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作者： Shen, Yongpeng Wu, Kexuan Yuan, Xiaofang Wu, Chengzhong Liang, Weihua Chen, Zhiwei The College of Electric and Information Engineering Zhengzhou University of Light Industry Zhengzhou450002 China The Department of Electric and Information Engineering Hunan University Changsha410082 China The National Engineering Laboratory for Robot Visual Perception and Control Technology Hunan University Changsha410082 China

The DC link current sampling with a single sensor is a promising solution in the electric drive field due to its low cost and small volume. However, limited by the minimum sampling time, the current reconstruction unobservable area is the most intractable challenge in the traditional single sensor reconstruction method, especially in sector boundary areas, where the phase current cannot be reconstructed completely. In this article, a new non-zero compensation vector pulse width modulation (NCV-PWM) method is proposed to eliminate the current unobservable area. Firstly, by introducing the definition of minimum sampling time, the voltage space vector plane is accurately divided into different modulation areas. Then, by applying non-zero compensation vector in the sector boundary, the current sampling window is extended, and the sampling time requirement for the DC link current is satisfied. Finally, the practicability and accuracy of the NCV-PWM method are verified by experiments, and results show that the current reconstruction error is less than 2.86%, which provides accurate current feedback for high-performance motor control. © 2023 IEEE.

关键词： Electric drives

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Adaptive Obstacle Avoidance of UAV Leader/Followers with Nonlinear Logarithm Quaternion control

Adaptive Obstacle Avoidance of UAV Leader/Followers with Non...

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International Conference on Methods and Models in Automation and Robotics (MMAR)

作者： Huda Naji Al-Sudany Béla Lantos Department of Control Engineering and Information Technology Faculty of Electrical Engineering and Informatics Budapest University of Technology and Economics Budapest Hungary

ISBN: (数字)9798350362343

ISBN: (纸本)9798350362350

This paper addresses a significant challenge within the realm of fixed wing Unmanned Aerial Vehicles (UAVs), namely obstacle avoidance of the Leader-Follower system. The proposed approach involves formulating the Leader-Follower problem, consisting of one leader and three followers, with control strategies utilizing the logarithm exponential Quaternion method. Subsequently, the designed control law employing quaternions is applied to both the leader and the follower UAVs. Moreover, a notable contribution of this research lies in the incorporation of adaptive obstacle avoidance. The idea based on potential field as obstacle avoidance algorithm, with adaptive techniques for determining obstacle avoidance parameters. These parameters are dynamically adjusted based on the distance between the UAV and the obstacle through a non-linear adaptation function. The comprehensive implementation of these methods is rigorously evaluated by employing a predefined reference path for the leader and conducting numerous scenario-based tests. Random directions of followers were used in case of stuck in local minimum to avoid it; this was applied in case when potential filed force is less than small value. Experiments to test some scenarios of obstacle avoidance were conducted. The final results conclusively demonstrate the stability of the control law even in the presence of obstacles. The effectiveness of the adaptive obstacle avoidance approach is clearly evident from the trajectories of the UAV followers.

关键词： Adaptation models Automation Quaternions Heuristic algorithms Force Autonomous aerial vehicles Stability analysis

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Secure-by-Construction Synthesis for control Systems

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IEEE Transactions on Automatic control 2025年第6期70卷 4170-4177页

作者： Zhong, Bingzhuo Liu, Siyuan Caccamo, Marco Zamani, Majid Thrust of Artificial Intelligence Information Hub The Thrust of Intelligent Transportation System Hub China KTH Royal Institute of Technology Division of Decision and Control Systems Stockholm Sweden Technical University of Munich TUM School of Engineering and Design Germany University of Colorado Boulder Department of Computer Science United States Ludwig Maximilian University Department of Computer Science Munich Germany

In this note, we present the synthesis of secure-by-construction controllers that address safety and security properties simultaneously in cyber-physical systems. Our focus is on studying a specific security property called opacity, which characterizes the system's ability to maintain plausible deniability of its secret behavior in the presence of an intruder. These controllers are synthesized based on a concept of so-called (augmented) control barrier functions. We propose conditions that facilitate the construction of the desired (augmented) control barrier functions and their corresponding secure-by-construction controllers. To compute these functions, we propose an iterative scheme that leverages iterative sum-of-square programming techniques. This approach enables efficient computation of these functions, particularly for polynomial systems. Finally, we validate the effectiveness of our results through a case study of a vehicle. © 1963-2012 IEEE.

