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A Weighting Factors Autotuning Strategy for Model Predictive Torque control of Permanent Magnet Synchronous Motor Based on Adaptive Multi-objective Black Hole Algorithm

A Weighting Factors Autotuning Strategy for Model Predictive...

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第42届中国控制会议

作者： Zixiang Zhou Tao Wu School of Automation China University of Geosciences Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems System Integration and Industrial Robotics Laboratory

The rational design of weighting factors in the cost function for finite control set model predictive torque control(FCS-MPTC) has been a matter of great interest in power electronics and electrical drives. In order to solve this problem, a weighting factors autotuning strategy for FCS-MPTC of permanent magnet synchronous motor(PMSM) based on the adaptive multi-objective black hole algorithm(AMOBH) is proposed. In this paper, the design process of the FCS-MPTC algorithm is first analyzed in detail. Then, an AMOBH algorithm that can take into account both population convergence and population diversity is introduced, and based on this algorithm, the design problem of the weighting factors is successfully transformed into a multiobjective optimization problem by means of reconstructing the cost function and designing the motor operation information collected in real time as the objective functions of the multi-objective optimization algorithm. Simulation results show that the proposed method can find a set of weighting factor combinations suitable for different working condition requirements, and these weighting factors can effectively improve the operation performance of the PMSM system.

关键词： Model Predictive Torque control Weighting Factors Multi-objective Optimization Algorithm Permanent Magnet Synchronous Motor

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H∞ state estimation for delayed neural networks via variable-augmented-based free-weighting matrices method

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Journal of the Franklin Institute 2025年第7期362卷

作者： Chang, Xu-Kang He, Yong School of Automation China University of Geosciences Wuhan430074 China Hubei key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan430074 China Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Wuhan430074 China

The issue of H∞ state estimation for neural networks with time-varying delays is investigated in this study. Firstly, an augmented Lyapunov-Krasovskii functional (LKF) with two delay-product-type terms is constructed to enhance the consideration of system state, delay, and delay derivative information. Furthermore, for taking into account more effective information, the LKF is augmented with both single and double integral variables. Accordingly, the LKF derivative becomes a higher-order term that exhibits nonlinear characteristics of the time delay. To solve the nonlinear problem, a variable-augmented-based free-weighting-matrices (VAFWMs) approach is employed to transform the nonlinear term into a linear form and provides more freedom in obtaining less-conservative results. Consequently, two improved H∞ state estimation criteria are derived. Lastly, a numerical example is provided to demonstrate the merits and effectiveness of the presented methods. Meanwhile, to prove the practical viability of the presented methods, this study extends the methods to a real-world quadruple-tank process system. © 2025 The Franklin Institute

关键词： Lyapunov functions

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Temperature Rising Model of Tubular Permanent Magnet Linear Synchronous Motor Based on Transfer Learning-Deep Neural Network

Temperature Rising Model of Tubular Permanent Magnet Linear ...

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第42届中国控制会议

作者： YiFei Wang Tao Wu School of Automation China University of Geosciences Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems System Integration and Industrial Robotics Laboratory

High temperature rise of permanent magnet linear synchronous motor can lead to irreversible demagnetization of the motor permanent magnet, which can negatively affect the motor performance. To address this problem, a thermal modeling analysis method based on Transfer learning-Deep neural network(TL-DNN) was proposed in this paper. Its specific implementation steps include(1) corresponding to different heat source inputs, the equivalent thermal circuit method and the finite element analysis was adopted based on the structure and main parameters of PMLSM including overall average temperature rise, coil temperature rise and permanent magnet temperature rise from the data sets of the motor;(2) TL-DNN was used to fit the functional relationship between the input source features and the output targets based on the characteristics of the sample data sets. In order to verify the accuracy of the prediction model with small sample data sets, this paper divided the proportion of the data set and compared the results with other classic nonparametric models(random forest, support vector machine, and deep neural network). The results show that the TL-DNN model outperforms other machine learning models and has better robustness and generalization ability when the training datasets are insufficient, and achieves an organic combination of physical field path model and data-driven model, which provides a feasible solution for PMLSM modeling in the case of small samples.

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Bio-soft matter derived from traditional Chinese medicine:Characterizations of hierarchical structure,assembly mechanism,and beyond

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Journal of Pharmaceutical Analysis 2024年第6期14卷 823-839页

作者： Guiya Yang Yue Liu Yuying Hu Yue Yuan Yunan Qin Quan Li Shuangcheng Ma School of Traditional Chinese Medicine Beijing University of Chinese MedicineBeijing102488China Institute for Control of Chinese Traditional Medicine and Ethnic Medicine National Institutes for Food and Drug ControlBeijing100050China Tianjin Key Laboratory of Therapeutic Substance of Traditional Chinese Medicine School of Chinese Materia MedicaTianjin University of Traditional Chinese MedicineTianjin301617China Collaborative Innovation Center for Advanced Organic Chemical Materials Co-constructed by the Province and Ministry Ministry-of-Education Key Laboratory for the Synthesis and Application of Organic Functional MoleculesCollege of Chemistry and Chemical EngineeringHubei UniversityWuhan430062China

