检索结果-内蒙古大学图书馆

Research on Two-stage Isolated AC-DC Converter with PSO Optimized PI Control

JOURNAL OF ELECTRICAL ENGINEERING & TECHNOLOGY 2024年第3期19卷 1619-1631页

作者： Zhou, Kai Li, Zheng Wu, Xiaogang Harbin Univ Sci & Technol Engn Res Ctr Automot Elect Drive Control & Syst In Minist Educ Harbin Peoples R China Tsinghua Univ State Key Lab Automot Safety & Energy Beijing Peoples R China

AC-DC converter has the advantages of high power density, stable output, easy to control, etc., and is widely used in many industrial fields. In this paper, the two-stage isolated AC-DC converter is the object of study, and the converter consists of a front-stage Boost PFC converter and a rear-pole LLC resonant converter. The small signal perturbation analysis method and the extended function method are used to construct the equivalent mathematical models of the PFC converter and the LLC resonant converter, and analyze their input and output characteristics and frequency response characteristics. The control strategy based on particle swarm algorithm optimized PI is proposed, and the PSO algorithm optimized PI loop control system is designed on this basis. Simulation software is used to compare the performance indexes of PSO optimized PI control method, and an experimental platform is built to verify it. The results show that the isolated converter can not only meet the requirement of sinusoidal input current on the grid side, but also realize the electrical isolation and ensure the output voltage stability. The PSO algorithm is also introduced to automatically adjust the PI parameters according to the operating state of the converter, so that a faster regulation speed can be obtained during the startup of the converter and the sudden change of the load, and the system as a whole has a better dynamic characteristic and steady state characteristic, and the simulation analysis and experimental sessions verify the correctness of the circuit design and the effectiveness of the control strategy.

关键词： AC-DC converter Two-stage isolated Power factor correction particle swarm optimization algorithm

来源：评论

学校读者我要写书评

暂无评论

Enhanced Anti-Lock Braking System Performance: A Comparative Study of Adaptive Terminal Sliding Mode Control Approaches

VEHICLES

引用

VEHICLES 2025年第1期7卷 14-14页

作者： Khatory, Salma Chafouk, Houcine Mellouli, El Mehdi Natl Sch Appl Sci LISA Lab Fes 30050 Morocco Rouen Normandie Univ ESIGELEC IRSEEM Lab F-76000 Rouen France

Sliding Mode Control (SMC) has gained significant attention due to its simplicity, robustness, and rapid response in ensuring system stability, particularly with the Lyapunov approach. Despite its advantages, SMC faces challenges such as chattering near equilibrium, sensitivity to parameter variations, and delayed convergence. To address these issues, advanced techniques like Terminal Sliding Mode Control (TSMC) and Integral Terminal Sliding Mode Control (ITSMC) have been proposed. TSMC ensures finite-time convergence while mitigating chattering, while ITSMC further handles singularities and disturbances. Additionally, Adaptive Switching Control (ASC) based on particle swarm optimization (PSO) is applied to achieve faster convergence, suppress chattering, and enhance system robustness. The adaptive control law, utilizing a Lyapunov-based approach, is employed to estimate and compensate for external disturbances, further improving system performance under uncertainties. Gain tuning, essential for optimizing system performance and reducing tracking errors, is achieved using the efficient Teaching-Learning-Based optimization (TLBO) algorithm. This study applies TSMC, ITSMC, and ASC-based PSO to an Anti-Lock Braking System (ABS), aiming to enhance robustness, stability, and finite-time convergence while reducing chattering. Stability is analyzed through the Lyapunov theory, ensuring rigorous validation. MATLAB simulations demonstrate the effectiveness of the proposed methods in improving ABS performance, offering a valuable contribution to robust control techniques for systems operating under dynamic and uncertain conditions.

关键词： terminal sliding mode control integral TSMC adaptive control law anti-lock braking system gain-tuning particle swarm optimization algorithm finite-time convergence chattering reduction Lyapunov

来源：评论

学校读者我要写书评

暂无评论

Instrumentation Amplifier Input Impedance Calibration With Machine Learning-Based optimizations

引用

IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS II-EXPRESS BRIEFS 2025年第2期72卷 394-398页

作者： Abdelfattah, Safaa Hussein, Hussein M. E. Shrivastava, Aatmesh Onabajo, Marvin Northeastern Univ Dept Elect & Comp Engn Boston MA 02115 USA

This brief introduces a digital calibration technique to boost the input impedance of instrumentation amplifiers (IAs) with digitally tunable input impedance. The technique employs two machine learning-driven optimization algorithms, the genetic algorithm (GA) and the particle swarm optimization (PSO) algorithm, to efficiently control integrated capacitor banks within the IA for the determination of the optimal input impedance. These algorithms offer a significant time reduction compared to a calibration with an exhaustive search, reducing calibration time by a factor of over 10(6) (with four 9-bit digital control words) while conserving computational resources. A prototype platform was developed to automatically optimize a fabricated IA test chip designed with 65-nm CMOS technology, which allows to test the machine learning algorithms using a microcontroller to control the digitally tunable input impedance. With an extra input capacitance of 100 pF, the GA algorithm achieved an input impedance of 1.75 G Omega after four generations (iterations), while the PSO algorithm achieved 1.27 G Omega with five iterations.

