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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 42

A Weighting Factors Autotuning Strategy for Model Predictive...

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42nd Chinese control Conference, CCC 2023

作者： Zhou, Zixiang Wu, Tao School of Automation China University of Geosciences Wuhan430074 China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan430074 China System Integration and Industrial Robotics Laboratory Wuhan430074 China

ISBN: (纸本)9789887581543

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 multi-objective 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. © 2023 Technical Committee on control Theory, Chinese Association of Automation.

关键词： Cost functions

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Temperature Rising Model of Tubular Permanent Magnet Linear Synchronous Motor Based on Transfer Learning-Deep Neural Network 42

Temperature Rising Model of Tubular Permanent Magnet Linear ...

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42nd Chinese control Conference, CCC 2023

作者： Wang, YiFei Wu, Tao School of Automation China University of Geosciences Wuhan430074 China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan430074 China System Integration and Industrial Robotics Laboratory Wuhan430074 China

ISBN: (纸本)9789887581543

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. © 2023 Technical Committee on control Theory, Chinese Association of Automation.

关键词： Finite element method

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A Fully Distributed Hybrid control Framework For Non-Differentiable Multi-Agent optimization

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IEEE/CAA Journal of Automatica Sinica 2022年第10期9卷 1792-1800页

作者： Xia Jiang Xianlin Zeng Jian Sun Jie Chen Yue Wei the Key Laboratory of Intelligent Control and Decision of Complex Systems School of AutomationBeijing Institute of TechnologyBeijing 100081 the Beijing Institute of Technology Chongqing Innovation Center Chongqing 401120China IEEE the School of Electronic and Information Engineering Tongji UniversityShanghai 200082 the Peng Cheng Laboratory Shenzhen 518055China

This paper develops a fully distributed hybrid control framework for distributed constrained optimization *** individual cost functions are non-differentiable and *** on hybrid dynamical systems,we present a distributed state-dependent hybrid design to improve the transient performance of distributed primal-dual first-order optimization *** proposed framework consists of a distributed constrained continuous-time mapping in the form of a differential inclusion and a distributed discrete-time mapping triggered by the satisfaction of local jump *** the semistability theory of hybrid dynamical systems,the paper proves that the hybrid control algorithm converges to one optimal solution instead of oscillating among different *** simulations illustrate better transient performance of the proposed hybrid algorithm compared with the results of the existing continuous-time algorithms.

关键词： Distributed algorithm hybrid framework multiagent network non-differentiable optimization

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End-to-end multi-task reinforcement learning-based UAV swarm communication attack detection and area coverage

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Knowledge-Based systems 2025年 316卷

作者： Yu, Jin Zhang, Ya Sun, Changyin School of Automation Southeast University Nanjing210096 China Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of Education Nanjing China

This paper proposes an innovative end-to-end multi-task reinforcement learning framework designed to enhance the security and operational stability of UAVs in complex communication attacks environments. In scenarios involving multiple UAV coverage, the framework classifies attack types and predicts UAV behaviors, which enables rapid response and compromised data recovery. First, a spatiotemporal attention mechanism is employed to strengthen the network by capturing temporal dependencies in the communication data and improving the recognition of hybird attack mode, thereby facilitating the integration of detection and repair. Second, the causal inference mechanism introduced in this study extracts true causal relationships from interference, which ensures the accuracy and stability of action predictions for area coverage under attack conditions. Finally, the entire process is modeled as a multi-task Markov decision process, optimizing overall decision-making efficiency. Additionally, the dynamic pheromone induction mechanism is proposed to realize the adaptive regional cooperative coverage of multiple UAVs. The experimental results demonstrate that the proposed algorithm can effectively detect hybrid attacks and predict UAV actions, protecting the multi-UAV cooperative area coverage. © 2025

关键词： Reinforcement learning

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PDM control Strategy of Extremely High Gain ICPT System Applying for Electrical Isolation Aimed at Maximum Efficiency 48

PDM Control Strategy of Extremely High Gain ICPT System Appl...