关键词： Iterative methods

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Significant effects of transport on nanoparticles during new particle formation events in the atmosphere of Beijing

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Particuology 2023年第9期80卷 1-10页

作者： Dongjie Shang Min Hu Lizi Tang Xin Fang Ying Liu Yusheng Wu Zhuofei Du Xuhui Cai Zhijun Wu Shengrong Lou Mattias Hallquist Song Guo Yuanhang Zhang State Key Joint Laboratory of Environmental Simulation and Pollution Control College of Environmental Sciences and EngineeringInternational JointLaboratory for Regional Pollution ControlMinistry of Education(IJRC)Peking UniversityBeijing100871China Collaborative Innovation Center of Atmospheric Environment and Equipment Technology Nanjing University of Information Science&TechnologyNanjing210044China Department of Chemistry and Molecular Biology University of GothenburgGothenburg41296Sweden Shanghai Academy of Environmental Sciences Shanghai200233China

The mechanisms of new particle formation(NPF)events that occurred under high aerosol loadings(“polluted”NPF)in the atmosphere have been unclear,which has inhibited the precision of particlepollution *** deepen the understanding of how the“polluted”NPF events occur,a one-monthcomprehensive measurement was conducted in the atmosphere of Beijing during the summer ***“clean”NPF events(frequency=22%)(condensation sink,CS<0.015 s^(-1))were found to becaused by local nucleation and ***“polluted”NPF events(frequency=28%)(CS>0.015 s^(-1))were influenced by both local nucleation-growth and regional transport,and the contributions from thetwo factors to 6e25 nm particle number concentration were 60%and 40%,*** studyemphasized the importance of the transport for nanoparticles in relatively polluted atmospheres,and forthat the regional joint particle pollution control would be an essential policy.

关键词： New particle formation Transport Nanoparticles Sulfuric acid

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Short-Term Traffic Flow Prediction of Highway Tunnels Based on SSA and Improved TSK Fuzzy System

Short-Term Traffic Flow Prediction of Highway Tunnels Based ...

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2023 China Automation Congress, CAC 2023

作者： Huang, Zhijing Chen, Kang Xu, Zhezhuang Zeng, Qinbo Xu, Zhibin College of Electrical Engineering and Automation Fuzhou University Key Laboratory of Industrial Automation Control Technology and Information Processing Education Department of Fujian Province Fuzhou350000 China

ISBN: (纸本)9798350303759

Aiming at the problem that the traffic flow has an low prediction accuracy due to varying randomness and high nonlinearity, an improved TSK fuzzy neural network prediction model by singular spectrum analysis is ***, the original traffic flow time series can be decomposed into several subseries by singular spectrum analysis (SSA). Then, the Takagi-Sugeno-Kang (TSK) fuzzy system was trained using simulated annealing genetic algorithm (SAGA) and minibatch gradient descent method (MBGD) combined with three techniques (RDA) of regularization, DropRule and *** prediction model of TSK fuzzy neural network is established to predict each subseries independently, and the forecasted traffic flow is obtained by superimposing the prediction results of each subseries. Finally, the traffic flow dataset of expressway tunnels in a city is used to verify the effectiveness of the model. Experimental results can show high traffic flow prediction accuracy of the model, and the model can accurately predict multiple different highway tunnels. © 2023 IEEE.

关键词： Forecasting

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Prediction of Back-layering Length and Critical Velocity with Lithium-ion Battery Car Fires in Tunnel

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IAENG International Journal of Applied Mathematics 2024年第6期54卷 1033-1037页

作者： Bai, Zhenpeng Zhao, Xiaohan Song, Huaitao Zhang, Yang Yao, Haowei Zhang, Jin Department of Zhengzhou Key Laboratory of Electric Power Fire Safety College of Building Environment Engineering Zhengzhou University of Light Industry China Department of Financial Management Henan Light Industry Vocational College Zhengzhou China Department of College of Building Environment Engineering Zhengzhou University of Light Industry China Department of School of Electrical and Information Engineering Henan University of Engineering China Department of College of Building Environment Engineering Zhengzhou University of Light Industry Zhengzhou450002 China Department of Beijing Key Laboratory of Control Technology for City Toxic and Combustible Major Hazards Institute of Urban Safety and Environmental Science Beijing Academy of Science and Technology Beijing100054 China

