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4th International Conference on Power and Electrical engineering, ICPEE 2023

作者： Sun, Qiang Gao, Wensheng Zhang, Ziwei Nie, Hongyu Lu, Guiyou He, Yufeng Qu, Jiaqi Department of Electrical Engineering State Key Laboratory of Control and Simulation of Power System and Generation Equipment Tsinghua University Beijing China Sichuan Energy Internet Research Institute Tsinghua University Chengdu China Ultra High Voltage Branch State Grid Sichuan Electric Power Company Chengdu China School of Instrumentation and Optoelectronic Engineering Beihang University Beijing China

ISBN: (纸本)9789819716739

Most simulations of electric field distribution during the very fast transient (VFT) process are based on equivalent waveforms, which affects the analysis of the electric field. In this paper, the actual VFT process is simulated based on the 3D full Maxwell finite element method (FEM). The electric field distribution at different points is measured. To investigate the influence of the induced electric field during the VFT process, and whether high amplitude or high steepness is the main factor affecting insulation during the VFT process, the simulation results are compared with the electrostatic simulation results. The results show that the induced electric field caused by the wave process does not play an important part. The electric field distribution is hardly influenced by the induced electric field. Therefore, the threat to insulation caused by VFTO is mainly due to its high amplitude, which leads to a stronger Coulomb electric field. Besides, the wave process only leads to a 6~8% voltage amplitude increase, which is much lower than the amplitude increase due to externally generated low-frequency oscillations. Therefore, the high amplitude of VFTO is mainly caused by low-frequency oscillations, rather than the high-frequency components during the VFT process. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Electric fields

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Evolutionary Dynamics of Cooperation on Coupled Networks

Evolutionary Dynamics of Cooperation on Coupled Networks

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control Theory and Applications (ICoCTA), International Conference on

作者： Zhenyu Shi Qi Su Department of Automation Ministry of Education of China Key Laboratory of System Control and Information Processing Shanghai Engineering Research Center of Intelligent Control and Management Shanghai Jiao Tong University Shanghai China

ISBN: (数字)9798331506155

ISBN: (纸本)9798331506162

Network reciprocity has been recognized as a crucial mechanism for promoting the evolution of cooperation. However, prior studies on network reciprocity have predominantly focused on isolated networks, neglecting the interconnected nature of real-world systems. In reality, many large-scale systems are closely coupled, where the interaction patterns between systems differ significantly from those within individual networks. Motivated by this observation, we construct a strategy evolution model on coupled networks to explore the effects of such interconnections. Our findings reveal that coupled networks foster cooperation more effectively than isolated networks. This advantage persists across various network structures, suggesting the universality of this phenomenon. Interestingly, we discover that the strength of the coupling plays a pivotal role: weaker coupling between networks is more conducive to the emergence and stability of cooperation compared to stronger coupling. These results high-light the importance of considering cross-system interactions in understanding and designing mechanisms to enhance cooperative behavior.

关键词： Couplings Stability analysis Large-scale systems control theory Game theory

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An Approach for Predicting and Compensating the End Deformation of a Heavy Load Robot for Friction Stir Welding 16th

An Approach for Predicting and Compensating the End Deformat...

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16th International Federation of Theory of Machines and Mechanisms World Congress, IFToMM WC 2023

作者： Bai, Yujie Liu, Haitao Meng, Shaofei Ma, Yue Yue, Wei Li, Guangxi Xiao, Juliang Wang, Guofeng Ding, Yabin Key Laboratory of Mechanism Theory and Equipment Design of State Ministry of Education Tianjin University Tianjin300072 China Tianjin Key Laboratory for Advanced Mechatronic System Design and Intelligent Control School of Mechanical Engineering Tianjin University of Technology Tianjin300384 China Dongguan University of Technology Dongguan523808 China

ISBN: (纸本)9783031457043

Aiming at solving the problem of insufficient plunge depth by the deformation of friction stir welding robots, this paper presents a method for predicting and compensating for the end deformation of friction stir welding robots. Firstly, the numerical model of FSW is established using DEFORM-3D software, then the axial force on the welding tool is obtained. Subsequently, the robot stiffness model is established based on the screw theory to realize the prediction of robot deformation in the workspace. Finally, a deformation compensation method based on the mirror principle is proposed to realize the compensation of the welding trajectory. Taking the 10 mm AA6061 aluminium alloy FSW as an example, the compensation method proposed in this paper is verified by using computer simulation. The results show that the stiffness model established in this paper can accurately predict the end deformation of the robot. The maximum plunge depth deviation of the compensation method proposed in this paper is reduced by 95.17% compared with the uncompensated case, which proves the effectiveness of the proposed compensation method. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023.

关键词： Friction stir welding

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A Multi-Kernel Principal Component Analysis Method for Quality-Related Fault Detection 37

A Multi-Kernel Principal Component Analysis Method for Quali...

