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arXiv 2024年

作者： Niu, Youcheng Xu, Jinming Sun, Ying Chai, Li Chen, Jiming State Key Laboratory of Industrial Control Technology The College of Control Science and Engineering Zhejiang University Hangzhou310027 China School of Electrical Engineering and Computer Science Pennsylvania State University PA16802 United States

This paper studies a class of distributed bilevel optimization (DBO) problems with a coupled inner-level subproblem. Existing approaches typically rely on hypergradient estimations involving computationally expensive Hessian information. To address this, we propose an equivalent constrained reformulation by treating the inner-level subproblem as an inequality constraint, and introduce an adaptive penalty function to properly penalize both inequality and consensus constraints based on subproblem properties. Moreover, we propose a loopless distributed algorithm, AHEAD, that employs multiple-timescale updates to solve each subproblem asymptotically without requiring Hessian information. Theoretically, we establish convergence κ4 rates of O((1−ρ)2K1/3 ) for nonconvex-strongly-convex cases and κ2 O((1−ρ)2K2/3 ) for distributed min-max problems. Our analysis shows the clear dependence of convergence performance on bilevel heterogeneity, the adaptive penalty parameter, and network connectivity, with a weaker assumption on heterogeneity requiring only bounded first-order heterogeneity at the optimum. Numerical experiments validate our theoretical findings. Copyright © 2024, The Authors. All rights reserved.

关键词： Consensus algorithm

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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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MPC-Based Trajectory Tracking control Strategy Incorporating Autonomous Lane-Changing Mechanism for Intelligent Vehicles

MPC-Based Trajectory Tracking Control Strategy Incorporating...

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Conference on Vehicle control and Intelligence (CVCI)

作者： Dongxue Zhang Huiwen Liu Xiaohong Jiao Ting Zhang Hebei Key Laboratory of Industrial Computer Control Engineering Yanshan University Qinhuangdao Hebei Province China School of Vehicle and Mobility Tsinghua University Beijing China

ISBN: (数字)9798331504892

ISBN: (纸本)9798331504908

Navigating the intricate traffic environment, lane-changing has emerged as a frequent and essential driving maneu-ver for intelligent vehicles. However, it is challenging to guarantee the safety of the intelligent vehicle and the trajectory tracking accuracy against the external environment during lane-changing. Considering these challenges to overcome, the model predictive control (MPC)-based trajectory tracking strategy incorporating an autonomous lane-changing mechanism is proposed in this paper. The autonomous lane-changing mechanism considering lane-changing intention is proposed to ensure that the intelligent vehicle generates safe lane-changing decisions and trajectories. Subsequently, the MPC strategy that considers the physical char-acteristics of the intelligent vehicle is proposed to guarantee the accuracy and smoothness of trajectory tracking. The validation of its effectiveness and advantages is established on the PreScan and MATLAB/Simulink co-simulation platforms.

关键词： Accuracy Intelligent vehicles Trajectory tracking Navigation Vehicle safety Trajectory MATLAB Predictive control

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Bimodal Fusion Network for Basic Taste Sensation Recognition from Electroencephalography and Electromyography

Bimodal Fusion Network for Basic Taste Sensation Recognition...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Han Gao Shuo Zhao Huiyan Li Li Liu You Wang Ruifen Hu Jin Zhang Guang Li State Key Laboratory of Industrial Control Technology Zhejiang University School of Computer and Communication Engineering Changsha University of Science and Technology

Taste sensation can be objectively measured using electroencephalography (EEG) or electromyography (EMG). How-ever, it is still challenging to effectively utilize the complementary information from EEG and EMG signals in taste sensation recognition. This paper proposes a bimodal fusion network (Bi-FusionNet) for recognizing basic taste sensations (sour, sweet, bitter, salty, umami, and blank). Two convolutional backbones with similar structures are designed to separately extract the single-modal features of EEG and EMG. Then, EEG and EMG features are concatenated for bimodal interaction and complementarity. Finally, three loss functions are adopted: a center loss for aggregating intra-class samples, a mean squared error loss for sequence positions for minimizing the difference between signals during the stimulation, and a softmax loss for minimizing the entropy of prediction and true labels. The results on the taste sensation dataset show that bimodal fusion improves recognition performance, and Bi-FusionNet outperforms single-modal methods and other fusion methods. Bi-FusionNet paves the way for the application of multimodal fusion in taste sensation recognition.

关键词： Convolution Speech recognition Brain modeling Feature extraction Electromyography Electroencephalography Entropy

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Fast convergent on-board Li-ion battery state of charge estimation via back-forth moving horizon estimation

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IEEE Transactions on Power Electronics 2025年第9期40卷 13187-13201页

作者： Liu, Zhihao Xiao, Yu Yuan, Yuan Xu, Xiaodong Luo, Biao Huang, Tingwen Ministry of Education School of Automation and Key Laboratory of Industrial Intelligence and Systems Central South University Changsha410083 China Shenzhen University of Advanced Technology Faculty of Computer Science and Control Engineering Shenzhen China

Accurate state of charge (SOC) estimation is crucial for the safe operation of lithium-ion batteries (LIBs), yet existing methods are limited by sensitivity to initial SOC guess or high computational complexity. To address these challenges, this study proposes an onboard back-forth moving horizon estimation (BFMHE) framework for SOC estimation, achieving high precision, robustness, and computational efficiency compatible with microcontroller implementation. An efficient linear equivalent circuit model (ECM) is established, where the open-circuit voltage-SOC relationship is approximated by piecewise linear functions. The proposed framework employs two MHE estimators that iteratively operate in opposite directions over a finite time horizon, which enables rapid convergence. Moreover, this study provides a rigorous proof of the stability of the BFMHE method for the battery system with current input. The proposed approach is validated experimentally under various operating conditions and with different initial SOC guesses. Experimental results show that the proposed onboard BFMHE method achieves a maximum estimation error of less than 1% and quickly converges to the true SOC value, demonstrating strong robustness to initial estimation errors. © 1986-2012 IEEE.

