检索结果-内蒙古大学图书馆

Adaptive Prescribed Finite-Time Bipartite Consensus control for Nonaffine Nonlinear MASs Under Structurally Unbalanced Topology

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IEEE Transactions on Systems, Man, and Cybernetics: Systems 2025年第5期55卷 3532-3541页

作者： Wang, Xiaomei Song, Yongduan Zhao, Xudong Wang, Huanqing Wang, Ding Niu, Ben Chongqing University School of Automation Chongqing400044 China Shandong Normal University School of Information Science and Engineering Shandong Jinan250014 China Dalian University of Technology School of Control Science and Engineering Liaoning Dalian116023 China Bohai University College of Mathematical Sciences Liaoning Jinzhou121013 China Beijing University of Technology Faculty of Information Technology Beijing100124 China Sichuan University School of Electrical Engineering Chengdu610065 China

This article investigates an adaptive bipartite consensus tracking control algorithm for a class of heterogeneous nonaffine nonlinear multiagent systems (MASs) with prescribed finite-time tracking performance under an unbalanced communication topology. In the case of an unbalanced digraph, a novel locally optimal bipartition strategy is proposed to transform the unbalanced communication topology into a structurally balanced one, thereby enabling the implementation of bipartite consensus tracking control. To achieve the expected tracking performance, the design philosophy focuses on developing a prescribed finite-time performance function (PFTPF), capable of preassigning the convergence time and accuracy precisely beforehand. The explored adaptive control algorithm can ensure that the whole signals concerning the closed-loop MASs remain bounded while the bipartite consensus errors converge to a predetermined range around zero within the prescribed finite time. Ultimately, the simulation results on robotic systems prove the availability of the developed design solution. © 2013 IEEE.

关键词： Topology

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Short Term Photovoltaic Power Combined Forecasting Based on Feature Extraction 4

Short Term Photovoltaic Power Combined Forecasting Based on ...

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4th International Conference on Power engineering, ICPE 2023

作者： Chen, Qingbin Yang, Genghuang Su, Juan School of Automation and Electrical Engineering Tianjin University of Technology and Education Tianjin China Tianjin Key Laboratory of Information Sensing and Intelligent Control Tianjin China College of Information and Electrical Engineering China Agricultural University Beijing China

ISBN: (纸本)9798350340020

The proposed method aims to address the randomness and volatility of photovoltaic power generation by forecasting short-term photovoltaic power combinations using feature extraction. The method incorporates the firefly sparrow algorithm to optimize the parameters of variational mode decomposition and obtain the signal component with the best forecasting effect. Because the signal components contain different influence characteristics and time series information, the maximum information coefficient is used to screen the characteristics of each signal component, and the feature matrix is established. The zero-crossing rate is used as the index to determine the demarcation point of high and low-frequency signals. The proposed model in the paper aims to enhance the accuracy and stability of short-term photovoltaic power generation forecasting by leveraging the distinct characteristics of high and low-frequency signals. It achieves this by combining the strengths of the long short-term memory neural network and the multi-layer perceptron to handle high and low-frequency signal components, respectively. The model's performance is compared with a baseline model called VMD-LSTM-MLP, as well as other existing forecasting models, under various weather conditions. © 2023 IEEE.

关键词： Forecasting

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Fault prediction of electric vehicle charging stations based on cooperative game strategy and DBO-BiLSTM 6

Fault prediction of electric vehicle charging stations based...

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6th IEEE Advanced Information Management, Communicates, Electronic and automation control Conference, IMCEC 2024

作者： Tian, Liguo Shang, Chunhu Li, Meng Chen, Qingbin Tianjin University of Technology and Education College of Automation and Electrical Engineering Tianjin China Tianjin University of Technology and Education Tianjin Key Laboratory of Information Sensing and Intelligent Control Tianjin China

ISBN: (纸本)9798350316520

Aiming at the problem of high failure rate of electric vehicle charging pile, an electric vehicle charging pile failure prediction method based on cooperative game strategy and dung beetle optimisation algorithm-bidirectional long and short-term memory network (DBO-BiLSTM) is proposed. Firstly, the outliers are handled by parameter statistical distribution, the missing values are handled by mean padding, and the normalisation operation is performed on the processed data;secondly, multiple single-assignment methods are selected to calculate the feature weights, the combined weights are calculated by the cooperative game strategy, and the parameter feature matrix is enlarged;and then, the DBO-BiLSTM model is constructed, and under the simulation experiments, the accuracy rate of the training set and test set, the F1 coefficients are 0.89, 0.89, 0.90 and 0.89, ***, relevant comparison experiments are constructed, and the results show that the proposed model has better performance, which verifies the effectiveness and reasonableness of the proposed model. © 2024 IEEE.