Structural and functional explorations on bio-soft matter such as micelles,vesicles,nanoparticles,aggregates or polymers derived from traditional Chinese medicine(TCM)has emerged as a new topic in the field of *** discovery of such cross-scaled bio-soft matter may provide a unique perspective for unraveling the new effective material basis of TCM as well as developing innovative medicine and *** the rapid rise of TCM-derived bio-soft matter,their hierarchical structure and assembly mechanism must be unambiguously probed for a further in-depth understanding of their pharmacological *** this review,the current emerged TCM-derived bio-soft matter assembled from either small molecules or macromolecules is introduced,and particularly the unambiguous elucidation of their hierarchical structure and assembly mechanism with combined electron microscopic and spectroscopic techniques is *** pros and cons of each technique are also *** future challenges and perspective of TCM-derived bio-soft matter are outlined,particularly the requirement for their precise in situ structural determination is highlighted.

关键词： Electron microscopy Spectroscopy Traditional Chinese medicine Bio-soft matter Structural characterization

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Research on Dust Removal Efficiency for Photovoltaic Panel Cleaning Robot Based on DDPM 5

Research on Dust Removal Efficiency for Photovoltaic Panel C...

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5th International Symposium on Robotics and intelligent Manufacturing Technology, ISRIMT 2023

作者： Zong, Yuqian Zhao, Gang He, Kai Zhang, Feifei Chen, Junwen Key Laboratory of Metallurgical Equipment and Control Technology Wuhan University of Science and Technology Ministry of Education Wuhan430081 China Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen518055 China Hubei Key Laboratory of Mechanical Transmission and Manufacturing Engineering Wuhan University of Science and Technology Wuhan430081 China Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen Key Laboratory of Precision Engineering Shenzhen518055 China Key Laboratory of Automotive Power Train and Electronics Hubei University of Automotive Technology Hubei Shiyan442002 China

ISBN: (纸本)9798350316087

To address the challenge of cleaning rooftop photovoltaic panels with water, an innovative waterless dust removal cleaning robot has been designed. This study simulates the movement trajectory of gas-solid two-phase flow containing dust particles within a sealed enclosure based on Dense Discrete Phase Model (DDPM) of Fluent. The L16 Taguchi experimental method is adopted to analyze the dust removal characteristics under different conditions, including exit directions of the sealed enclosure, brush rotational speed and brush rotational direction. The particle velocity and the particle quantity are obtained to analyze the dust removal efficiency. The research shows that the impact of each processing parameter is ranked as follows: brush rotational speed, exit direction and brush rotational direction. The optimal dust removal efficiency is above 85% when the outlet is at the rear of the sealed enclosure, and the brush rotates clockwise at a speed of 5r/s. © 2023 IEEE.

关键词： Enclosures

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Efficient Facial Expression Recognition Network Based on Spot-Adaptive Knowledge Distillation 26

Efficient Facial Expression Recognition Network Based on Spo...

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26th International Conference on Industrial Technology, ICIT 2025

作者： Wen, Xin Liu, Zhen-Tao Chen, Dan School of Automation China University of Geosciences Wuhan430074 China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education China

ISBN: (纸本)9798331521950

Facial expression recognition plays a key role in promoting the development of comprehensive intelligence and building friendly human-computer interaction. Due to the interference of feature noise in expression data, the lightweight facial expression recognition model with fewer parameters is difficult to learn more expression features through simple training, which limits the improvement of its recognition performance. An efficient facial expression recognition network based on Spot-adaptive Knowledge Distillation is proposed in this paper. Inspired by VoVNetV2, the network designed in this paper is lightweightly improved using Depthwise Separable Convolution and the parameter-free SimAM attention mechanism, reducing the number of parameters to 0.21 M. To further improve the recognition accuracy of the model, Spot-adaptive Knowledge Distillation is employed to improve the characterization ability of the model. The recognition accuracies of the student network designed in this paper on the KDEF and RAF-DB datasets are 93.05% and 81.17% respectively after spot-adaptive distillation. © 2025 IEEE.

关键词： Facial Expression Recognition Knowledge Distillation Lightweight Neural Network

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Analysis of MFCS topology for high efficiency and long lifetime 43

Analysis of MFCS topology for high efficiency and long lifet...