关键词： Impedance optimization Genetic algorithms Capacitors Calibration Capacitance Machine learning algorithms Cables particle swarm optimization Impedance measurement Input impedance calibration instrumentation amplifier genetic algorithm particle swarm optimization algorithm negative capacitance generation feedback (NCGFB)

来源：评论

学校读者我要写书评

暂无评论

Fuzzy Adaptive Whale optimization Control algorithm for Trajectory Tracking of a Cable-Driven Parallel Robot

引用

IEEE TRANSACTIONS ON AUTOMATION SCIENCE AND ENGINEERING 2024年第4期21卷 5149-5160页

作者： Zhou, Bin Wang, Yuhang Zi, Bin Zhu, Weidong Hefei Univ Technol Sch Mech Engn Hefei 230027 Peoples R China Univ Maryland Baltimore Cty Dept Mech Engn Baltimore MD 21250 USA

This paper proposes a fuzzy proportion integration differentiation (PID) control strategy based on an adaptive whale optimization algorithm (FPID-AWOA) for trajectory tracking of a cable-driven parallel robot (CDPR). A mechanical prototype, and kinematic and dynamic models of the CDPR are established. Thus, new fuzzy rules are developed and a new fuzzy PID controller is designed. Subsequently, the AWOA is introduced to optimize quantization and scale factors of the fuzzy PID controller to obtain the optimal solution. Among them, AWOA is an improvement on WOA. Numerical examples show that the fuzzy PID control strategy based on adaptive whale optimization algorithm (FPID-AWOA) has higher CDPR trajectory tracking accuracy than the traditional fuzzy PID control strategy, the fuzzy PID control strategy based on whale optimization algorithm (FPID-WOA), and the fuzzy PID control strategy based on particle swarm optimization (FPID-PSOA). In comparison with the FPID and FPID-PSOA, the experimental results show that the trajectory tracking error of the proposed FPID-AWOA is reduced by 51.2% and 19.5% in the X-axis direction, respectively, 64.2% and 49.7% in the Y-axis direction, respectively, and 29.1% and 12.2% in the Z-axis direction, respectively.

关键词： Cable-driven parallel robot fuzzy control particle swarm optimization algorithm trajectory tracking whale optimization algorithm

来源：评论

学校读者我要写书评

暂无评论

Optimized FOPID controller for nuclear research reactor using enhanced planet optimization algorithm

引用

ALEXANDRIA ENGINEERING JOURNAL 2024年 97卷 267-282页

作者： Abdelfattah, Hany Aseeri, Ahmad O. Abd Elaziz, Mohamed Suez Univ Fac Technol & Educ Elect Dept Suez 41522 Egypt Prince Sattam Bin Abdulaziz Univ Coll Comp Engn & Sci Dept Comp Sci Al Kharj 11942 Saudi Arabia Zagazig Univ Fac Sci Dept Math Zagazig 44519 Egypt Galala Univ Fac Comp Sci & Engn Suze 435611 Egypt Ajman Univ Artificial Intelligence Res Ctr AIRC Ajman 346 U Arab Emirates Lebanese Amer Univ Dept Elect & Comp Engn Byblos 135053 Lebanon Middle East Univ MEU Res Unit Amman 11831 Jordan

Nuclear reactor control is pivotal for the safe and efficient operation of nuclear power plants, facilitating the regulation of reactor reactivity. This study introduces an optimized fractional-order proportional-integral-derivative (FOPID) controller tailored for maintaining reactivity levels in nuclear power plants, particularly during load-following operations. The controller adjusts the position of control rod to regulate power output effectively. We enhance FOPID controller's performance using a modification of Planet optimization algorithm (POA-M), leveraging the strengths of the Arithmetic optimization algorithm (AOA) to improve its exploitation capabilities. We evaluate the efficacy of POA-M-FOPID controller against traditional techniques, including POA, AOA, and particle swarm optimization (PSO). We assess its performance using the Egyptian Testing Research Reactor (ETRR-2) as a case study. Our results demonstrate that the POA-M-FOPID controller outperforms alternative algorithms across various control metrics, exhibiting superior resilience and efficiency. Notably, the utilization of the POA-M-FOPID controller yields remarkable improvements in reactor power performance, achieving significantly reduced settling time (25.27 sec) and maximum overshoot (0.67 %) compared to conventional FOPID controllers incorporating POA, AOA, and PSO methods. These findings underscore the effectiveness of POA-MFOPID in enhancing nuclear reactor control systems, offering potential benefits for broader nuclear power industry in terms of safety, stability, and operational efficiency.