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

作者： Yue, Haojiang Fang, Zhijian Xia, Yuangeng China University of Geosciences Wuhan Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems School of Automation Wuhan China

ISBN: (数字)9781665480253

ISBN: (纸本)9781665480253

In order to achieve electrical isolation, high-voltage transformers must use insulating materials. However, in some applications, such as medical treatment, transformers using insulating materials may cause problems of weight increase and flexible movement. As a result, the inductively coupled power transfer (ICPT) system are used to replace regular high transformers, with the air gap between the primary and secondary coils serving as electrical insulation. Under the SP resonance compensation structure, the ICPT system needs to achieve exceptionally high gain, which will result in the voltage gain being sensitive to frequency variations. When the frequency deviates slightly from the resonance point, more circulation and loss occurs, lowering the efficiency of the system. Therefore, this paper proposes a control strategy based on the combination of pulse-duration modulation (PDM) and maximum efficiency point tracking (MEPT) is proposed, at the first resonance point of the ICPT system based on the SP resonance compensation structure. This control method is capable of producing medium and high voltage constant voltage output, and the switch tube achieves zero current switching during operation. In the case of a wide load variation range, maximum efficiency point tracking may be obtained at the same time. Finally, a 100W high-gain ICPT experimental platform is built, and the constant voltage output with 25 times high gain is realized over the transmission distance of 11.2cm, which verifies the effectiveness of the control strategy. © 2022 IEEE.

关键词： Pulse width modulation

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Framework and key Technologies for High-speed Railway Comprehensive Transport Hub 3

Framework and Key Technologies for High-speed Railway Compre...

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3rd IEEE International Conference on Digital Twins and Parallel Intelligence, DTPI 2023

作者： Xiong, Gang Zhang, Wei Liu, Xiaoming Yuan, Zhiming Zhu, Fenghua Zhang, Zundong Chinese Academy of Sciences State Key Laboratory for Multimodal Artificial Intelligence Systems Beijing China North China University of Technology School of Electrical and Control Engineering Beijing China China Academy of Railway Science Communication Signal Research Institute Beijing China Chinese Academy of Sciences State Key Laboratory for Management and Control of Complex Systems Beijing China

ISBN: (纸本)9798350318470

The surge in urban population density in China has underscored the vital role of high-speed rail and subway systems in urban transit. Comprehensive transportation hubs, notably high-speed rail passenger terminals, have emerged as linchpins for seamless multimodal transit both within and beyond urban centers. China's railway sector vigorously champions the innovative concept of "station-city integration"and has articulated a forward-looking "16-word policy"for modern railway stations: "Seamless integration, environmental friendliness, economic viability, and intelligent accessibility". The judicious allocation of resources and the augmentation of coordination and control capabilities within comprehensive transportation hubs are imperative for the harmonious evolution of urban transit. The implementation of intelligent control systems for transportation hubs has emerged as a pivotal area of research. This study delves into the framework and critical technologies for high-speed railway comprehensive transport hubs, encompassing various components and their intricate interplay, including transport modalities, infrastructure, travelers, environmental factors, and information systems, thus forming a prototypical cyber-physical-social system. Addressing the prevalent and pressing imperative for the advancement of "station-city integration"high-speed railway comprehensive transport hubs, this research endeavor aims to surmount bottleneck challenges and refine the oversight of passenger flows at high-speed rail terminals. The findings hold noteworthy theoretical import and portend promising practical applications. © 2023 IEEE.

关键词： Integration

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A distributed finite-time economic dispatch algorithm in smart grid

A distributed finite-time economic dispatch algorithm in sma...

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2022 Jiangsu Annual Conference on Automation, JACA 2022

作者： Lin, Luxin Wang, Xiangyu School of Automation Southeast University Nanjing210096 China Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of Education China

ISBN: (纸本)9781839538476

This paper focuses on the design of an initialization-free distributed finite-time algorithm for the economic dispatch problem in smart grid, where the generation units only communicate with its neighbors to minimize the total cost cooperatively. Firstly, the optimal solution of the economic dispatch problem is analyzed in accordance with the saddle-point dynamics. Then, an initialization-free finite-time algorithm is presented to guarantee the active power generation and the incremental cost converge to the optimal solution in a distributed manner. At last, some numerical simulations are preformed to validate the proposed method. Copyright© JACA 2022 All rights reserved.

关键词： Smart power grids

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基于反常热传导的分数阶热弹耦合理论

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Acta Mechanica Sinica 2024年第5期40卷 141-149页

作者：尉亚军吴华邓子辰 School of Mechanics Civil Engineering and Architecture Northwestern Polytechnical University Xi’an 710072 China MIIT Key Laboratory of Dynamics and Control of Complex Systems Northwestern Polytechnical University Xi’an 710072 China State Key Laboratory for Strength and Vibration of Mechanical Structures Xi’an Jiaotong University Xi’an 710049 China

