检索结果-内蒙古大学图书馆

Simplified Cluster Voltage Balancing Control Based on Zero-sequence Voltage Injection for Star-connected Cascaded H-bridge STATCOM

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CSEE Journal of Power and Energy Systems 2024年第5期10卷 2255-2264页

作者： Kai Hu Guoliang Zhao Daorong Lu Zhengang Lu Nianwen Xiang Shulai Wang Jintian Lin School of Electrical Engineering and Automation Hefei University of TechnologyHefei 230009China School of Electrical and Automation Engineering Nanjing Normal UniversityNanjing 210023China State Grid Smart Grid Research Institute(SGRI) Beijing 102209China Power grid Technology Center of State Grid Zhejiang Electric Power Research Institute Hangzhou 310004China

The cluster DC voltage balancing control adopting zero-sequence voltage injection is appropriate for the starconnected cascaded H-bridge STATCOM because no zerosequence currents are generated in the three-phase three-wire ***,as the zero-sequence voltage is expressed in trigonometric form,traditional control methods involve many complicated operations,such as the square-root,trigonometric operations,and inverse tangent *** simplify cluster voltage balancing control,this paper converts the zero-sequence voltage to the dq frame in a DC representation by introducing a virtually orthogonal variable,and the DC components of the zero-sequence voltage in the dq frame are regulated linearly by proportional integral regulators,rather than being calculated from uneven active powers in traditional *** removes all complicated ***,this paper presents simulation and experimental results for a 400 V±7.5 kvar star-connected STATCOM,in balanced and unbalanced scenarios,thereby verifying the effectiveness of the proposed control.

关键词： Cluster DC voltage balancing control DC representation of zero-sequence voltage star-connected cascaded H-bridge STATCOM zero-sequence voltage injection

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Cooperative Fault-Tolerant Positioning Based on Model-Mixed Robust Gaussian Belief Propagation

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IEEE Internet of Things Journal 2025年第12期12卷 21537-21551页

作者： Li, Qijie Shi, Chenfa Xiong, Zhi Xiong, Jun Chen, Mingxing School of Automation Engineering Nanjing University of Aeronautics and Astronautics Navigation Research Center Nanjing China Nanjing University of Posts and Telecommunications School of Internet of Things Nanjing China Anhui Polytechnic University School of Electrical Engineering Anhui Wuhu China

In Internet of Things (IoT)-driven vehicular networks, accurate and reliable positioning services are critical for enabling vehicle collaboration and intelligence. The integration of Global Navigation Satellite Systems (GNSS) and Ultra-Wideband (UWB) technologies is widely employed for cooperative positioning (CP) of vehicles in IoT applications. However, in practical CP state estimation, GNSS and UWB measurements are often affected by environmental uncertainties such as non-line-of-sight (NLOS) conditions and multipath effects. This paper proposes a novel cooperative fault-tolerant positioning scheme based on Model-Mixed Robust Gaussian Belief Propagation (MMRGBP), designed for cooperative network positioning in interference environments. Firstly, we utilize belief propagation within a factor graph model for CP estimation, deriving the messages in the message passing (MP) process in Gaussian form. Subsequently, we introduce an improved Huber M estimation model under the Gaussian belief propagation framework, which approximates the non-Gaussian noise in the MP process as a Gaussian noise by dynamically adjusting the Huber function threshold to achieve more robust CP estimation. To address complex faulty measurements, we design an information interaction mechanism within the MP process, employing interactive multiple models for information fusion and switching the model of fault observation to prevent the introduction of serious positioning errors. Experimental results demonstrate that, compared to other advanced fault-tolerant CP methods such as RAIM-CPKF and Huber-CPKF filters, MMRGBP exhibits superior performance in both positioning accuracy and robustness. This makes MMRGBP particularly suitable for cooperative fault-tolerant positioning networks. © 2025 IEEE.

