{"title":"Retracted: Building Construction Design Based on Particle Swarm Optimization Algorithm","authors":"Journal of Healthcare Engineering","doi":"10.1155/2023/9823945","DOIUrl":"https://doi.org/10.1155/2023/9823945","url":null,"abstract":"<jats:p />","PeriodicalId":46052,"journal":{"name":"Journal of Control Science and Engineering","volume":null,"pages":null},"PeriodicalIF":1.7,"publicationDate":"2023-08-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"72829794","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Retracted: Electrical Line Fault Detection and Line Cut-Off Equipment and Control","authors":"Journal of Healthcare Engineering","doi":"10.1155/2023/9892508","DOIUrl":"https://doi.org/10.1155/2023/9892508","url":null,"abstract":"<jats:p />","PeriodicalId":46052,"journal":{"name":"Journal of Control Science and Engineering","volume":null,"pages":null},"PeriodicalIF":1.7,"publicationDate":"2023-08-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83036214","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Retracted: Intelligent Analysis of Logistics Information Based on Dynamic Network Data","authors":"Journal of Control Science and Engineering","doi":"10.1155/2023/9820456","DOIUrl":"https://doi.org/10.1155/2023/9820456","url":null,"abstract":"","PeriodicalId":46052,"journal":{"name":"Journal of Control Science and Engineering","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-08-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136392151","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
With the increasing penetration of distributed generation (DG) in the distribution network, the original network structure of the distribution network has been changed. In addition, the randomness and intermittency of renewable power generation will also have an impact on the voltage and power flow of the distribution network. To solve this problem, this paper proposes a reactive power optimization control method for distribution network with DGs based on second-order oscillating particle swarm optimization (PSO) algorithm with a constriction factor. Considering the economic operation of the distribution network, the proposed control method realizes the coordinated operation of the DGs and battery group with the conventional static reactive power compensation device, so as to improve the voltage quality of the distribution network and reduce the system network loss. At the same time, an improved second-order oscillating PSO algorithm is proposed to improve the speed and convergence of the multiobjective algorithm. Finally, the effectiveness of the proposed control method is verified by using MATLAB/Simulink on IEEE 33 bus distribution network with DGs in both static and dynamic situations.
{"title":"Research on Reactive Power Optimization Control Method for Distribution Network with DGs Based on Improved Second-Order Oscillating PSO Algorithm","authors":"Youming Cai, Jingmin Liu, Ning Gao","doi":"10.1155/2023/5813277","DOIUrl":"https://doi.org/10.1155/2023/5813277","url":null,"abstract":"With the increasing penetration of distributed generation (DG) in the distribution network, the original network structure of the distribution network has been changed. In addition, the randomness and intermittency of renewable power generation will also have an impact on the voltage and power flow of the distribution network. To solve this problem, this paper proposes a reactive power optimization control method for distribution network with DGs based on second-order oscillating particle swarm optimization (PSO) algorithm with a constriction factor. Considering the economic operation of the distribution network, the proposed control method realizes the coordinated operation of the DGs and battery group with the conventional static reactive power compensation device, so as to improve the voltage quality of the distribution network and reduce the system network loss. At the same time, an improved second-order oscillating PSO algorithm is proposed to improve the speed and convergence of the multiobjective algorithm. Finally, the effectiveness of the proposed control method is verified by using MATLAB/Simulink on IEEE 33 bus distribution network with DGs in both static and dynamic situations.","PeriodicalId":46052,"journal":{"name":"Journal of Control Science and Engineering","volume":null,"pages":null},"PeriodicalIF":1.7,"publicationDate":"2023-07-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83009228","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
In order to solve the problem of low abnormal diagnosis rate of self-powered power supply system, an improved grey wolf optimization-support vector machine (GWO-SVM) algorithm combined with maximal information coefficient (MIC) are proposed. First, the feature sets of 11 kinds of monitoring data are optimized and selected based on MIC for self-powered power supply system. By eliminating redundant variables and insensitive variables, feature variable sets with great influence on abnormal diagnosis are selected. Second, by upgrading the selection method of control parameter σ from linear to nonlinear, an improved GWO-SVM algorithm that can take into account both global and local search capabilities is proposed. Furthermore, the optimal feature set which has great influence on abnormal diagnosis is selected as the input of the proposed algorithm, and then the abnormal diagnosis method combining the improved GWO-SVM with MIC is constructed for self-powered power supply system. The specific algorithm flow and step are given. Finally, compared with other algorithm, the simulation experiments show that the GWO-SVM method has a higher accuracy and a higher recall rate for the abnormal diagnosis in the self-powered power supply system.