原子模拟和实验研究表明低维纳米材料具有反常热传导特性,建立唯象反常热弹耦合模型,研究强热传导导致的热力耦合响应,对微纳器件的安全稳定运行至关重要.本文旨在从反常热传导的视角重新审视分数阶热弹耦合模型。反常热传导由第二声效... 详细信息

原子模拟和实验研究表明低维纳米材料具有反常热传导特性,建立唯象反常热弹耦合模型,研究强热传导导致的热力耦合响应,对微纳器件的安全稳定运行至关重要.本文旨在从反常热传导的视角重新审视分数阶热弹耦合模型。反常热传导由第二声效应引起,考虑波动热传导理论,建立了波动热传导与粘弹性理论的类比,阐明了Cattaneo-Vernotte模型和Green-Naghdi模型的关联.通过引入分数阶导数,构建了基于Cattaneo-Vernotte和Green-Naghdi理论的分数阶热弹耦合模型。数值结果表明:对分数阶次区间[O,1],分数阶Cattaneo-Vernotte(FCV)I模型和分数阶Green-Naghdi(FGN)I-III模型都能预测反常热弹耦合响应:比经典热弹性理论更高的温度和应力;温度和应力随时间的变化显示:FGNII模型在所有时间范围内都能得到反常响应.对Green-Naghdi模型及其分数阶模型的进一步系统研究,有助于揭示反常热传导和热弹耦合机理,并促进纳米材料的广泛应用.

关键词：粘弹性理论分数阶导数热弹耦合热弹性理论热传导低维纳米材料分数阶模型微纳器件

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A novel task-oriented framework for dual-arm robotic assembly task

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Frontiers of Mechanical Engineering 2021年第3期16卷 528-545页

作者： Zhengwei WANG Yahui GAN Xianzhong DAI School of Automation Southeast UniversityNanjing 210096China Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of EducationNanjing 210096China

In industrial manufacturing,the deployment of dual-arm robots in assembly tasks has become a ***,making the dual-arm robots more intelligent in such applications is still an open,challenging *** paper proposes a novel framework that combines task-oriented motion planning with visual perception to facilitate robot deployment from perception to execution and finish assembly problems by using dual-arm *** this framework,visual perception is first employed to track the effects of the robot behaviors and observe states of the workpieces,where the performance of tasks can be abstracted as a high-level state for intelligent *** assembly task and manipulation sequences can be obtained by analyzing and reasoning the state transition trajectory of the environment as well as the ***,the corresponding assembly manipulation can be generated and parameterized according to the differences between adjacent states by combining with the prebuilt knowledge of the *** are set up with a dual-arm robotic system(ABB YuMi and an RGB-D camera)to validate the proposed *** results demonstrate the effectiveness of the proposed framework and the promising value of its practical application.

关键词： dual-arm assembly Al reasoning intelligent system task-oriented motion planning visual perception

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Deep Learning-based Moving Object Segmentation:Recent Progress and Research Prospects

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Machine Intelligence Research 2023年第3期20卷 335-369页

作者： Rui Jiang Ruixiang Zhu Hu Su Yinlin Li Yuan Xie Wei Zou College of Information Engineering Shanghai Maritime UniversityShanghai201306China School of Computer Science and Technology East China Normal UniversityShanghai200062China Research Center of Precision Sensing and Control Institute of AutomationChinese Academy of SciencesBeijing100190China State Key Laboratory for Management and Control of Complex Systems Institute of AutomationChinese Academy of SciencesBeijing100190China

Moving object segmentation(MOS),aiming at segmenting moving objects from video frames,is an important and challenging task in computer vision and with various *** the development of deep learning(DL),MOS has also entered the era of deep models toward spatiotemporal feature *** paper aims to provide the latest review of recent DL-based MOS methods proposed during the past three ***,we present a more up-to-date categorization based on model characteristics,then compare and discuss each category from feature learning(FL),and model training and evaluation *** FL,the methods reviewed are divided into three types:spatial FL,temporal FL,and spatiotemporal FL,then analyzed from input and model architectures aspects,three input types,and four typical preprocessing subnetworks are *** terms of training,we discuss ideas for enhancing model *** terms of evaluation,based on a previous categorization of scene dependent evaluation and scene independent evaluation,and combined with whether used videos are recorded with static or moving cameras,we further provide four subdivided evaluation setups and analyze that of reviewed *** also show performance comparisons of some reviewed MOS methods and analyze the advantages and disadvantages of reviewed MOS methods in terms of ***,based on the above comparisons and discussions,we present research prospects and future directions.

关键词： Moving object segmentation(MOS) change detection background subtraction deep learning(DL) video understanding

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