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第43届中国控制会议

作者： Xiang Gu Dewei Li Aoyun Ma Yaru Yu Department of Automation Shanghai Jiao Tong University Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai Engineering Research Center of Intelligent Control and Management

ISBN: (数字)9789887581581

ISBN: (纸本)9798350366907

In the practical application of model predictive control in power electronics, both online computational burden and model accuracy are crucial. This paper presents an explicit model predictive control method based on input-mapping *** address the issue of parameter drift in the forward DC-DC converter system, the input-mapping method is introduced to compensate for model deviations using historical data. Firstly, the average model of the forward DC-DC converter system is introduced. For the control objective of voltage tracking, an explicit predictive control using the incremental model is ***, input-mapping method is integrated with explicit predictive control. During offline computation, analytical forms of the input-mapping combination coefficients and the optimal solution function of explicit model predictive control are *** computation of combination coefficients is conducted to obtain the optimal solution rapidly. The effectiveness of the proposed method is verified through simulations in MATLAB.

关键词： Forward DC-DC converter explicit model predictive control input-mapping method

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Trajectory Optimization and control Method Based on Multistage Nonlinear Model Predictive control 4

Trajectory Optimization and Control Method Based on Multista...

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4th International Seminar on Artificial Intelligence, Networking and Information Technology, AINIT 2023

作者： Zheng, Qingchun Peng, Zhi Zhu, Peihao Zhai, Ran Tianjin University of Technology Tianjin Key Laboratory for Advanced Mechatronic System Design and Intelligent Control Tianjin China School of Mechanical Engineering Tianjin University of Technology Tianjin China Tianjin University of Technology National Demonstration Center for Experimental Mechanical and Electrical Engineering Education Tianjin China

ISBN: (纸本)9798350314526

To improve the trajectory optimization efficiency of robot manipulators, this paper proposes a novel approach for trajectory optimization and control of manipulators based on multistage nonlinear model predictive control (msNLMPC). This approach is realized by using a neural state space model as the prediction model of nonlinear model predictive control (NLMPC). The simulation results show that the actual trajectory of the manipulator in our proposed scheme coincides with the desired trajectory. The manipulator achieves the control task of following the optimal trajectory. Our proposed method improves the trajectory optimization efficiency of the robot manipulator. © 2023 IEEE.

关键词： Model predictive control

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Research on Identification Strategy of Fault-Sensitive Frequency for Planetary Gearboxes

Research on Identification Strategy of Fault-Sensitive Frequ...

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TEPEN International Workshop on Fault Diagnostics and Prognostics, TEPEN-IWFDP 2024

作者： Xie, Ruitong Zhang, Mian Chen, Jiwei Zhu, Songsong Wang, Zhiyuan Zhao, Mengxiong Xiang, Hongbiao Tianjin Key Laboratory of the Design and Intelligent Control of the Advanced Mechanical System Tianjin University of Technology Tianjin300384 China National Demonstration Center for Experimental Mechanical and Electrical Engineering Education Binshuixidao 391 Xiqing District Tianjin300384 China State Key Laboratory of Water Resources Engineering and Management Wuhan University Wuhan430072 China

ISBN: (纸本)9783031694820

Planetary gearboxes find extensive application in wind power generation, aerospace, industrial production and other fields. Harsh operational conditions and prolonged periods of continuous work often lead to various gear damages. Therefore, it is important to continuously monitor the health status of planetary gearboxes. However, the fault-sensitive frequencies of planetary gearboxes used in the existing signal models and fault diagnosis indicators are calculated theoretically or searched artificially. In response to this, this paper proposes a strategy for identifying fault-sensitive frequencies in planetary gearboxes intelligently by combining spectral amplitude analysis with three models: Random Forest (RF), Support Vector Machine (SVM) and Deep Neural Network (DNN). Experiments are carried out to verify the effectiveness and superiority of the proposed strategy. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Deep neural networks

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An Order Demodulation Analysis Method for Planetary Gearboxes

An Order Demodulation Analysis Method for Planetary Gearboxe...

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TEPEN International Workshop on Fault Diagnostics and Prognostics, TEPEN-IWFDP 2024

作者： Chen, Jiwei Xie, Ruitong Zhu, Songsong Zhao, Mengxiong Wang, Zhiyuan Zhang, Mian Tianjin Key Laboratory of the Design and Intelligent Control of the Advanced Mechanical System Tianjin University of Technology Tianjin300384 China National Demonstration Center for Experimental Mechanical and Electrical Engineering Education Binshuixidao 391 Xiqing District Tianjin300384 China State Key Laboratory of Water Resources Engineering and Management Wuhan University Wuhan430072 China