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43rd Chinese control Conference, CCC 2024

作者： Xiang, Hongfu Wu, Jiajian Wang, Zhen Wang, Jie Li, Xi Huazhong University of Science and Technology School of Artificial Intelligence and Automation Key Laboratory of Image Processing and Intelligent Control of Education Ministry Hubei Wuhan430074 China Shenzhen Huazhong University of Science and Technology Research Institute Guangdong Shenzhen518055 China

ISBN: (纸本)9789887581581

Multi-stack fuel cell system(MFCS) are an important basis for large-scale application of solid oxide fuel cell(SOFC) technology, MFCS can provide higher system power and longer service life. As the number of stacks increases, different topologies can be formed between stacks, including parallel structures and series structures, and the physical characteristics of MFCS will also change. In this study, the degradation of the stacks is first considered, and the system model of series structure and parallel structure including the degradation of the stacks is built. The parallel structure proposed in this paper is the key to ensure the high efficiency and long lifetime of MFCS. Through the simulation of systems with different topologies, it is found that the power generation efficiency of the systems caused by the topologies varies greatly in different output power stages. The efficiency of the parallel structure in the low power stage is significantly better than that of the series structure, and the parallel structure is more conducive to the high efficiency of the system in the whole stage. Different topological structures also have a greater impact on the degradation of stacks. The degradation rates of stacks in parallel structures are close to each other, and the state of stacks is more consistent. The overall life of the system in parallel structures is also better than that in series structures. © 2024 Technical Committee on control Theory, Chinese Association of Automation.

关键词： Topology

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Fixed-time Sliding Mode control for Buck-Boost Converter 4

Fixed-time Sliding Mode Control for Buck-Boost Converter

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2022 4th International Conference on Energy Systems and Electrical Power, ICESEP 2022

作者： Zhou, Yusheng Liu, Shilin Wang, Ning Key Laboratory of Advanced Perception and Intelligent Control of High-end Equipment Ministry of Education Anhui Polytechnic University Wuhu241000 China

To obtain stable state within the fixed time and reduce the influence of uncertain device parameters on the buck-boost converter, this paper proposes the fixed-time sliding mode control (FSMC) and fixed-time adaptive sliding mode control (FASMC). Firstly, we build the state-space average model on the basis of the continuous conduction mode (CCM) of the buck-boost converter. Secondly, the fixed-time sliding mode surface and FSMC are studied when the device parameters are known. By analyzing the Lyapunov stability, it has been proved that the system can reach a stable state within the fixed time, besides the maximum of the fixed time is merely determined by the parameters of the control strategy. Thirdly, when the system device parameters are unknown, the FASMC is developed ensuring the system can reach the adjacent domain of the equilibrium point within the fixed time. Finally, the effectiveness of the proposed control strategies was verified by the simulation experiments. © 2022 Institute of Physics Publishing. All rights reserved.

关键词： Buck-Boost converter

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Semantic Segmentation of Indoor Point Clouds with the Channel Attention Mechanism

Semantic Segmentation of Indoor Point Clouds with the Channe...

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2022 Chinese Automation Congress, CAC 2022

作者： Liu, Wei Lu, Yisheng Zhang, Tao Chen, Jun School of Automation China University of Geosciences Wuhan430074 China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan430074 China Engineering Research Center of Intelligent Technology for Geoexploration Ministry of Education Wuhan430074 China

ISBN: (纸本)9781665465335

In recent years, the semantic segmentation of 3D point cloud has received increasing attention the field of computer vision, because 3D point cloud can better reflect our 3D space. Because of the unstructured and disordered characteristics of 3D point cloud data, semantic segmentation of point cloud is still a difficult task. Our network automatically learns the importance of feature channels by adding a channel attention module, which enables the network to obtain better training results. After the channel attention module is fused, the important channels in the features are enhanced, and the unimportant channels are suppressed, making the network training more efficient. In this paper, we propose an indoor point cloud semantic segmentation method combined with channel attention mechanism. The experimental results show that our method achieves better results than other methods. © 2022 IEEE.

关键词： Semantic Segmentation

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Visual localization and map construction based on ground texture 19th

Visual localization and map construction based on ground tex...

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19th Chinese intelligent Systems Conference, CISC 2023

作者： Chen, Xin Yu, Lei School of Mechanical and Electric Engineering Soochow University Suzhou China Key Laboratory of Opto-Technology and Intelligent Control Ministry of Education Lanzhou Jiaotong University Lanzhou China Jiangsu Key Laboratory of Advanced Manufacturing Technology Huaiyin Institute of Technology Huai’an China

ISBN: (纸本)9789819968817

This paper describes a visual localization and map construction method based on ground texture. The ground texture is scanned twice in a zigzag pattern and divided into a grid to avoid distortion of the stitched image. A global optimization method is used to calculate the image pose by reprojection constraint and key frame selection to stitch the global map. For positioning, the global positioning is transformed into a local positioning process through global search and local optimization, which improves the speed and accuracy of positioning. For map updating, local optimization is performed by constructing loopbacks for possible stain occlusion or breakage problems to solve tracking loss and repositioning problems. Experimental results show that the ground texture stitching and localization results after global optimization match well with the true values, and the global maps constructed by stitching are aligned with the true physical locations. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023.

关键词： Textures

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