The purpose of this article is is to investigate the critical speed and back-layer length of smoke propagation during fires of lithium-ion battery vehicles in tunnels. The combustion of lithium-ion battery vehicles in tunnels is numerically simulated. This article uses heat release rate (HRR) and ventilation rate to predict the length of smoke counterflow in tunnel fires with new energy vehicles. Temperature is an important parameter for calculating smoke counterflow. The temperature in the tunnel is measured by thermocouples. The results showed that dimensionless critical velocity influences dimensionless HRR. The numerical simulation results indicated that there is a certain correlation between the HRR variable, which corresponds to the dimensionless smoke counterflow length and the critical speed in the tunnel. This article proposed a computational model for predicting the length of smoke counterflow. This paper contributes to the prevention of tunnel fires and reduction of social and economic losses. Therefore, this paper guides the propagation of smoke under the roof of lithium-ion battery vehicle fires in tunnels. © (2024), (International Association of Engineers). All rights reserved.

关键词： Ions

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A Novel Fuzzy PIDF Enhancing PIDF controller Tuned in Two Stages by TLBO and PSO Algorithms for Reliable AVR Performance

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IEEE Access 2025年 13卷 93505-93522页

作者： Shouran, Mokhtar Alenazi, Mohammed Libyan Centre for Engineering Research and Information Technology Bani Walid Libya College of Electronic Technology Department of Control Engineering Bani Walid Libya Cardiff University School of Engineering Cardiff United Kingdom

Reliability in control engineering remains a fundamental consideration in the design of controllers for various applications. This study proposes an innovative control design for Automatic Voltage Regulator (AVR) that integrates Fuzzy Proportional-Integral-Derivative with Filter action (Fuzzy PIDF) enhancing PIDF controllers (PIDF + Fuzzy PIDF), which is tuned in a two-stage framework. This framework leverages Teaching-Learning-Based Optimization (TLBO) and Particle Swarm Optimization (PSO) algorithms to find the optimum values of the PIDF and Fuzzy-PIDF controllers' gains, respectively. The dual-stage design ensures that the controller maintains functionality even if one stage experiences partial failure, thereby enhancing the reliability of both the controller and the system under control. The tuning process for both stages employs the well-known Integral Time Absolute Error (ITAE) cost function in conjunction with the TLBO and PSO algorithms. The reliability of the proposed controller is rigorously tested, demonstrating its ability to deliver robust performance even when operating partially. A comprehensive comparative analysis with existing techniques in the literature demonstrates the superior performance of the proposed PIDF plus Fuzzy PIDF controller. The results show significant improvements in transient response metrics: overshoot is reduced from 0.1559 pu to 0.0211 pu, undershoot from 0.0954 pu to 0.006 pu, and settling time from 0.7568 seconds to 0.2299 seconds. These advancements underscore the controller's enhanced dynamic stability and precision compared to prior methodologies. Additionally, the robustness of the PIDF + Fuzzy-PIDF controller is evaluated under conditions of system uncertainty and varying load disturbances, with validation conducted in the MATLAB and Simulink environments. The results affirm the controller's superiority, robustness, and reliability, establishing its potential for successful implementation in real-world applications. Thi

关键词： Proportional control systems

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Surface Defect Detection of Wooden Boards Based on Multi-Modal Fusion

Surface Defect Detection of Wooden Boards Based on Multi-Mod...

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2023 China Automation Congress, CAC 2023

作者： Zhang, Hongchuan Zheng, Song Zhu, Yuhang Lin, Ye Ai, Zhijie Xu, Zhezhuang Fuzhou University Key Laboratory of Industrial Automation Control Technology and Information Processing College of Electrical Engineering and Automation Education Department of Fujian Province Fuzhou350000 China

ISBN: (纸本)9798350303759

Automated wood defect detection is of great significance for improving wood utilization. However, the stains on the wood surface resemble defect features and it is difficult to distinguish the appearance of wane from defect-free regions. Relying solely on machine vision for extracting surface features of wood makes it difficult to achieve accurate defect detection. To address these challenges, we propose MMDNet, a multimodal detection network that combines point cloud and image data. By adaptively fusing point cloud and image features at multiple stages, the network effectively highlights wood surface defect regions. Additionally, we introduce an Atrous Spatial Pyramid Pooling (ASPP) module into the network, which expands the network's receptive field and enables a comprehensive perception of the defects. Furthermore, we employ a deep supervision strategy to encourage the network to learn discriminative feature representations, enhancing the model's ability to differentiate defect regions. Experimental results validate the effectiveness of our method in detecting defects that are similar to the background and reducing interference from stains. © 2023 IEEE.

