for Multi-Ag...

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第42届中国控制会议

作者： Yongxin Yin Jianxiong Li Chongyi Gao Yiming Fang Key Lab of Industrial Computer Control Engineering of Hebei Province Yanshan University Department of Electromechanical Engineering Tangshan University

In this paper,the consensus control problem for multi-agent systems（MASs） suffering hybrid attacks is discussed,in which the hybrid attacks include randomly occurring deception attacks on system dynamic behavior and denial-of-service attacks（DoS）.An event-triggered robust model predictive control（RMPC） method is presented to achieve the consensus of MASs under hybrid *** can be proved that the consensus errors can be asymptotic with prescribed H performance,the sufficient condition for stability can be given in terms of linear matrix inequalities（LMIs）,and the controller gains are obtained by solving an optimization problem involving some *** the event-triggered mechanism with maximum trigger interval,not only can not only save computation time and facilitate the efficiency of the shared resources but also can effectively be responsible for the negative impact of cyber-attacks on system ***,simulation results are given to illustrate the validity and effectiveness of the proposed method.

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A Novel A-ESO Composited Non-recursive control Methodology for Complex High-order Disturbed Nonlinear Systems

A Novel A-ESO Composited Non-recursive Control Methodology f...

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第42届中国控制会议

作者： Cong Zhang Shiji Liu Shuang Liu School of Electrical Engineering Yanshan University Key Lab of Industrial Computer Control Engineering of Hebei Province Yanshan University

This paper mainly focuses on a disturbance rejection control methodology for a series of mismatched and matched disturbed complex ***,a novel finite-time arbitrary-order extended state observer(A-ESO) is proposed to accurately estimate the system states,disturbances,and their corresponding order *** then,a novel concise non-recursive control ideology utilizing all the estimations of A-ESO is proposed to strictly guarantee finite-time stability and convergence of the whole closed-loop ***,compared with the existing non-recursive control methods,the essential characteristic is that all the estimation information is utilized to construct the composite *** will be tremendously conducive to cost-saving and promoting practical implementations.

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CPL-SLAM: Centralized Collaborative Multi-Robot Visual-Inertial SLAM Using Point-and-Line Features

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IEEE Internet of Things Journal 2025年

作者： Liu, Xin Wen, Shuhuan Liu, Huaping Richard Yu, F. Yanshan University Engineering Research Center The Ministry of Education for Intelligent Control System and Intelligent Equipment Key Lab of Intelligent Rehabilitation and Neuroregulation in Hebei Province Department of Key Lab of Industrial Computer Control Engineering of Hebei Province Qinhuangdao066004 China Tsinghua University Department of Computer Science and Technology Beijing100084 China Shenzhen University College of Computer Science and Software Engineering China Carleton University Canada

Traditional visual-inertial SLAM (Simultaneous Localization and Mapping) systems predominantly rely on feature point matching from a single robot to realize the robot pose estimation and environment map construction. However, in complex scenarios, these traditional systems struggle with issues such as tracking failures due to illumination changes, rapid movements, and low-texture environments, and they perform poorly in terms of mapping efficiency and global consistency. To address these challenges, we propose a centralized collaborative SLAM system that employs both point and line features for tracking in the robot and map fusion in the cloud. The proposed system leverages the fusion of point and line features across all instances in the process, which allows our method to achieve higher localization accuracy in structured, low-texture scenes. With the aid of classifying gravity-aligned vertical lines and spatial parallel lines, the proposed system can deliver faster and more accurate odometry in complex scenes. Furthermore, we developed intra-robot and inter-robot loop closure detection methods based on point and line features, generating a globally consistent sparse point cloud and structured scene map in the cloud. Our method is able to build richer maps while improving accuracy compared to existing methods. Experimental results on public datasets and in real-world environments show that, compared to existing advanced methods, our approach demonstrates better performance. © 2014 IEEE.

关键词： SLAM robotics

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Multi-dimension and multi-modal rolling mill vibration prediction model based on multi-level network fusion

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Journal of Central South University 2024年第9期31卷 3329-3348页

作者： CHEN Shu-zong LIU Yun-xiao WANG Yun-long QIAN Cheng HUA Chang-chun SUN Jie School of Electrical Engineering Yanshan UniversityQinhuangdao 066004China Key Lab of Industrial Computer Control Engineering of Hebei Province Yanshan UniversityQinhuangdao 066004China Shougang Jingtang United Iron&Steel Co. Ltd.Tangshan 063200China State Key Laboratory of Rolling and Automation Northeastern UniversityShenyang 110819China