关键词： Belief propagation

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Disturbance Observer-Based Neural Network Control of a 2-DOF Helicopter System With Input Saturation and Output Constraints

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IEEE Transactions on Systems, Man, and Cybernetics: Systems 2025年第5期55卷 3152-3162页

作者： Zhao, Zhijia Zhang, Di Wu, Jiale Liu, Zhijie Wang, Min Hong, Keum-Shik Guangzhou University School of Mechanical and Electrical Engineering Guangzhou510006 China University of Science and Technology Beijing School of Intelligence Science and Technology Beijing100083 China South China University of Technology School of Automation Science and Engineering Guangzhou510641 China Pusan National University School of Mechanical Engineering Busan46241 Korea Republic of

This article presents a disturbance observer (DO)-based neural network (NN) control for a two-degree-of-freedom (2-DOF) helicopter system with input saturation, external disturbances, and output constraints. First, the uncertainties in the helicopter system are approximated using a radial basis function NN. Subsequently, a DO is used to approximate unknown compound disturbances, involving errors from NN estimation, input saturation, and external disturbances. To address the issue of output constraints imposed at a prescribed time period, a novel time-shift function and an adjusted barrier function are employed. Through the direct Lyapunov method, the boundedness of all control signals in the closed-loop system is verified. Finally, the effectiveness of the proposed control method is validated through numerical simulation results. © 2013 IEEE.

关键词： Lyapunov methods

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Electric Vehicle Charging/Discharging Control Method based on Deep Reinforcement Learning for Photovoltaic Energy Storage Stations 25

Electric Vehicle Charging/Discharging Control Method based o...

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25th IEEE China Conference on System Simulation Technology and its Application, CCSSTA 2024

作者： Zhao, Meng Li, Donghe Yang, Qingyu Xi'an Jiaotong University School of Automation Science and Engineering Xi'an China Xi'an Jiaotong University SKLMSE Laboratory School of Automation Science and Engineering Xi'an China

ISBN: (纸本)9798350366600

With the implementation of Internet of Things technology and the proliferation of electric vehicles (EVs), real-time control of EV charging/discharging is one of the keys to ensuring the safe, stable and efficient operation of smart grids. Nevertheless, existing EV charging/discharging control methods fail to comprehensively consider photovoltaic power generation, dynamic energy price, and the randomness and uncertainty of EV loads. This article proposes an EV charging/discharging control method for photovoltaic storage stations based on deep reinforcement learning (DRL). Considering the EV charging demand, photovoltaic energy output, and time-of-use electricity price, under the conditions of meeting the EV charging demand and photovoltaic energy utilization efficiency, the Markov decision process (MDP) of EV charging/discharging control based on DRL is established with the goal of minimizing user charging costs and enhancing user satisfaction. Considering the continuity of EV charging/ discharging action, an improved deep deterministic policy gradient (DDPG) algorithm is applied for the above scenario. Finally, simulation results are conducted on the proposed method, and comparable with disordered EV charging. The results verify that the recommend approach and model can significantly absorb photovoltaic energy and enhance user satisfaction while saving user costs by 27. 94%. © 2024 IEEE.

关键词： Charging stations

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A novel low pressure-difference fluctuation electro-hydraulic large flowrate control valve for fuel flowrate control of aeroengine afterburner system

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Chinese Journal of Aeronautics 2022年第11期35卷 363-376页

作者： Jie HANG Yunhua LI Liman YANG School of Automation Science and Electrical Engineering Beihang UniversityBeijing 100191China

