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General Impedance Modeling for Modular Multilevel Converter with Grid-forming and Grid-following control

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arXiv 2024年

作者： Sun, Chu Zhang, Fei Xiao, Huafeng Wang, Na Chen, Jikai The School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Stockholm11428 Sweden The School of Electrical Engineering Southeast University Nanjing210096 China The School of Automation Science and Electrical Engineering Beihang University Beijing100191 China The Key Laboratory of Modern Power System Simulation and Control & Renewable Energy Technology Ministry of Education Northeast Electric Power University Jilin132012 China

Modular multilevel converter (MMC) has complex topology, control architecture and broadband harmonic spectrum. For this, linear-time-periodic (LTP) theory, covering multi-harmonic coupling relations, has been adopted for MMC impedance modeling recently. However, the existing MMC impedance models usually lack explicit expressions and general modeling procedure for different control strategies. To this end, this paper proposes a general impedance modeling procedure applicable to various power converters with grid-forming and grid-following control strategies. The modeling is based on a unified representation of MMC circuit as the input and output relation between the voltage or current on the AC side and the exerted modulation index, while the control part vice versa, thereby interconnected as closed-loop feedback. With each part expressed as transfer functions, the final impedance model keeps the explicit form of harmonic transfer function matrix, making it convenient to directly observe and analyze the influence of each part individually. Thereby the submodule capacitance is found as the main cause of difference between MMC impedance compared to two-level converter, which will get closer as the capacitance increases. Effectiveness and generality of the impedance modeling method is demonstrated through comprehensive comparison with impedance scanning using electromagnetic transient simulation. Copyright © 2024, The Authors. All rights reserved.

关键词： Harmonic analysis

End-to-end Flow Scheduling Optimization for Industrial 5G and TSN Integrated Networks

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End-to-end Flow Scheduling Optimization for Industrial 5G an...

IEEE Conference on Global Communications (GLOBECOM)

作者： Houling Liu Mingyan Li Fuqiang Gu Qihao Li Weiting Zhang Songtao Guo College of Computer Science Chongqing University P. R. China Key Laboratory of System Control and Information Processing Ministry of Education of China P. R. China College of Communication Engineering Jilin University Changchun P. R. China School of Electronic and Information Engineering Beijing Jiaotong University Beijing P. R. China

ISBN: (数字)9798350351255

ISBN: (纸本)9798350351262

The integrated of the fifth generation (5G) and time-sensitive networking (TSN) is a promising approach to meet the requirements of deterministic forwarding with extremely low latency and high flexibility for the Industrial Internet of Things (IIoT). However, due to the large dissimilarity of protocol stacks, the 5G system normally serves as a logical bridge for TSN, performing hold-and-forward operations in Base Stations (BSs) under the current 5G-TSN frameworks. To provide ubiquitous and seamless connectivity for IIoT devices, this paper focuses on the integrated enhancement of 5G and TSN by optimizing the scheduling of time-sensitive flows with end-to-end latency of 5G-TSN transmission taken into consideration. Specifically, we propose a novel architecture named GF-CQF, which combines 5G Grant-free (GF) access with TSN Cyclic Queuing and Forwarding (CQF). Subsequently, the distributed flow scheduling problem based on GF-CQF architecture is established due to the high timeliness demands. To alleviate the impact of the uncertainty inherent in 5G channels on end-to-end deterministic transmission, a feature-aware decentralized real-time scheduling (FDRS) policy based on Multi-agent Reinforcement Learning is proposed. FDRS allows each agent at BS to adaptively allocate TSN injection slots for flows mainly based on dynamic 5G transmission performance and TSN network resource state so that TSN queue overflow can be avoided and flow delay constraints can be guaranteed. Simulations show that FDRS offers superior scheduling capabilities under the limited TSN queue resources.

关键词： Wireless communication Training Job shop scheduling Uncertainty Protocols 5G mobile communication Reinforcement learning Real-time systems Resource management Industrial Internet of Things

Energy Consumption Prediction for Manufacturing in Industrial IoT Based on Heterogeneous GNN

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Energy Consumption Prediction for Manufacturing in Industria...