ISBN: (纸本)9783031694820

In addressing challenges such as the speed change of planetary gearbox (PG) during operation and the complexity in extracting fault features, an order demodulation analysis method based on order tracking (OT) and variational mode decomposition (VMD) is proposed. Non-stationary time domain signals are transformed into relatively stable angular domain signals through angular resampling. The intrinsic mode function (IMF) is obtained by demodulation of the angular multi-component signal using the VMD method. The instantaneous order of a proper IMF is calculated via Hilbert Transform. The sensitive single component is selected by matching the meshing order and the central order of the instantaneous order. Finally, the amplitude of the characteristic order components extracted from the instantaneous order spectrum was used as features and Support Vector Machine (SVM) was employed for the classification of four different conditions in the sun gear. Comparing the results of the proposed method with Deep Neural Networks (DNN) and Convolutional Neural Networks (CNN), the experimental results demonstrate the effectiveness of the proposed method in diagnosing PGs under non-stationary conditions. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Epicyclic gears

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control, Modeling and Stability Analysis of DRU-based Grid-forming Converter

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Chinese Journal of Electrical engineering 2025年第1期11卷 83-92页

作者： Wencong Wu Minzhi Wang Liang Tu Haiqing Cai Guanglei Yan Xiaohui Qu Haohan Gu Wei Chen State Key Laboratory of HVDC China Southern Power GridGuangzhou 510663China National Energy Power Grid Technology R&D Center Guangzhou 510663China Guangdong Provincial Key Laboratory of Intelligent Operation and Control for New Energy Power Systems China Southern Power GridGuangzhou 510663China CSG Key Laboratory for Power System Simulation China Southern Power GridGuangzhou 510663China School of Electrical Engineering Southeast UniversityNanjing 210096China

Diode rectifier unit(DRU)-based high-voltage direct current(HVDC)transmissionsystems areeffectivein achieving the stableandeconomical operation of offshore *** theuncontrollable characteristicsof DRUs,a grid-forming(GFM)strategy forwind-turbine converters isnecessary to support offshore AC voltageand ***,the active power-synchronization control in traditional GFM converters is unsuitable for DRU-based GFM ***,the stability issue for DRU-based HVDC systems involving DRU-based GFM and grid-following(GFL)converters has not yet been *** solve these issues,this study begins with the characteristics of a DRU-based HVDC system and presents a control scheme for DRU-based GFM converters for power ***,the dq-frame impedance model of the DRU-based GFM converteris proposed for the stability analysis of the entire HVDC ***,a simulation platform is built to verify the model accuracy and system stability.

关键词： Diode rectifier unit offshore wind-powergeneration grid-forming converter impedance model stabilityanalysis

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Optimal Design for the Torsional Elastic Element Topology of a Rotary Series Elastic Actuator 7

Optimal Design for the Torsional Elastic Element Topology of...

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7th Asia-Pacific Conference on intelligent Robot systems, ACIRS 2022

作者： Yang, Xishun Luo, Wenbo Liu, Haokun Zhang, Sen Ge, Weimin Tianjin University of Technology Tianjin Key Laboratory for Advanced Mechatronic System Design and Intelligent Control National Demonstration Center for Experimental Mechanical and Electrical Engineering Education School of Mechanical Engineering Tianjin300384 China

ISBN: (数字)9781665485197

ISBN: (纸本)9781665485197

The series elastic actuator improves the compliance of exoskeleton by introducing a elastic element and ensures its safe interaction ability. Therefore, it is regarded as a new driving mechanism by scholars. Suitable elastic element is conducive to realize the miniaturization and lightweight of SEA, but its topology design is complex and lack of guidance. Based on the finite element simulation environment and three-dimensional parametric CAD environment, this paper analyzes the influence of different size parameters and topology on the pressure of the elastic element, summarizes the characteristics of three topology structures, and puts forward the optimal design method of a torsional elastic element topology. Through the research, we can conclude that the compression effect of elastic beam with specific shape is better than that of curved elastic beam, and the width of elastic beam has a great influence on the torsional elastic element. © 2022 IEEE.

关键词： Finite element method

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A Nonlinear Disturbance Observer Based on Sliding Mode control for a Lower Limber Exoskeleton Robot 7

A Nonlinear Disturbance Observer Based on Sliding Mode Contr...

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7th Asia-Pacific Conference on intelligent Robot systems, ACIRS 2022

作者： Luo, Wenbo Liu, Haokun Yang, Xishun Zhang, Sen Ge, Weimin Tianjin University of Technology Tianjin Key Laboratory for Advanced Mechatronic System Design and Intelligent Control National Demonstration Center for Experimental Mechanical and Electrical Engineering Education School of Mechanical Engineering Tianjin300384 China

ISBN: (数字)9781665485197

ISBN: (纸本)9781665485197

During the process of human-robot cooperative motion, the internal frictions and modeling errors existing in the system may significantly deteriorate the control precision of a lower limb exoskeleton. To compensate the uncertainty during the human-robot cooperative motion, a nonlinear disturbance observer based sliding mode control strategy is designed in this paper. Firstly, the dynamic model of a lower limb exoskeleton is established. Secondly, low-pass filter and nonlinear disturbance observer are designed to observe the uncertain disturbance and modeling errors. Then, a high-order sliding mode controller is designed to ensure the control precision of the human-robot interaction system. Finally, the effectiveness and reliability of the designed control method are verified with the joint simulation between MATLAB and ADAMS. © 2022 IEEE.