To address the control accuracy of large fuel flowrate during pressure fluctuation,a novel electro-hydraulic fuel metering unit(FMU)is constructed for afterburner fuel system of military *** from the previous FMU,the proposed FMU can achieve the higher precision opening control by a new metering valve with double control chambers(MVDCC),and realize the lower pressure difference fluctuation regulating by a novel two-stage constant pressure difference compensated valve(CPDCV)with dynamic damping orifice and damping *** experimental and AMESim simulation results verify the validity and superiority of the novel *** the temperature-induced variation in fuel properties and device capabilities may degrade or even impair the properties of novel FMU,the discharge flowrate is analyzed by global sensitivity analysis to research the effect proportion of each factor,the temperature effect is explored to ensure the working reliability in long-span temperature ***,the optimization of structure parameters for novel CPDCV can further reduce pressure difference fluctuation during pressure regulation,and the overshoot,adjust time and the integral of time multiplied by absolute value of error(ITAE)can be reduced by 24%,30%and 26%,*** paper provides a reference for improving the stability of large flowrate during pressure fluctuation.

关键词： Aeroengine Fuel metering unit Large flowrate control Optimization Pressure difference compensation

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Discovering Latent Variables for the Tasks With Confounders in Multi-Agent Reinforcement Learning

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IEEE/CAA Journal of Automatica Sinica 2024年第7期11卷 1591-1604页

作者： Kun Jiang Wenzhang Liu Yuanda Wang Lu Dong Changyin Sun the School of Automation Southeast UniversityNanjing 210096China the School of Artificial Intelligence Anhui UniversityHefei 230601China IEEE the School of Cyber Science and Engineering Southeast UniversityNanjing 211189China the Engineering Research Center of Autonomous Unmanned System Technology Ministry of EducationHefei 230601China

Efficient exploration in complex coordination tasks has been considered a challenging problem in multi-agent reinforcement learning(MARL). It is significantly more difficult for those tasks with latent variables that agents cannot directly observe. However, most of the existing latent variable discovery methods lack a clear representation of latent variables and an effective evaluation of the influence of latent variables on the agent. In this paper, we propose a new MARL algorithm based on the soft actor-critic method for complex continuous control tasks with confounders. It is called the multi-agent soft actor-critic with latent variable(MASAC-LV) algorithm, which uses variational inference theory to infer the compact latent variables representation space from a large amount of offline ***, we derive the counterfactual policy whose input has no latent variables and quantify the difference between the actual policy and the counterfactual policy via a distance function. This quantified difference is considered an intrinsic motivation that gives additional rewards based on how much the latent variable affects each agent. The proposed algorithm is evaluated on two collaboration tasks with confounders, and the experimental results demonstrate the effectiveness of MASAC-LV compared to other baseline algorithms.

关键词： Latent variable model maximum entropy multi-agent reinforcement learning(MARL) multi-agent system

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Human Cognitive Learning in Shared Control via Differential Game With Bounded Rationality and Incomplete Information

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第10期5卷 5141-5152页

作者： Wu, Huai-Ning Jiang, Xiao-Yan Wang, Mi Beihang University Science and Technology on Aircraft Control Laboratory School of Automation Science and Electrical Engineering Beijing100191 China Hangzhou International Innovation Institute of Beihang University Hangzhou311115 China Beihang University School of Automation Science and Electrical Engineering Beijing100191 China

Since human beings are of limited reasoning ability as well as the machines do not usually know human intentions, how to learn human cognitive levels in shared control to enhance the machines’ intelligence is a challenging issue. In this study, this issue is addressed in the context of human–machine shared control for a class of human-in-the-loop (HiTL) systems based on a differential game with bounded rationality and incomplete information. Initially, we formulate the human–machine shared control problem as a two-player nonzero-sum linear quadratic dynamic game (LQDG), where the weighting matrix of the cost function representing the human intention is unknown for the machine. To model the human bounded rationality, the level-k (LK) approach is employed to set up the LK control policies of two players performing the corresponding steps of strategic thinking. To infer the human intention, an online adaptive inverse optimal control (IOC) algorithm is then developed by using the system state data, so that the control policies of different cognitive levels can be computed. In addition, a reinforcement learning method is proposed for the machine to identify the distribution of the human cognitive levels while providing a proactive collaborative control to assist the human in a probabilistic switching way. Finally, simulation results on a cooperative shared control driver assistance system (DAS) illustrate the efficacy of the proposed approach. © 2024 IEEE.