IEEE International Conference on Communications in China (ICCC)

作者： Xuan Su Mingyan Li Qihao Li Chao Chen Songtao Guo Xu Wang College of Mechanical and Vehicle Engineering Chongqing University Chongqing P. R. China College of Computer Science Chongqing University Chongqing P. R. China Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai P. R. China College of Communication Engineering Jilin University Changchun P. R. China

ISBN: (数字)9798350378412

ISBN: (纸本)9798350378429

Energy consumption prediction plays a crucial role in green manufacturing for industry, providing a theoretical foundation for energy-saving planning, workshop scheduling, and process optimization. However, owing to the processing complexity of mechanical products and the multiplicity of production information, previous methods can only achieve prediction for individual devices, which are quite restrictive to collaborative manufacturing planning. To deal with product-level energy management, this paper first proposes a heterogeneous manufacturing association graph (HetMG) which contains various manufacturing elements including product projects, decomposed orders and processing devices. Then, with HetMG, an energy consumption prediction method, HetEP, is introduced based on heterogeneous graph neural network (GNN), which incorporates Relational Graph Convolutional Networks (R-GCNs), and Long Short-Term Memory (LSTM) to mine the spatial-temporal relation of heterogeneous elements during the manufacturing process. Moreover, Graph Attention Network (GAT) is utilized in HetEP to adaptively learn the graph-structured data so that informative representations of each element can be obtained. The experimental results based on a manufacturing dataset from a generator factory demonstrate that the proposed HetEP yields superior prediction performance compared to the device-oriented prediction methods in terms of prediction accuracy.

关键词： Representation learning Performance evaluation Energy consumption Green manufacturing Job shop scheduling Prediction methods Production facilities

PUGAN: Physical Model-Guided Underwater Image Enhancement Using GAN with Dual-Discriminators

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arXiv 2023年

作者： Cong, Runmin Yang, Wenyu Zhang, Wei Li, Chongyi Guo, Chun-Le Huang, Qingming Kwong, Sam Institute of Information Science Beijing Jiaotong University Beijing100044 China School of Control Science and Engineering Shandong University Jinan250061 China Key Laboratory of Machine Intelligence and System Control Ministry of Education Jinan250061 China Beijing Key Laboratory of Advanced Information Science and Network Technology Beijing100044 China College of Computer Science Nankai University Tianjin300350 China School of Computer Science and Technology University of Chinese Academy of Sciences Beijing101408 China Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing100190 China Peng Cheng Laboratory Shenzhen518055 China Department of Computer Science City University of Hong Kong Hong Kong City University of Hong Kong Shenzhen Research Institute Shenzhen51800 China

Due to the light absorption and scattering induced by the water medium, underwater images usually suffer from some degradation problems, such as low contrast, color distortion, and blurring details, which aggravate the difficulty of downstream underwater understanding tasks. Therefore, how to obtain clear and visually pleasant images has become a common concern of people, and the task of underwater image enhancement (UIE) has also emerged as the times require. Among existing UIE methods, Generative Adversarial Networks (GANs) based methods perform well in visual aesthetics, while the physical model-based methods have better scene adaptability. Inheriting the advantages of the above two types of models, we propose a physical model-guided GAN model for UIE in this paper, referred to as PUGAN. The entire network is under the GAN architecture. On the one hand, we design a Parameters Estimation subnetwork (Par-subnet) to learn the parameters for physical model inversion, and use the generated color enhancement image as auxiliary information for the Two-Stream Interaction Enhancement subnetwork (TSIE-subnet). Meanwhile, we design a Degradation Quantization (DQ) module in TSIE-subnet to quantize scene degradation, thereby achieving reinforcing enhancement of key regions. On the other hand, we design the Dual-Discriminators for the style-content adversarial constraint, promoting the authenticity and visual aesthetics of the results. Extensive experiments on three benchmark datasets demonstrate that our PUGAN outperforms state-of-the-art methods in both qualitative and quantitative metrics. The code and results can be found from the link of https://***/proj_***. © 2023, CC BY-NC-SA.

关键词： Discriminators

A Novel Relocating Recognition Algorithm in Overlapping Region for Multi-UAVs Cluster

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A Novel Relocating Recognition Algorithm in Overlapping Regi...