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37th Youth Academic Annual Conference of Chinese Association of Automation, YAC 2022

作者： Mu, Lingxia Lei, Biyu Liu, Ding Xi'An University of Technology School of Automation and Information Engineering Xi'an China Xi'An University of Technology Shaanxi Key Laboratory of Complex System Control and Intelligent Information Processing Xi'an China

ISBN: (纸本)9781665465366

In this paper, a multi-kernel principal component analysis (MKPCA) method for quality-related fault detection is proposed. The initial space is firstly mapped to a new space. The correlated information between the new space and output quality is then obtained by the kernel function. Meanwhile, with consideration of the advantage of global function and local function, a weight factor which combines them together is introduced to construct a multi-kernel function. In this way, the algorithm achieves better learning ability. The new space is projected to two mutually orthogonal subspaces, i.e., quality-related part and quality-unrelated part. In each subspace, fault information is expressed by different statistical indicators. The numerical example is presented to evaluate the performance of the MKPCA. The results show better reliability and high fault detection rate through proper spatial decomposition and kernel function construction. © 2022 IEEE.

关键词： Fault detection

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Cluster Consensus Problem on Time-varying Higher-Order Interaction Networks

Cluster Consensus Problem on Time-varying Higher-Order Inter...

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IEEE Conference on Decision and control

作者： Haoyu Wei Lulu Pan Haibin Shao Dewei Li Wenbin Yu Department of Automation Shanghai Jiao Tong University and Key Laboratory of System Control and Information Processing Ministry of Education of China and Shanghai Engineering Research Center of Intelligent Control and Management Shanghai China

ISBN: (数字)9798350316339

ISBN: (纸本)9798350316346

This paper examines the cluster consensus problem of higher-order interaction networks on switching networks. The matrix-valued inter-agent coupling mechanism is employed to capture the higher-order interactions amongst neighboring agents. Specifically, for a higher-order interaction network with both positive definite and positive semi-definite coupling matrices, we examine the scenario in which the nullspace of positive semi-definite coupling matrices is constructed by a set of orthogonal basis vectors. From the perspective of the lower-order component graph -the projection of network structure onto the direction of each orthogonal basis–results on the explicit characterization of cluster consensus of higher-order interaction networks are provided. Moreover, for time-varying higher-order interaction networks, necessary and sufficient conditions are provided to ensure that a time-varying network shares the same steady-state values as the integral time-invariant network. Simulation results are provided to demonstrate the theory.

关键词： Couplings Sufficient conditions Network topology Simulation Switches Vectors Topology Steady-state Matrix decomposition Convergence

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A novel two-stage Dissolved Gas Analysis fault diagnosis system based semi-supervised learning

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High Voltage 2022年第4期7卷 676-691页

作者： Xuemin Tan Chao Guo Ke Wang Fu Wan College of Control Engineering Chengdu University of Information TechnologyChengduChina State Grid Chengdu Power Supply Company ChengduChina China Electric Power Research Institute BeijingChina State Key Laboratory of Power Transmission Equipment and System Security and New Technology Chongqing UniversityChongqingChina

Dissolved Gas Analysis(DGA)is an important method for oil-immersed transformer fault ***,collecting labelled DGA data is difficult because the determi-nation of the transformer fault is time-consuming and expensive in the transformer substation,but DGA data without labels is easier to ***,the paper pro-posed a semi-supervised two-stage diagnostic system based DGA by using less labelled *** two-stage system includes a novel semi-supervised feature selection based Genetic Algorithm(GA)and Support Vector Machine(SVM)model(SSL-FS-GASVM)for selecting optimal features and a novel semi-supervised transformer fault diagnosis model based improved Artificial Fish Swarm Algorithm(AFSA)and SVM(SSL-IAFSA-SVM)for optimising the SVM ***,the performances of SSL-FS-GASVM and SSL-IAFSA-SVM models are tested and compared with traditional supervised diagnostic models combined with other optimisation methods,*** results show that the proposed two-stage system works in optimising features and parameters and has strong robustness in solving small sample classification problems.

关键词： fault diagnosis system

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Deep learning based fault diagnosis for chemical process with statistical feature fusion

Deep learning based fault diagnosis for chemical process wit...

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第42届中国控制会议

作者： Lei Zhang Hao Xia Bao Chao School of Control Science and Control Engineering Dalian University of Technology Science and Technology on Reactor System Design Technology Laboratory Nuclear Power Institute of China

The existing fault diagnosis methods based on deep neural networks mostly rely on process data for fault classification and diagnosis,which makes it difficult to distinguish minor faults with similar data ***,this paper proposes a deep learning-based fault diagnosis model that combines statistical indicators and neural *** analyzing and calculating the relevant information entropy to screen the statistical signal features of time domain and frequency domain,we can extract more hidden information from the raw data,and use the convolutional autoencoder to compress the statistical indicators for feature extraction,and use CNN network to extract features from raw ***,the two sets of features are fused and input into the LSTM network to obtain temporal information for fault *** conducted on the Tennessee Eastman(TE)process show that the proposed model outperforms typical classifiers and improves the diagnostic accuracy of minor faults.