关键词： Brain

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A policy gradient algorithm integrating long and short-term rewards for soft continuum arm control

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Science China(Technological Sciences) 2022年第10期65卷 2409-2419页

作者： DONG Xiang ZHANG Jing CHENG Long XU WenJun SU Hang MEI Tao School of Electrical Engineering and Automation Anhui UniversityHefei 230601China State Key Laboratory for Control and Management of Complex Systems Institute of AutomationChinese Academy of SciencesBeijing 100190China Robotics Research Center Peng Cheng LaboratoryShenzhen 518055China

The soft continuum arm has extensive application in industrial production and human life due to its superior safety and flexibility. Reinforcement learning is a powerful technique for solving soft arm continuous control problems, which can learn an effective control policy with an unknown system model. However, it is often affected by the high sample complexity and requires huge amounts of data to train, which limits its effectiveness in soft arm control. An improved policy gradient method, policy gradient integrating long and short-term rewards denoted as PGLS, is proposed in this paper to overcome this issue. The shortterm rewards provide more dynamic-aware exploration directions for policy learning and improve the exploration efficiency of the algorithm. PGLS can be integrated into current policy gradient algorithms, such as deep deterministic policy gradient(DDPG). The overall control framework is realized and demonstrated in a dynamics simulation environment. Simulation results show that this approach can effectively control the soft arm to reach and track the targets. Compared with DDPG and other model-free reinforcement learning algorithms, the proposed PGLS algorithm has a great improvement in convergence speed and performance. In addition, a fluid-driven soft manipulator is designed and fabricated in this paper, which can verify the proposed PGLS algorithm in real experiments in the future.

关键词： soft arm control Cosserat rod deep reinforcement learning policy gradient algorithm high sample complexity

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Research on Speech Recognition Methods Based on Spectral Subtraction and Improved BP Neural Network Algorithm 7

Research on Speech Recognition Methods Based on Spectral Sub...

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7th IEEE Information Technology, Networking, Electronic and automation control Conference, ITNEC 2024

作者： Ma, Yuqi Zhang, Yunxi Wang, Zihan Zhao, Ya Liu, Jia School of Automation and Electrical Engineering Tianjin University of Technology and Education Tianjin China Tianjin University of Technology and Education Tianjin Key Laboratory of Information Sensing and Intelligent Control Tianjin China

ISBN: (纸本)9798350370782

This paper presents a speech recognition method combining spectral subtraction with improved BP neural network algorithm. The speech samples containing unstable noise are collected in the real environment. Firstly, the noise from speech signals is removed using spectral subtraction method, and the speech feature parameters after noise removal are extracted. Then the speech is recognized effectively by the improved BP neural network algorithm. Finally, some comparative experiments are given to verify the effectiveness of the proposed method. © 2024 IEEE.

关键词： Speech enhancement

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Robust H∞ Consensus control for Heterogeneous Multi-agent Systems Based on Time-delay and Switching Topology 4

Robust H∞ Consensus Control for Heterogeneous Multi-agent S...

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4th IEEE International Conference on Information Technology, Big Data and Artificial Intelligence, ICIBA 2024

作者： Du, Jiayi Liu, Jia Cao, Runxi Zhang, Yijing Zhang, Yunxi Tianjin University of Technology and Education School of Automation and Electrical Engineering Tianjin China Tianjin University of Technology and Education Tianjin Key Laboratory of Information Sensing and Intelligent Control Tianjin China

ISBN: (纸本)9798350363586

This study proposes a consensus control strategy for heterogeneous multi-agent systems characterized by uncertain parameters and external disturbances, to address the intricate robust control challenges posed by time-varying delays and dynamic network topologies. A distributed control protocol is design to make the states of the heterogeneous multi-agent system be consistent. Then the stability of the control protocol is analyzed by Lyapunov-Krasovskii stability theory and integral inequality techniques, and the sufficient conditions for the system to achieve robust H∞ consistency are obtained. Finally, the results of the simulations verify the accuracy of the theoretical analysis presented in this paper. © 2024 IEEE.

关键词： Multi agent systems

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Speed Profile Optimization for Autonomous Electric Bus in Urban Area: Based on Service Route Parameter in Thailand

Speed Profile Optimization for Autonomous Electric Bus in Ur...

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2023 International Conference on Power, Energy and Innovations, ICPEI 2023

作者： Sakulphaisan, Guntinan Son-In, Sirirat Junhuathon, Nitikorn Muankhaw, Theerapol Pathumwan Institute of Technology Faculty of Science and Technology Industrial Technology and Innovation Management Bangkok Thailand Bansomdejchaopraya Rajabhat University Faculty of Engineering and Industrial Technology Electrical Engineering and Automation Control Systems Bangkok Thailand Rajamangala University of Technology Faculty of Engineering Department of Electrical Engineering Thanyaburi Pathumthani Thailand

ISBN: (纸本)9798350344585

This study conducts a comprehensive analysis of optimizing the speed profile of an AE-bus, a critical step in enhancing its operational efficiency. It encompasses three distinct case studies, each targeting specific objectives. The first case emphasizes energy consumption reduction, contributing to overall resource efficiency. The second focuses on eco-conscious speed profiles within predefined travel durations, balancing environmental sustainability and timeliness. The third case prioritizes travel time minimization for streamlined efficiency. Detailed insights into speed adjustments, bus positions, and energy consumption patterns are provided for each case. The findings reveal tailored approaches for energy conservation, speed, and travel time optimization. Additionally, emissions analysis demonstrates that electric buses significantly reduce CO2 emissions compared to conventional NGV buses, showcasing their potential for environmental benefits. © 2023 IEEE.