{"title":"Abnormal Diagnosis Method of Self-Powered Power Supply System Based on Improved GWO-SVM","authors":"Ya jie Li, Shaochong Li, W. Li","doi":"10.1155/2023/1981056","DOIUrl":"https://doi.org/10.1155/2023/1981056","url":null,"abstract":"In order to solve the problem of low abnormal diagnosis rate of self-powered power supply system, an improved grey wolf optimization-support vector machine (GWO-SVM) algorithm combined with maximal information coefficient (MIC) are proposed. First, the feature sets of 11 kinds of monitoring data are optimized and selected based on MIC for self-powered power supply system. By eliminating redundant variables and insensitive variables, feature variable sets with great influence on abnormal diagnosis are selected. Second, by upgrading the selection method of control parameter \u0000 \u0000 σ\u0000 \u0000 from linear to nonlinear, an improved GWO-SVM algorithm that can take into account both global and local search capabilities is proposed. Furthermore, the optimal feature set which has great influence on abnormal diagnosis is selected as the input of the proposed algorithm, and then the abnormal diagnosis method combining the improved GWO-SVM with MIC is constructed for self-powered power supply system. The specific algorithm flow and step are given. Finally, compared with other algorithm, the simulation experiments show that the GWO-SVM method has a higher accuracy and a higher recall rate for the abnormal diagnosis in the self-powered power supply system.","PeriodicalId":46052,"journal":{"name":"Journal of Control Science and Engineering","volume":null,"pages":null},"PeriodicalIF":1.7,"publicationDate":"2023-06-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76354960","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-04-27DOI: 10.11648/j.cse.20230701.12
Jose Luiz Guarino, Jose Flavio Silveira Feiteira
{"title":"Historical Background of the Origin of the Speeddroop, Additional Control Signals and Analogic Retrofit of a Governor of a Francis Turbine, Using Operational Amplifier IC741","authors":"Jose Luiz Guarino, Jose Flavio Silveira Feiteira","doi":"10.11648/j.cse.20230701.12","DOIUrl":"https://doi.org/10.11648/j.cse.20230701.12","url":null,"abstract":"","PeriodicalId":46052,"journal":{"name":"Journal of Control Science and Engineering","volume":null,"pages":null},"PeriodicalIF":1.7,"publicationDate":"2023-04-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80864890","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The reliability and economy of dc-link capacitors are important concerns in multiphase drive systems. Due to the parallel connection of several converters, the dc-link capacitors are subjected to a higher RMS current, and the root mean square (RMS) current of dc-link capacitors is an important reference standard to determine its lifetime, cost, and volume. In this paper, the RMS current of dc-link capacitor is calculated by using the dual Fourier integral method and the effect of carrier interleave is studied. Meanwhile, the modulation ratio, harmonic sidebands, and switching frequency are also considered. In order to optimize the reliability and economy of the multiphase drive system, a Cotes method combined with carrier-phase shifting technology (CPST) for calculating RMS current of the dc-link capacitor is proposed. The proposed method can provide optimization guidance for the design of dc-link capacitors. Finally, the analytical and experiment results are compared with the existing methods, and the experimental results verify the effectiveness of the proposed method.