Mill vibration is a common problem in rolling production,which directly affects the thickness accuracy of the strip and may even lead to strip fracture accidents in serious *** existing vibration prediction models do not consider the features contained in the data,resulting in limited improvement of model *** address these challenges,this paper proposes a multi-dimensional multi-modal cold rolling vibration time series prediction model(MDMMVPM)based on the deep fusion of multi-level *** the model,the long-term and short-term modal features of multi-dimensional data are considered,and the appropriate prediction algorithms are selected for different data *** on the established prediction model,the effects of tension and rolling force on mill vibration are *** the 5th stand of a cold mill in a steel mill as the research object,the innovative model is applied to predict the mill vibration for the first *** experimental results show that the correlation coefficient(R^(2))of the model proposed in this paper is 92.5%,and the root-mean-square error(RMSE)is 0.0011,which significantly improves the modeling accuracy compared with the existing *** proposed model is also suitable for the hot rolling process,which provides a new method for the prediction of strip rolling vibration.

关键词： rolling mill vibration multi-dimension data multi-modal data convolutional neural network time series prediction

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A Heterogeneous Reinforcement Learning Approach for Joint Relay Selection and Power Allocation in Time-Varying Uasns with Energy Harvesting

SSRN

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SSRN 2024年

作者： Han, Song Wang, Ziyuan Li, Aijia Li, Xinbin Liu, Zhixin Yan, Lei Zhang, Tong Wei Key Lab of Industrial Computer Control Engineering of Hebei Province Institute of Electrical Engineering Yanshan University Hebei Province Qinhuangdao066004 China School of Computer and Communication Engineering Northeastern University Hebei Province Qinhuangdao066004 China Department of Technology Northeastern University Shandong Province Qingdao266237 China

In this study, we investigate the joint relay selection and power allocation problem for underwater acoustic communication networks with energy harvesting to maximize the cumulative capacity. We specially formulate a heterogeneous reinforcement learning-based framework. In the different decision layers, the Upper Confidence Bound 1 and enhanced Deep Deterministic Policy Gradient algorithms are employed to efficiently explore the discrete and continuous action spaces, respectively. Therefore, the solving difficulty of the complex hybrid discrete-continuous optimization problem can be significantly mitigated. Furthermore, a circular interaction structure is constructed to facilitate the exchange of decision-making information between the two decision layers. The strongly interdependent relationship of the different substrategies can be comprehensively considered, thereby improving the entire performance of thejoint strategy. Moreover, we propose an adaptive reward function, which can guide the user to balance the instantaneous capacity and interruptions, thereby effectively utilizing the green energy harvesting technology and enhancing long-term cumulative performance. Simulation results verify the effectiveness of our proposed algorithm. © 2024, The Authors. All rights reserved.

关键词： Reinforcement learning

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EAVFormer: an end-to-end audio and visual emotion recognition network based on transformers

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Multimedia Systems 2025年第3期31卷 1-22页

作者： Liu, Haoran Sun, Zijian Li, Haibin Li, Yaqian Zhang, Wenming Song, Tao School of Information Science and Engineering Yanshan University Qinhuangdao066004 China The Key Laboratory for Special Fiber and Fiber Sensor of Hebei Province Yanshan University Qinhuangdao066004 China Key Lab of Industrial Computer Control Engineering of Hebei Province Yanshan University Qinhuangdao066004 China Shenzhen Research Institute of Yanshan University Shenzhen518057 China Key Lab of Measurement Technology & Instrumentation of Hebei Province School of Electrical Engineering Yanshan University Qinhuangdao066004 China

Audio-visual emotion recognition, integrating information from various sources, enhances the comprehensive understanding and analysis of human emotions. However, existing methods in this field primarily focus on feature fusion, often overlooking the equally crucial stage of feature extraction. Typically, these methods employ pre-trained models to extract features during the initial phase, complicating modality-specific feature extraction and increasing system complexity and coordination challenges, which hinder effective audio-visual feature fusion. To address these issues, we propose an end-to-end audio-visual emotion recognition model, EAVFormer. For the audio modality, we input raw audio signals along with a mix of mel spectrograms and Mel Frequency Cepstral Coefficients (MFCCs), employing a hierarchical transformer technique that facilitates both shallow local and deep global feature extraction to fully harness complementary acoustic information, significantly enhancing the acoustic modality’s contribution. For the visual modality, we input raw video frames and employ a hierarchical transformer feature extraction method that integrates three-dimensional convolution and spatio-temporal self-attention mechanisms to address spatial and temporal redundancies and dependencies, thereby enhancing the expression of dynamic visual information. In the modal fusion stage, we construct dual-modality representations and encode inter-modal information while preserving modality-specific and meaningful intra-modal features. Testing on three popular datasets RAVDESS, CREMA-D, and CMU-MOSEI demonstrates that EAVFormer surpasses existing state-of-the-art methods in audio-visual emotion recognition accuracy, achieving rates of 95.17%, 86.13%, and 78.22%, respectively. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2025.