关键词： Electric buses

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Low-Carbon Optimization Dispatch of Multi-Integrated Energy System Based on Kriging Metamodel and Stackelberg Game 16

Low-Carbon Optimization Dispatch of Multi-Integrated Energy ...

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16th IEEE PES Asia-Pacific Power and Energy engineering Conference, APPEEC 2024

作者： Zhang, Zhaoqing Sun, Yonghui Sheng, Fan Yin, Chenxu Sidorov, Denis N. Iskakov, Alexey B. School of Electrical and Power Engineering Hohai University Nanjing China Melentiev Energy System Institute Russian Academy of Sciences Irkutsk Russia School of Electrical Engineering and Automation Harbin Institute of Technology Harbin China Trapeznikov Institute of Control Sciences Russian Academy of Sciences Moscow Russia

ISBN: (纸本)9798350386127

Integrated energy system (IES) is an advanced configuration that combines electrical energy, thermal energy, and renewable energy, into a unified system. In recent years, the architecture of IES has gradually changed from vertical integration to interactive competition, and its distributed characteristics have become increasingly evident. Therefore, traditional centralized optimization strategies can no longer meet the current practical situation. In this paper, a low-carbon optimization dispatch for IES based on Stackelberg game and kriging metamodel is proposed. In order to address this problem, firstly, three typical models containing hydrogen blending gas technology and a kind of ladder-type carbon trading mechanism considering reward and punishment (RP-LCTM) are established. Secondly an integrated energy manager (IEM) is introduced and a Stackelberg game model with the IEM as the leader and three typical IESs as followers is established. Finally, the kriging metamodel is used to optimize the transaction electricity price. During the game process, the IPSO algorithm is used to optimize electricity prices in key price regions. At last, an experimental example is utilized to demonstrate the effectiveness of the proposed method. © 2024 IEEE.

关键词： Carbon sequestration

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Target Assignment in the Multi-UAV Combat Based on Distributed Compensation Auction Algorithm with Fixed-Time Consensus Protocol 7th

Target Assignment in the Multi-UAV Combat Based on Distri...

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7th Chinese Conference on Swarm Intelligence and Cooperative control, CCSICC 2023

作者： Chen, Yiyang Hua, Yongzhao Yu, Jianglong Dong, Xiwang Institute of Artificial Intelligence Beihang University Beijing100191 China School of Automation Science and Electrical Engineering Science and Technology on Aircraft Control Laboratory Beihang University Beijing100191 China

ISBN: (纸本)9789819733231

This paper mainly focuses on the target assignment problems in the unmanned aerial vehicle (UAV) swarm combat. Firstly, the idea of the multi-UAV swarm combat is shown and the constraints and the benefit matrix of the target assignment task are modeled. Secondly, a fixed-time consensus protocol is utilized to estimate the state of all the other agents and its convergence is proved by Lyapunov method. Then, a distributed compensation auction algorithm is proposed. Finally, the proposed distributed compensation auction algorithm is simulated and the results demonstrate the successful completion of the assignment task with only neighboring information. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Lyapunov methods

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State of Charge Estimation for Lithium Batteries Based on the Square Root Cubature Kalman Filter Algorithm 4

State of Charge Estimation for Lithium Batteries Based on th...

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4th International Conference on Energy engineering and Power Systems, EEPS 2024

作者： Liu, Hanhan Su, Jun School of Electrical Engineering and Automation Xiamen University of Technology Xiamen China Xiamen University of Technology Xiamen Key Laboratory of Frontier Electric Power Equipment and Intelligent Control Xiamen China

ISBN: (纸本)9798350366914

The State of Charge of lithium batteries is a crucial parameter for the battery management system in electric vehicles and energy storage applications. Accurate estimation of the State of Charge is of significant importance for the design of the battery management system. To address the problem of accurately estimating the State of Charge, this paper employs the square root cubature Kalman filter algorithm in conjunction with a second-order resistance-capacitance equivalent circuit model of lithium batteries for precise State of Charge estimation. This study utilizes the open-source experimental dataset of the Panasonic NCR18650PF lithium battery and identifies the parameters of the second-order resistance-capacitance equivalent circuit model through the forgetting factor recursive least squares method. To verify the estimation accuracy of the Square Root Cubature Kalman Filter algorithm, the algorithm is simulated and tested using the Urban Dynamometer Driving Schedule conditions within the experimental dataset. The estimation accuracy of the Square Root Cubature Kalman Filter algorithm is compared with that of the Extended Kalman Filter algorithm and the Unscented Kalman Filter algorithm under the same conditions. Simulation test results indicate that the Square Root Cubature Kalman Filter algorithm achieves higher accuracy in estimating the State of Charge of lithium batteries. © 2024 IEEE.

关键词： State of charge

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