{"title":"Calculation of RMS Current Load on DC-Link Capacitors for Multiphase Machine Drives under Carrier-Phase Shift Control","authors":"Zhigang Zhang, Pengcheng Zhang, Yang Zhang, Wenjuan Zhang, Mengdi Li, Zichen Xiong","doi":"10.1155/2023/6909403","DOIUrl":"https://doi.org/10.1155/2023/6909403","url":null,"abstract":"The reliability and economy of dc-link capacitors are important concerns in multiphase drive systems. Due to the parallel connection of several converters, the dc-link capacitors are subjected to a higher RMS current, and the root mean square (RMS) current of dc-link capacitors is an important reference standard to determine its lifetime, cost, and volume. In this paper, the RMS current of dc-link capacitor is calculated by using the dual Fourier integral method and the effect of carrier interleave is studied. Meanwhile, the modulation ratio, harmonic sidebands, and switching frequency are also considered. In order to optimize the reliability and economy of the multiphase drive system, a Cotes method combined with carrier-phase shifting technology (CPST) for calculating RMS current of the dc-link capacitor is proposed. The proposed method can provide optimization guidance for the design of dc-link capacitors. Finally, the analytical and experiment results are compared with the existing methods, and the experimental results verify the effectiveness of the proposed method.","PeriodicalId":46052,"journal":{"name":"Journal of Control Science and Engineering","volume":null,"pages":null},"PeriodicalIF":1.7,"publicationDate":"2023-04-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81710163","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
In order to provide accurate image information for the analysis and treatment of dangerous rocks and rockfalls during the early investigation, a UAV tilt photography control method for numerical simulation of high and steep rock slopes is proposed. Based on the UAV tilting photography technology, the slope section was obtained through a real 3D modeling and poststage point cloud data processing. Numerical simulation is used to study the motion characteristics of dangerous rock falling in a high and steep slope of a railway station. This essay introduces the application of a UAV tilting photography and real 3D modeling technology in the process of rock fall analysis and realizes the real scene restoration of the site. The point cloud data of the site is obtained, and the processing process of the point cloud is introduced in detail. The slope section of the site was obtained based on the point cloud, and RocFall software was used to obtain the motion characteristics of dangerous rock falling (falling trajectory, bouncing height impact energy, and impact velocity). The simulation results show that because of the rugged slope, the falling rocks collide and rebound on the slope for many times. In addition, near the bottom of the slope, there is a steep cliff with a height of 136.21 m, which is approximately 54° from the horizontal line, causing the falling rock to bounce and eventually fall at a higher height. It moves to the bottom of the slope and bounces off the level of the railway line before finally settling on the railway road. The maximum bounce height of falling rock in the process of slope rolling motion reaches 30 m. When falling rock moves near the railway line (coordinate is on the right side of zero), the bounce height is 15∼25 m, which threatens the safety of the railway operation. Conclusion. The UAV tilt photography technology can be well applied to the analysis of rockfall motion characteristics of dangerous rocks, and provide an accurate cross-section data information for the study of rockfall motion characteristics of dangerous rocks.
{"title":"UAV Tilt Photography Control for Numerical Simulation of High and Steep Rock Slopes","authors":"Yani Wang, Yinpeng Zhou, Bo Wang","doi":"10.1155/2023/7489283","DOIUrl":"https://doi.org/10.1155/2023/7489283","url":null,"abstract":"In order to provide accurate image information for the analysis and treatment of dangerous rocks and rockfalls during the early investigation, a UAV tilt photography control method for numerical simulation of high and steep rock slopes is proposed. Based on the UAV tilting photography technology, the slope section was obtained through a real 3D modeling and poststage point cloud data processing. Numerical simulation is used to study the motion characteristics of dangerous rock falling in a high and steep slope of a railway station. This essay introduces the application of a UAV tilting photography and real 3D modeling technology in the process of rock fall analysis and realizes the real scene restoration of the site. The point cloud data of the site is obtained, and the processing process of the point cloud is introduced in detail. The slope section of the site was obtained based on the point cloud, and RocFall software was used to obtain the motion characteristics of dangerous rock falling (falling trajectory, bouncing height impact energy, and impact velocity). The simulation results show that because of the rugged slope, the falling rocks collide and rebound on the slope for many times. In addition, near the bottom of the slope, there is a steep cliff with a height of 136.21 m, which is approximately 54° from the horizontal line, causing the falling rock to bounce and eventually fall at a higher height. It moves to the bottom of the slope and bounces off the level of the railway line before finally settling on the railway road. The maximum bounce height of falling rock in the process of slope rolling motion reaches 30 m. When falling rock moves near the railway line (coordinate is on the right side of zero), the bounce height is 15∼25 m, which threatens the safety of the railway operation. Conclusion. The UAV tilt photography technology can be well applied to the analysis of rockfall motion characteristics of dangerous rocks, and provide an accurate cross-section data information for the study of rockfall motion characteristics of dangerous rocks.","PeriodicalId":46052,"journal":{"name":"Journal of Control Science and Engineering","volume":null,"pages":null},"PeriodicalIF":1.7,"publicationDate":"2023-04-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84456041","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
In order to improve the reliability of robot electrical fault detection and diagnosis, the author proposes a robot electrical fault detection and diagnosis method based on deep learning. Taking the return power and active power as constraints, the electrical fault data collection of the robot is carried out. Taking the resonant inductance and resonant capacitance of the robot electrical equipment as identification parameters, we conduct electrical fault differential feature mining. The fault features are extracted according to the time-delay distribution sequence of the electrical fault data of the robot, and the electrical fault detection and diagnosis results are output by using the deep learning function. Simulation results show that the author's method has a high accuracy probability for robot electrical fault diagnosis. The author's method is on average 14.7% higher than the neural network-based method and 24.5% higher than the expert system-based method. The accuracy rate of the author's method for robot electrical fault diagnosis is high. The author’s method is 16.6% higher than the neural network-based method on average and 34.2% higher than the expert system-based method. It is proved that the robot electrical fault detection and diagnosis based on deep learning has high accuracy and short time.