关键词： Surface defects

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Extended NNs-Based Adaptive Event-Triggered Asymptotic control for Nonlinear Systems Against Actuator Faults 7

Extended NNs-Based Adaptive Event-Triggered Asymptotic Contr...

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7th International Symposium on Autonomous Systems, ISAS 2024

作者： Liu, Shanlin Zhang, Yingwei Zhao, Xudong Wang, Hesheng Northeastern University College of Information Science and Engineering Shenyang China Northeastern University State Laboratory of Synthesis Automation of Process Industry Shenyang China Dalian University of Technology Faculty of Electronic Information and Electrical Engineering Dalian China Department of Automation Key Laboratory of System Control and Information Processing of Ministry of Education Key Laboratory of Marine Intelligent Equipment and System of Ministry of Education Shanghai Engineering Research Center of Intelligent Control and Management Shanghai Jiao Tong University Shanghai China

ISBN: (纸本)9798350363173

In this article, an adaptive event-triggered asymp¬totically fault-tolerant control (FTC) issue for nonlinear systems with actuator faults is investigated. An extended neural networks (NNs) technique is introduced to estimate unknown functions of the system under consideration. Compared with traditional NNs that ignore approximation errors, the proposed extended NNs further processes approximation errors, reducing the conservatism of the proposed algorithm. Then, within the framework of backstepping method, a new adaptive control strategy is developed by combining event-triggered mechanism (ETM) and bound estimation (BE) approach, which ensures the asymptotic convergence of system states while also reducing the transmission of unnecessary information, thus improving the utilization of communication resources. At the same time, the designed controller eliminates the adverse effects of actuator failures and system nonlinear terms. Finally, a simulation example of a single-link robot system is provided to demonstrate that the developed control strategy can ensure that all signals are bounded and system states are asymptotically convergent. © 2024 IEEE.

关键词： Approximation algorithms

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An Efficient Medical Image Deep Fusion Model Based on Convolutional Neural Networks

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Computers, Materials & Continua 2023年第2期74卷 2905-2925页

作者： Walid El-Shafai Noha A.El-Hag Ahmed Sedik Ghada Elbanby Fathi E.Abd El-Samie Naglaa F.Soliman Hussah Nasser AlEisa Mohammed E.Abdel Samea Department of Electronics and Electrical Communications Engineering Faculty of Electronic EngineeringMenoufia UniversityMenouf32952Egypt Security Engineering Laboratory Department of Computer SciencePrince Sultan UniversityRiyadh11586Saudi Arabia Higher Institute of Commercial Science Management Information SystemsEl-Mahala El-KobraEgypt Department of the Robotics and Intelligent Machines Faculty of Artificial IntelligenceKafrelsheikh UniversityKafr El-SheikhEgypt Department of Industrial Electronics and Control Engineering Faculty of Electronic EngineeringMenoufia UniversityMenouf32952Egypt Department of Information Technology College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityRiyadhSaudi Arabia Medical Imaging and Interventional Radiology National Liver InstituteMenoufia UniversityEgypt

Medical image fusion is considered the best method for obtaining one image with rich details for efficient medical diagnosis and *** learning provides a high performance for several medical image analysis *** paper proposes a deep learning model for the medical image fusion *** model depends on Convolutional Neural Network(CNN).The basic idea of the proposed model is to extract features from both CT and MR ***,an additional process is executed on the extracted *** that,the fused feature map is reconstructed to obtain the resulting fused ***,the quality of the resulting fused image is enhanced by various enhancement techniques such as Histogram Matching(HM),Histogram Equalization(HE),fuzzy technique,fuzzy type,and Contrast Limited Histogram Equalization(CLAHE).The performance of the proposed fusion-based CNN model is measured by various metrics of the fusion and enhancement *** realistic datasets of different modalities and diseases are tested and ***,real datasets are tested in the simulation analysis.

关键词： Image fusion CNN deep learning feature extraction evaluation metrics medical diagnosis

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