关键词： Sliding mode control

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T-KMGA a Two-Stage Approach for Collaborative Task Allocation with Air-Sea Heterogeneous Autonomous Unmanned systems

T-KMGA a Two-Stage Approach for Collaborative Task Allocatio...

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International Conference on Guidance, Navigation and control, ICGNC 2024

作者： Chen, Xinyu Qi, Juntong Peng, Yan Ping, Yuan Wu, Chong Wang, Mingming Institute of Artificial Intelligence Shanghai University Shanghai China EFY Intelligent Control (Hainan) Technology Co. Ltd. R&D Center Hainan China Tianjin Key Laboratory of Intelligent Unmanned Swarm Technology and System Institute of Electrical and Information Engineering Tianjin University Tianjin China

ISBN: (纸本)9789819622634

This paper introduces a two-stage method, combining K-means++ and a modified genetic algorithm (T-KMGA), aimed at simplifying collaborative task allocation complexity and enhancing operational efficiency within heterogeneous autonomous task allocation unmanned systems. The proposed T-KMGA constructs a solution for task allocation through two stages: task clustering and path planning. In the first stage, we develop a modified K-means++ clustering model sensitive to unmanned aerial vehicle (UAV) endurance radii to tackle the increasing complexity of task allocation as the scale of tasks grows. In the second stage, we propose a modified genetic algorithm to plan paths for multiple UAVs and a single unmanned surface vehicle (USV) to minimize the execution time of cooperative tasks. We adopt a virtual node encoding method to solve the multiple UAV task allocation problem in the second stage. The effectiveness of the proposed approach is assessed through simulation experiments. The results demonstrate that the proposed method can significantly reduce task execution time and computational consumption, especially in large-scale task scenarios. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： air-sea heterogeneous unmanned systems cooperative task allocation genetic algorithms k-means++ Task allocation

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TSN-compatible Industrial Wired/Wireless Multi-protocol Conversion Mechanism and Module 48

TSN-compatible Industrial Wired/Wireless Multi-protocol Conv...

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48th Annual Conference of the IEEE Industrial Electronics Society, IECON 2022

作者： Chen, Yingxiu Xu, Qimin Zhang, Jinglong Xu, Lei Li, Lingzhi Chen, Cailian Department of Automation Shanghai Jiao Tong University Shanghai 200240 China Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai Engineering Research Center of Intelligent Control and Management Shanghai 200240 China

ISBN: (数字)9781665480253

ISBN: (纸本)9781665480253

The interoperability of heterogeneous networks, including hybrid industrial wired/wireless protocols, is a vital aspect of the Industrial Internet of Things (IIoT) since various industrial protocols have coexisted. Therefore, it is hard to obtain a multiple protocol conversion solution with the flexible configuration. Moreover, the previous works do not adapt to the Time Sensitive Networking (TSN), which is considered as the potential technology of IIoT. To tackle the above problems, we proposed an architecture of the multiple conversion system supporting the conversion of multiple protocols. Then, we proposed a protocol conversion mechanism to improve the flexibility of multiple protocol conversion by separating into data and configuration planes. Moreover, we propose a TSN-compatible frame with dynamic priority mapping algorithm, which adjusts the priority in the VLAN tag by the processing delay. Finally, we develop a hardware module and system supporting multiple protocol conversion, which verify that the proposed architecture could satisfy the flexible conversion requirement through the experiments. © 2022 IEEE.

关键词： Ethernet

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Defect Detection in Solar Panels Based on PV-Flow

Defect Detection in Solar Panels Based on PV-Flow

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WRC Symposium on Advanced Robotics and Automation (WRC SARA)

作者： Minghao Niu Jiaqi Li Mengyu Li Yifan Zhao Ying Liu Shuhuan Wen Key Laboratory of Industrial Computer Control Engineering of Hebei Province Key Lab of Intelligent Rehabilitation and Neuroregulation in Hebei Province Engineering Research Center of the Ministry of Education for Intelligent Control System and Intelligent Equipment Yanshan University Qinhuangdao China Yingli Energy Development Co. Baoding China

ISBN: (数字)9798331506100

ISBN: (纸本)9798331506117

With technological advancements, resource utilization has increased, making renewable energy sources like solar power essential. Effective defect detection for solar panels is crucial. However, manual inspection is currently inefficient and machine vision lacks accuracy. In order to improve defect detection in high-resolution images and overcome the limitations of existing methods, we propose an unsupervised deep learning model called PV-Flow, which combines a hybrid attentional mechanism and a multiscale feature extraction module of the flow model. Before defect detection we need to categorize the defects and make a dataset, after observing the features we classify the defects into black spots, broken grids, hidden cracks and fragmentations. The performance of the model while performing defect detection is evaluated by metrics of reconstructed images and visual comparisons. Experiments on real solar panel datasets show that our proposed PV-Flow model is able to accurately identify the location of broken gate defects and show more details, with even the smallest defects clearly labeled and magnified. This suggests that our proposed model is able to detect potential defects earlier, thus reducing production risks and costs. We also conducted comparative experiments to further validate the reliability of the model.

关键词： Deep learning Visualization Accuracy Costs Sensitivity Production Feature extraction

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