关键词： Image coding

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Improved Lightweight YOLOv5 Based on GhostNet and Its Application in Target Board Detection for Ladle Nozzle Positioning

Improved Lightweight YOLOv5 Based on GhostNet and Its Applic...

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第43届中国控制会议

作者： Fengfei Si Yiming Fang Hao Ju Dongliang Zhao Jingliang Xin Key Lab of Industrial Computer Control Engineering of Hebei Province Yanshan University Engineering Research Center of the Education Ministry for Intelligent Control System and Intelligent Equipment Yanshan University Tangshan Branch HBIS Company Limited

ISBN: (数字)9789887581581

ISBN: (纸本)9798350366907

The detection of the target board of ladle nozzle positioning is the basis of automatic loading and unloading of ladle nozzle by ladle robot in continuous casting. Due to the complex situation of high temperature, dust, backlight and so on, the target board of ladle nozzle positioning is prone to false detection or low detection accuracy, and it is difficult to take into account the contradiction between rapidity and detection accuracy. Therefore, this paper proposes an improved lightweight algorithm GVP-YOLOv5(YOLOv5 with GhostNet, VoVGSCSP and Pruning), which takes into account both detection accuracy and rapidity, to detect the target board for ladle nozzle positioning. Firstly, Ghost module is used to improve the backbone network of YOLOv5 as the backbone feature extraction network to reduce the number of parameters and calculation of the backbone. Then in the neck, the GSConv module is used to design a thin neck structure, which can ensure the multi-scale feature fusion capability and further reduce the complexity of the network. Finally, the model pruning technology is used to prune the whole model, and the channels with small scaling factor are removed to compress the model to the greatest extent, so as to meet the requirements of industrial field application deployment. The experimental results show that the lightweight GVP-YOLOv5 algorithm proposed in this paper can complete the real-time detection of the target board in complex industrial scenarios. Compared with the original YOLOv5s algorithm, the number of parameters in the model is reduced by 82.6%, and the size of the model is significantly reduced to 19.4%. At the same time, the detection accuracy is maintained at more than 95% on mAP@50:95 and the detection speed is effectively improved.

关键词： Target board detection Ladle nozzle positioning YOLOv5 lightweight GhostNet thin neck structure channel pruning

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Event-Triggered Robust Model Predictive control for Multi-Agent Systems Under Hybrid Attacks

Event-Triggered Robust Model Predictive Control for Multi-Ag...

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Chinese control Conference (CCC)

作者： Yongxin Yin Jianxiong Li Chongyi Gao Yiming Fang Key Lab of Industrial Computer Control Engineering of Hebei Province Yanshan University Qinhuangdao China Department of Electromechanical Engineering Tangshan University Tangshan China

In this paper, the consensus control problem for multi-agent systems (MASs) suffering hybrid attacks is discussed, in which the hybrid attacks include randomly occurring deception attacks on system dynamic behavior and denial-of-service attacks (DoS). An event-triggered robust model predictive control (RMPC) method is presented to achieve the consensus of MASs under hybrid attacks. It can be proved that the consensus errors can be asymptotic with prescribed H ∞ performance, the sufficient condition for stability can be given in terms of linear matrix inequalities (LMIs), and the controller gains are obtained by solving an optimization problem involving some LMIs. Employing the event-triggered mechanism with maximum trigger interval, not only can not only save computation time and facilitate the efficiency of the shared resources but also can effectively be responsible for the negative impact of cyber-attacks on system performance. Finally, simulation results are given to illustrate the validity and effectiveness of the proposed method.

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An Improved Artificial Potential Field Method for Formation control and Obstacle Avoidance of the Multi-Agents Systems

An Improved Artificial Potential Field Method for Formation ...

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Chinese Automation Congress (CAC)

作者： Yubiao Yan Jianxiong Li Haibo Dong Chongyi Gao Yiming Fang Key Lab of Industrial Computer Control Engineering of Hebei Province Yanshan University Qinhuangdao China Department of Electromechanical Engineering Tangshan University Tangshan China

Obstacle avoidance is a significant research content in multi-agents formation control. The obstacle avoidance of multi-agents systems is investigated in this paper, and an improved artificial potential field method (IAPF) is proposed to avoid unknown obstacles in multi-agent formation. Aiming at improving the efficiency of formation avoidance and solving the non-reachable and local minima problems, the dynamic sub-target algorithm is proposed. The proposed method enables the multi-agent system (MASs) to avoid obstacles smoothly and quickly and complete formation tasks in complex environments by tracking dynamic sub-target as well, in which the position and motion direction of adjacent agents are taken into account in the selection of a sub-target for the formation efficiency. In addition, a variable potential field range is defined to ensure the safety of the formation. Finally, several simulation results verify the superiority and effectiveness of the proposed approach.

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