关键词：

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Formal control Synthesis via Safe Reinforcement Learning Under Real-Time Specifications

Formal Control Synthesis via Safe Reinforcement Learning Und...

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International Conference on control and Automation (ICCA)

作者： Peng Lv Guangqing Luo Zhou He Xianwei Li Xiang Yin Department of Automation Key Laboratory of System Control and Information Processing Shanghai Jiao Tong University Shanghai China School of Electrical and Control Engineering Shaanxi University of Science and Technology Xi'an China

ISBN: (数字)9798350354409

ISBN: (纸本)9798350354416

In recent years, reinforcement learning techniques have gained widespread application in control synthesis. However, in the context of safety-critical systems, employing trial-and-error based reinforcement learning may be unacceptable due to the potential risks it poses during the learning process. Consequently, the development of safe reinforcement learning techniques has become imperative. This paper addresses the challenge of safe reinforcement learning for controller synthesis, particularly when safety specifications are intricately linked to the real-time behavior of the system. To articulate time-sensitive requirements, we leverage Metric Interval Temporal Logic (MITL). To ensure safety throughout the learning process, we introduce an additional reactive controller called a shield. Specifically, the shield functions to reject any behavior that violates the real-time specifications, thus mitigating potential risks. The efficacy of our proposed approach is demonstrated through simulation results, highlighting its ability to satisfy safety constraints in the dynamic environment of controller synthesis.

关键词： Measurement Heuristic algorithms Simulation Process control Reinforcement learning Real-time systems Safety

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End-to-end Automatic Targetless Calibration between Low-resolution LiDARs and Cameras based on Cross-modal Attention

End-to-end Automatic Targetless Calibration between Low-reso...

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Chinese control and Decision Conference, CCDC

作者： Tianyang Huang Jun-Guo Lu Ming Wei Qing-Hao Zhang 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 Shanghai China

ISBN: (数字)9798350387780

ISBN: (纸本)9798350387797

LiDARs and cameras, commonly used sensors on autonomous vehicles and robots, require precise calibration for accurate joint environmental perception. Current calibration methods, capable of adjusting both intrinsic and extrinsic parameters, often depend on physical targets or human intervention, making them unsuitable for automatic online calibration. Moreover, these methods are primarily optimized for dense point clouds, as they are often tuned on open datasets featuring high-quality point cloud data. However, low-resolution LiDARs, preferred for their cost-effectiveness, are prevalent in practical applications. To automate the calibration process while maintaining high precision on sparse point clouds, multi-modal image registration techniques are leveraged to develop an end-to-end, cross-attention-based neural network for automatic calibration of low-resolution LiDARs and Cameras. Specifically, given an initial perturbation on point clouds, our network is capable of recovering the true extrinsic parameters without manual intervention. Additionally, specialized input preprocessing and training strategies are incorporated to enhance performance with sparse point clouds. Promising results are achieved on both open datasets and custom sensor acquisitions, highlighting the effectiveness and adaptability of our network to diverse scenarios.

关键词： Point cloud compression Training Image registration Visualization Laser radar Perturbation methods Neural networks

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Efficient Reinforcement Learning for 3D LiDAR Navigation of Mobile Robot 41

Efficient Reinforcement Learning for 3D LiDAR Navigation of ...

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第41届中国控制会议

作者： Yu Zhai Zhe Liu Yanzi Miao Hesheng Wang China University of Mining and Technology MoE Key Lab of Artificial Intelligence Shanghai Jiao Tong University Department of Automation Key Laboratory of System Control and Information Processing of Ministry of EducationShanghai Engineering Research Center of Intelligent Control and ManagementShanghai Jiao Tong University

ISBN: (数字)9789887581536

ISBN: (纸本)9781665482561

Developing an efficient automatic navigation system for mobile robots is challenging in the strange scenarios where robots can only observe the environment of the surrounding limited *** other distributed automatic navigation systems exist,they often require extracting semantic information to calculate navigation action,which requires extra modules to provide perceptual information and is not *** propose an end-to-end automatic navigation system based on the reinforcement learning *** particular,the raw 3 D LiDAR data is used to directly map an efficient navigation *** design a novel dense reward function to handle the reward sparsity issue and provide a graphical representation method to enable the efficient feature learning from the raw 3 D LiDAR data in our navigation *** addition,an imitation learning based policy initialization is introduced before the subsequent reinforcement learning,which increases the learning efficiency and,in the meantime,still encouraging the robot to explore all the potential states to achieve advanced performance than the imitated *** navigation model is trained in the Webots environment and the experimental results show that our model has efficient and flexible navigation performance in complex *** importantly,trained model can be easily extended to unfamiliar environments.

关键词： End-to-end Navigation Mobile robot Reinforcement Learning 3D LiDAR

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