关键词： Lithium-ion batteries

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Global-Local Context Enhanced Yolo for Small Object Detection in Uav Images

SSRN

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SSRN 2025年

作者： Wei, Lixin Han, Kangshuai Li, Xin Che, Haijun Hu, Ziyu Engineering Research Center of the Ministry of Education for Intelligent Control System and Intelligent Equipment Yanshan University Hebei Qinhuangdao066000 China Key Lab of Industrial Computer Control Engineering of Hebei Province Yanshan University Hebei Qinhuangdao066000 China

Object detection using Unmanned Aerial Vehicles (UAVs) has emerged as a criti cal application across diverse domains. However, the wide-angle views of drones often result in images containing a high density of small objects, posing chal lenges for object detection such as few learnable features, significant occlusion, and an imbalanced distribution of positive and negative samples. To address these issues, this paper introduces AGLC-YOLO, an enhanced version of the YOLOv7 architecture specifically designed for detecting small objects in UAV images. AGLC-YOLO integrates global and local context information through a Attention guide Global-Local Context Information Extraction (AGLC) module. This module employs parallel dilated convolutions to capture local context infor mation and a transformer-based structure to extract global dependencies, which are then fused using an improved attention mechanism. The network also adds an additional small object detection head to enrich the small object information in the model. Additionally, AGLC-YOLO utilizes an auxiliary bounding box in con junction with the Inner-Wise Intersection over Union (Inner-WIoU) loss function to accelerate the bounding box regression process and improve detection accu racy. Experimental results on the VisDrone and ManipalUav datasets demonstrate that AGLC-YOLO achieves significant improvements over the baseline YOLOv7 model, with anincrease of 3%inAP50and2.7%inAP95ontheVisDronedataset, and 1.9% in AP50 and 2% in AP95 on the ManipalUav dataset. © 2025, The Authors. All rights reserved.

关键词： Convolution

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CAMInterHand: Cooperative Attention for Multi-View Interactive Hand Pose and Mesh Reconstruction

CAMInterHand: Cooperative Attention for Multi-View Interacti...

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IEEE International Conference on Robotics and Automation (ICRA)

作者： Guwen Han Qi Ye Anjun Chen Jiming Chen College of Computer Science and Technology Zhejiang University Hangzhou China College of Control Science and Engineering Zhejiang University Hangzhou China The College of Control Science and Engineeringthe State Key Laboratory of Industrial Control Technology Zhejiang University and the Key Key Lab of CS&AUS of Zhejiang Province

ISBN: (数字)9798350384574

ISBN: (纸本)9798350384581

Interactive hand mesh reconstruction from singleview images poses a significant challenge with the severe occlusion and depth ambiguity inherent in interactive hand gestures. Recent approaches that employ probabilistic models and tokenpruned techniques have shown decent results in multi-view human body reconstruction. Nevertheless, these methods have not fully utilized multi-scale semantic information from multiview images and are not applicable in scenarios involving severe occlusion during dual-hand interactions. Simultaneously, current single-view methods independently reconstruct the left and right hands, which are ineffective in enhancing the interaction between both hands. To address these challenges, we propose CAMInterHand, a cooperative attention-based method for multi-view interactive hand pose and mesh reconstruction. Specifically, CAMInterHand extracts local pyramid features and global vertex features from multi-scale feature maps of multi-view images, enabling the exploration of rich local semantic information and facilitating effective feature alignment. Furthermore, CAMInterHand employs the cooperative attention fusion module to fuse all features from multi-view images, enhancing interactions among vertices of dual hands within global and local contexts. We conduct extensive experiments on the large-scale multi-view dataset InterHand2.6M and CAMInterHand achieves a substantial performance improvement over existing methods for multi-view and single-view interactive hand reconstruction.

关键词： Fuses Semantics Feature extraction Probabilistic logic Data mining Robotics and automation Image reconstruction

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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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Rollover Prevention control for Autonomous Vehicles with High Centers of Gravity Considering Input Saturation

Rollover Prevention Control for Autonomous Vehicles with Hig...

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第43届中国控制会议

作者： Linying Lu Ting Zhang Lin Qi Jiayi Fang Xiaohong Jiao School of Electrical Engineering Yanshan University Key Lab of Industrial Computer Control Engineering of Hebei Province Yanshan University School of Vehicle and Mobility Tsinghua University School of Mechanical Engineering Beijing Institute of Technology

ISBN: (数字)9789887581581

ISBN: (纸本)9798350366907

A fixed-time terminal sliding mode control(FTSMC) strategy based on extended state observer for cooperative control of rollover prevention and trajectory tracking is proposed to enable the high-center-of-gravity autonomous vehicles to ensure tracking performance and prevent rollover. The FTSMC allows the errors to converge globally to a predefined arbitrarily small set centered at zero in a fixed time and then exponentially converge to zero while weakening the influence of input saturation on the controller by compensating the auxiliary system of input saturation. Simulation comparisons with existing results verify the effectiveness and advantages of the designed strategy under the MATLAB/Simulink environment.

关键词： Autonomous Vehicles Fixed-time Rollover Prevention Input Saturation

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