关键词： Planet optimization algorithm Arithmetic optimization algorithm particle swarm optimization algorithm fractional-order proportional-integral-deriva tive (FOPID) controller

来源：评论

学校读者我要写书评

暂无评论

optimization process of designing and manufacturing of cylinder block system in cast iron industry using metaheuristic computing technique

引用

INTERNATIONAL JOURNAL OF INTERACTIVE DESIGN AND MANUFACTURING - IJIDEM 2025年第3期19卷 2203-2222页

作者： Urvashi, Shikha Bansal, Shikha SRM Inst Sci & Technol Dept Math Delhi NCR Campus Ghaziabad 201204 India

A cylinder block system is a complicated component within a cast iron manufacturing structure, involving several interaction subsystems throughout the production process. Given the increasing need for efficient automotive components, optimizing the design and manufacturing processes of this system to enhance its performance and production efficiency is imperative. The primary aim of this work is to design and enhance the performance of the cylinder block system utilizing a hybrid metaheuristic approach, specifically the genetic algorithm (GA) and particle swarm optimization (PSO) algorithm. The algorithms are executed in MATLAB-R2022 software for the computation of numerical results for the performance of the system. The system's mathematical model is constructed using the Markov birth-death process, and the differential associated with that model is generated from the system's state transition diagram. The Markov birth-death technique examines the system's performance and determines which subsystem is more critical and needs greater maintenance. The system's performance is enhanced through the utilization of the PSO and GA algorithms. The system's optimism parameters value is determined using these algorithms, varying both population size and generation size;these optimal parameters help the casting production industry to enhance system performance.

关键词： Design Genetic algorithm Maintenance Markov approach Performance particle swarm optimization algorithm Steady-state availability

来源：评论

学校读者我要写书评

暂无评论

PSO-GA Hyperparameter Optimized ResNet-BiGRU-Based Intrusion Detection Method

引用

IEEE ACCESS 2024年 12卷 135535-135550页

作者： Xia, Zhixin He, Siyuan Liu, Changwei Liu, Yongshan Yang, Xiaolei Bu, Huifeng Yanshan Univ Sch Informat Sci & Engn Qinhuangdao 066004 Peoples R China Qinhuangdao Port Co Ltd Branch Co 9 Qinhuangdao 066004 Peoples R China

Intrusion detection systems (IDS) identify network intrusions by detecting abnormal traffic data, thereby ensuring network security. However, intrusion detection data can vary with changes in the network and attack environment, resulting in poor performance and portability of intrusion detection algorithms. Therefore, an intrusion detection method based on PSO-GA hyperparameter optimized ResNet-BiGRU is proposed. The two-layer bidirectional gated recurrent unit (BiGRU) is connected to the fully connected layer of the residual neural network (ResNet). Firstly, ResNet is used to extract parallel local features, and BiGRU is used to extract long-distance-dependent features from the parallel local features, and the attention mechanism is added after the BIGRU to utilize correlation between the features to assign weights to the extracted features, so as to more comprehensively capture the important features of network intrusion and improve the detection performance. At the same time, the parameters of the basic particle swarm optimization (PSO) are dynamically optimized and combined with the genetic algorithm (GA) to perform a mutation operation when the iterative process falls into a local optimal solution, adding a random perturbation to the current velocity and position of the particles, so that the particles are able to explore new regions in the space in order to jump out of the local optimal solution, and ultimately achieve automatic optimization of the hyperparameters of the ResNet-BiGRU model to achieve a model with better generalization performance. Finally, the proposed method is validated by using the variant NSL-KDD dataset, which achieves an accuracy of 98.46% and average precision, average recall and average False Alarm Rate(FAR) of 91.84%, 95.99% and 0.31%, and achieved high accuracy on three datasets KDD99, UNSW-NB15, and CIC-IDS 2017. The method is proved to have a strong intrusion detection capability by comparison experiments with other algorithms.