2022 IEEE International Conference on Unmanned systems, ICUS 2022

作者： Li, Chen Qi, Xuelei Zhao, Juntao Liao, Xuanhong Lin, Zitian Ma, Hongjun Liang, Xinkai College of Information Science and Engineering Northeastern University Shenyang China Hong Kong University of Science and Technology Department of Electronic and Computer Engineering Hong Kong Hong Kong High School Attached to Northeast Normal University International Curriculum Center Changchun China School of Automation Science and Engineering South China University of Technology Guangzhou China Beijing Electro-Mechanical Engineering Institute Science and Technology on Complex System Control and Intelligent Agent Cooperation Laboratory Beijing China

ISBN: (数字)9781665484565

ISBN: (纸本)9781665484565

How improve the mapping efficiency and location accuracy of the multi-UAV cluster based on the distributed SLAM technology is a significant problem in overlapping regions. Therefore, this paper mainly proposes a novel relocation recognition algorithm for the multi-UAV cluster. First, the autonomous localization algorithm based on visual-inertial fusion is proposed. Then, the algorithm for vocabulary updating and relocating is designed. Finally, practical testing is implemented to verify and improve the efficiency of exploration ability and the accuracy of the autonomous navigation and positioning in the complex overlapping regions. © 2022 IEEE.

关键词： Unmanned aerial vehicles (UAV)

control of General Linear Time-Delay systems: Applications of the Kronecker-Seuret Decomposition

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arXiv 2022年

作者： Feng, Qian Zheng, Wei Xing Xiao, Feng Wang, Xiaoyu School of Control and Computer Engineering North China Electric Power University Beijing China School of Computer Data and Mathematical Sciences Western Sydney University Sydney Australia State Key Laboratory of Alternative Electrical Power System with Renewable Energy Sources North China Electric Power University Beijing China

We consider the design of stabilizing feedback controllers for autonomous linear time-delay system ẋ(t) = Xi=0ν Aix(t − ri) + Z−0r Ae(τ)x(t + τ)dτ + Xi=0ν Biu(t − ri) + Z−0r Be(τ)u(t + τ)dτ + D1w(t), z(t) = Xi=0ν Cix(t − ri) + Z−0r Ce(τ)x(t + τ)dτ + Xi=0ν Biu(t − ri) + Z−0r Be (τ)u(t + τ)dτ + D2w(t), x(t0 + θ) = ψ(θ), ∀θ ∈ [−r, 0], r = rν > · · · > r2 > r1 > r0 = 0, ν ∈ N under dissipative constraints expressed by quadratic forms, where matrix-valued functions Ae(·), Ce(·), Be(·), Be (·) can contain finite but unbounded number of square integrable functions. Our result is derived from an application of the Kronecker-Seuret decomposition in conjunction with the construction of Krasovskiı̆ functionals, where the final solution is expressed as a set of convex semidefinite programming problems. All functions in Ae(·), Ce(·), Be(·), Be (·) are explicitly addressed without requiring the use of operator or C0-semigroup theory. Moreover, the resulting optimization sufficient conditions can be efficiently computed by numerical solvers that are accessible to the general public, without requiring that the stabilizability and detectability of the open-loop system are known. We present the test results of two challenging examples, which could not be efficiently tackled by existing methods, to demonstrate the effectiveness of our methodology. The main objective of this study is to develop a non-conservative and numerically efficient framework for the mathematical control problem under investigation, designed to be accessible to engineers without expertise in operator theory and functional analysis. Additionally, this article briefly reviews recent research advancements in the mathematical control of linear time-delay systems, which could be useful to both emerging and experienced researchers in this field. Copyright © 2022, The Authors. All rights reserved.

关键词： State feedback

Optimal Action Strategy approach for Desert Crossing Based on Dynamic Programming

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Procedia computer Science 2021年 188卷 144-154页

作者： Xingrong Fan Dian Meng Chongqing Engineering Laboratory for Detection Control and Integrated System Chongqing Technology and Business University Chongqing and 400067 China School of Computer Science and Information Engineering Chongqing Technology and Business University Chongqing and 400067 China

It is a novel but challenging task that simulated desert crossing strategy for the game players under the specified conditions. This study is aimed to develop an optimal action strategy approach for desert crossing based on dynamic programming. According to the game rules, a multi-stage decision optimization model was first established to maximize the remaining funds of players within given game time. Then, a simulation-based dynamic programming algorithm was designed to solve the optimization model, and the optimal action strategy was obtained by backtracking crossing routes. Finally, two experimental case studies with different initial conditions were used to validate the model. In comparison with the benchmark exhaustive search algorithm, the proposed approach has higher performance and efficiency. The case studies confirm that the method can obtain the optimal action strategy under different game maps and initial resources.