关键词： Game theory

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Regional Multi-Agent Cooperative Reinforcement Learning for City-Level Traffic Grid Signal Control

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IEEE/CAA Journal of Automatica Sinica 2024年第9期11卷 1987-1998页

作者： Yisha Li Ya Zhang Xinde Li Changyin Sun School of Automation Southeast UniversityNanjing 210096 Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of EducationSoutheast UniversityNanjing 210096China

This article studies the effective traffic signal control problem of multiple intersections in a city-level traffic system.A novel regional multi-agent cooperative reinforcement learning algorithm called RegionSTLight is proposed to improve the traffic *** a regional multi-agent Q-learning framework is proposed,which can equivalently decompose the global Q value of the traffic system into the local values of several regions Based on the framework and the idea of human-machine cooperation,a dynamic zoning method is designed to divide the traffic network into several strong-coupled regions according to realtime traffic flow *** order to achieve better cooperation inside each region,a lightweight spatio-temporal fusion feature extraction network is *** experiments in synthetic real-world and city-level scenarios show that the proposed RegionS TLight converges more quickly,is more stable,and obtains better asymptotic performance compared to state-of-theart models.

关键词： Human-machine cooperation mixed domain attention mechanism multi-agent reinforcement learning spatio-temporal feature traffic signal control

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Fault Diagnosis of Misalignment Based on Wavelet and Attention Convolutional Neural Network 7

Fault Diagnosis of Misalignment Based on Wavelet and Attenti...

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7th IEEE International Electrical and Energy Conference, CIEEC 2024

作者： Li, Jianye Yang, Ming Wang, Jing School of Electrical Engineering and Automation Harbin Institute of Technology China

ISBN: (纸本)9798350359558

Servo motor and transmission device are important equipment in modern industrial production. However, after long-term use, it is inevitable that faults will occur. Therefore, identifying faults in equipment holds significant importance in preventing unforeseen losses. In this paper, we present a data-driven intelligent fault diagnosis approach for parallel misalignment based on a novel continuous wavelet transform-attention convolutional neural network (CWT-ACNN) model. Firstly, the current signals are collected and labeled as 8 fault types, and then Morlet wavelet transform is used to convert the current signals of time series into time-frequency feature images. After changing the size of the images, they are fed into a convolutional neural network with an attention mechanism to identify fault types. Experimental results show that the CWT-ACNN proposed in this paper can achieve 99.79% accuracy, which is better than the existing methods for fault classification. © 2024 IEEE.

关键词： Failure analysis

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ESO-based Adaptive Neural Network Control for Quadrotors under Multiple Uncertainties

ESO-based Adaptive Neural Network Control for Quadrotors und...

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2024 China automation Congress, CAC 2024

作者： Zhang, Xinyue Shen, Jiajun Wang, Wei Wang, Zhenqian School of Automation Science and Electrical Engineering Beihang University Beijing China School of Automation Science and Electrical Engineering Beihang University Zhongguancun Laboratory Beijing China

ISBN: (纸本)9798350368604

This paper introduces a robust framework for mitigating internal and external disturbances in quadrotor systems. Specifically, a radial basis function neural network (RBF-NN) is utilized for the estimation of model uncertainties, while extended state observer (ESO) compensates for external disturbances and RBF-NN approximation errors. This dual estimation mechanism provides stronger theoretical guarantees, enhancing the system’s capacity to handle complex scenarios. Additionally, a Lyapunov-based adaptive control strategy is employed to dynamically adjust control gains, managing variations in thrust and torque coefficients due to rotor dynamics. The incorporation of a projection operator in the parameter update law ensures the boundedness of parameter estimates. The proposed method demonstrates improved control accuracy and stability under multiple unmodeled uncertainties. © 2024 IEEE.

关键词： Disturbance rejection

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