关键词： Feature extraction Intrusion detection optimization Deep learning Accuracy Convolutional neural networks Adaptation models Bidirectional control particle swarm optimization Residual neural networks Bidirectional gated recurrent unit intrusion detection particle swarm optimization algorithm residual neural network

来源：评论

学校读者我要写书评

暂无评论

Multi-Objective optimization Analysis of Electromagnetic Performance of Permanent Magnet Synchronous Motors Based on the PSO algorithm

引用

ENERGIES 2024年第18期17卷 4637页

作者： Cen, Yufei Shen, Haoyu Wang, Xiaoyuan Wu, Yongming Du, Jingjuan Changan Univ Sch Construct Machinery Xian 710064 Peoples R China Tianjin Univ Sch Elect & Informat Engn Tianjin 300072 Peoples R China Leadr Technol Liuzhou Co Ltd Liuzhou 545000 Peoples R China Tianjin Chengjian Univ Sch Control & Mech Engn Tianjin 300384 Peoples R China

In order to optimize the electromagnetic performance of a permanent magnet synchronous motor (PMSM) during operation, this paper takes the size of the stator slot structure of the motor as the optimization variable and the peak cogging torque and no-load back electromotive force (EMF) amplitude of the motor as the optimization objectives. A multi-objective optimization method based on the particle swarm optimization (PSO) algorithm is adopted to obtain a structural parameter combination that minimizes the peak cogging torque and no-load back EMF amplitude while meeting the reasonable range requirements of magnetic flux density amplitude. The optimized motor structure design prototype is experimentally verified. The results show that through multi-objective optimization based on the PSO algorithm, the electromagnetic performance of the motor has been improved, with a reduction of 36.33% in peak cogging torque and 2.65% in peak no-load back EMF, indicating a reasonable magnetic flux density amplitude. The experimental results of the optimized prototype show that the difference between the theoretical simulation values and the experimental values is within a reasonable range, which verifies the effectiveness of the multi-objective optimization method.

关键词： permanent magnet synchronous motor particle swarm optimization algorithm multi-objective optimization electromagnetic performance

来源：评论

学校读者我要写书评

暂无评论

Measuring moisture content of dead fine fuels based on the fusion of spectrum meteorological data

引用

Journal of Forestry Research 2023年第5期34卷 1333-1346页

作者： Bo Peng Jiawei Zhang Jian Xing Jiuqing Liu Mingbao Li Northeast Forestry University Harbin 150040People’s Republic of China

Dead fine fuel moisture content(DFFMC)is a key factor affecting the spread of forest fires,which plays an important role in evaluation of forest fire *** order to achieve high-precision real-time measurement of DFFMC,this study established a long short-term memory(LSTM)network based on particle swarm optimization(PSO)algorithm as a measurement model.A multi-point surface monitoring scheme combining near-infrared measurement method and meteorological measurement method is *** near-infrared spectral information of dead fine fuels and the meteorological factors in the region are processed by data fusion technology to construct a spectral-meteorological data *** surface fine dead fuel of Mongolian oak(Quercus mongolica *** Ledeb.),white birch(Betula platyphylla Suk.),larch(Larix gmelinii(Rupr.)Kuzen.),and Manchurian walnut(Juglans mandshurica Maxim.)in the maoershan experimental forest farm of the Northeast Forestry University were *** used the PSO-LSTM model for moisture content to compare the near-infrared spectroscopy,meteorological,and spectral meteorological fusion *** results show that the mean absolute error of the DFFMC of the four stands by spectral meteorological fusion method were 1.1%for Mongolian oak,1.3%for white birch,1.4%for larch,and 1.8%for Manchurian walnut,and these values were lower than those of the near-infrared method and the meteorological *** spectral meteorological fusion method provides a new way for high-precision measurement of moisture content of fine dead fuel.

关键词： Near infrared spectroscopy Meteorological factors Data fusion Long-term and short-term memory network particle swarm optimization algorithm

来源：评论

学校读者我要写书评

暂无评论

A multiscale and multivariable differentiated learning for carbon price forecasting

引用

ENERGY ECONOMICS 2024年 131卷

作者： Chen, Linfei Zhao, Xuefeng Huazhong Univ Sci & Technol Sch Management Wuhan Peoples R China

Carbon price forecasting is important for policymakers and market participants. Due to the non -stationary and non -linearity of the carbon price, the commonly used methods adopt the ideology of 'decomposition and integration' to conduct multiscale forecasting. On this basis, multivariable forecasting discovers more informative knowledge with exogenous variables for carbon price forecasting, but it ignores that (i) the high -frequency and low -frequency components of the carbon price are mainly affected by different variables, and (ii) each variable contributes differently to each component forecasting. To address these challenges, we propose a multiscale and multivariable differentiated learning method for carbon price forecasting in this study. Specifically, different variables are introduced to forecast the high -frequency and low -frequency components, and a novel attentionweighted least squares support vector regression method is first proposed, in which the weight matrix of variables is constructed according to the idea of the attention mechanism. Furthermore, we analyze the contribution of each variable to the carbon price using Shapley additive explanations, thereby providing a reference for carbon market participants. We conduct experiments on the carbon price of the European Union Emissions Trading System and Hubei carbon market in China. Extensive results demonstrate that the proposed model achieves competitive and superior performance over the baseline and compared models.

关键词： Carbon price forecasting Variational mode decomposition particle swarm optimization algorithm Least squares support vector regression

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：