关键词： Crossing desert Action strategy Dynamic programming Multi-stage decision computer simulation

Multi-kernel Correntropy-based Orientation Estimation of IMUs: Gradient Descent Methods

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arXiv 2023年

作者： Li, Shilei Li, Lijing Shi, Dawei Lou, Yunjiang Shi, Ling The Department of Electronic and Computer Engineering The Hong Kong University of Science and Technology Hong Kong The School of Information and Control Engineering China University of Mining and Technology China The School of Automation Beijing Institute of Technology China The State Key Laboratory of Robotics and System School of Mechanical Engineering and Automation Harbin Institute of Technology Shenzhen Shenzhen518055 China

This paper presents two computationally efficient algorithms for the orientation estimation of inertial measurement units (IMUs): the multi-kernel correntropy-based gradient descent (CGD) and the multi-kernel correntropy-based decoupled orientation estimation (CDOE). Traditional methods, such as gradient descent (GD) and decoupled orientation estimation (DOE), rely on the least square (LS) criterion in algorithm derivation, making them vulnerable to external acceleration and magnetic interference. To address this issue, we first demonstrate that the multi-kernel correntropy loss (MKCL) is an optimal objective function under the maximum likelihood estimation (MLE) framework when the noise follows a specific type of heavy-tailed distribution. Then, we provide some important properties of the MKCL as a cost function. By replacing the LS cost with the MKCL, we develop the CGD and CDOE algorithms. We evaluate the effectiveness of our proposed methods by comparing them with existing algorithms in various situations. Experimental results indicate that our proposed methods (CGD and CDOE) outperform their conventional counterparts (GD and DOE), especially when faced with external acceleration and magnetic disturbances. Furthermore, the new algorithms demonstrate significantly lower computational complexity than Kalman filter-based approaches, making them suitable for applications with low-cost microprocessors. Copyright © 2023, The Authors. All rights reserved.

关键词： Maximum likelihood estimation

Multi-kernel Correntropy Regression: Robustness, Optimality, and Application on Magnetometer Calibration

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arXiv 2023年

作者： Li, Shilei Chen, Yihan Lou, Yunjiang Shi, Dawei Li, Lijing Shi, Ling The Department of Electronic and Computer Engineering The Hong Kong University of Science and Technology Hong Kong The State Key Laboratory of Robotics and System School of Mechanical Engineering and Automation Harbin Institute of Technology Shenzhen Shenzhen518055 China The School of Automation Beijing Institute of Technology China The School of Information and Control Engineering China University of Mining and Technology China

This paper investigates the robustness and optimality of the multi-kernel correntropy (MKC) on linear regression. We first derive an upper error bound for a scalar regression problem in the presence of arbitrarily large outliers and state that the kernel bandwidth plays an important role in minimizing the lowest upper error bound. Then, we find that the proposed MKC is related to a specific heavy-tail distribution, where its head shape is consistent with the Gaussian distribution but its tail shape is heavy-tailed, and the extent of heavy-tail is controlled by the kernel bandwidth. It becomes a Gaussian distribution when the bandwidth is infinite, which allows one to tackle both Gaussian and non-Gaussian problems without explicitly investigating the noise distributions. To explore the optimal underlying distribution parameters, an expectation-maximization-like (EM) algorithm is developed to estimate the parameter vectors and the distribution parameters in an alternating manner. The results show that our algorithm can achieve equivalent performance compared with the traditional linear regression under Gaussian noise, and it significantly outperforms the conventional method under heavy-tailed noise. Both numerical simulations and experiments on a magnetometer calibration application verify the effectiveness of the proposed method. Note to Practitioners-The goal of this paper is to enhance the accuracy of conventional linear regression in handling outliers while maintaining its optimality under Gaussian assumptions. Our algorithm is formulated under the maximum likelihood estimation (MLE) framework, assuming the regression residuals follow a type of heavy-tailed noise distribution with an extreme case of Gaussian. The degree of the heavy tail is explored alternatingly using an Expectation-Maximization (EM) algorithm which converges very quickly. The robustness and optimality of the proposed approach are investigated and compared with the traditional approaches. Both th

关键词： Linear regression