{"title":"Robot Fault Detection Based on Big Data","authors":"Fei Luo","doi":"10.1155/2023/8375382","DOIUrl":"https://doi.org/10.1155/2023/8375382","url":null,"abstract":"In order to improve the reliability of robot electrical fault detection and diagnosis, the author proposes a robot electrical fault detection and diagnosis method based on deep learning. Taking the return power and active power as constraints, the electrical fault data collection of the robot is carried out. Taking the resonant inductance and resonant capacitance of the robot electrical equipment as identification parameters, we conduct electrical fault differential feature mining. The fault features are extracted according to the time-delay distribution sequence of the electrical fault data of the robot, and the electrical fault detection and diagnosis results are output by using the deep learning function. Simulation results show that the author's method has a high accuracy probability for robot electrical fault diagnosis. The author's method is on average 14.7% higher than the neural network-based method and 24.5% higher than the expert system-based method. The accuracy rate of the author's method for robot electrical fault diagnosis is high. The author’s method is 16.6% higher than the neural network-based method on average and 34.2% higher than the expert system-based method. It is proved that the robot electrical fault detection and diagnosis based on deep learning has high accuracy and short time.","PeriodicalId":46052,"journal":{"name":"Journal of Control Science and Engineering","volume":null,"pages":null},"PeriodicalIF":1.7,"publicationDate":"2023-04-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89426945","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
In order to solve the problems of unstable motion and large trajectory tracking error of the manipulator when it is disturbed by the outside world, the author proposes an adaptive neural network manipulator motion trajectory error method. The author gives the dynamic equation of the manipulator and uses the positive feedback neural network to study the dynamic characteristics of the manipulator. An adaptive neural network control system is designed, and the stability and convergence of the closed-loop system are proved by the Lyapunov function. A schematic diagram of the manipulator model is established, and MATLAB/Simulink software is used to simulate the dynamic parameters of the manipulator. At the same time, it is compared and analyzed with the simulation results of the PID control system. Simulation results show that in robot arm 3, the expected motion trajectory is θ3 = 0.4cos(2πt), the initial condition θ(0) = [000]τ, the control parameter K = diag(40,40),40), the disturbance parameter τ’ = 20cos(πt), robot arm link parameters l1 = 0.62 m, l2 = 0.41 m, l3 = 0.34 m, m1 = 3.5, m2 = 2.5 kg, m3 = 2 kg, g = 9.82 m/s2, under t = 2s, the motion trajectory of the robotic arm is disturbed by the outside world, and the adaptive neural network is used to control the motion trajectory with a small tracking error, input torque ripple is small. Conclusion. The manipulator adopts the adaptive neural network control method, which can improve the control accuracy of the motion trajectory and weaken the jitter phenomenon of the manipulator motion.
为了解决机械手受外界干扰时运动不稳定和轨迹跟踪误差大的问题，提出了一种自适应神经网络机械手运动轨迹误差方法。给出了机械手的动力学方程，并利用正反馈神经网络对机械手的动力学特性进行了研究。设计了自适应神经网络控制系统，并用李雅普诺夫函数证明了闭环系统的稳定性和收敛性。建立了机械手模型的原理图，利用MATLAB/Simulink软件对机械手的动态参数进行了仿真。同时，与PID控制系统的仿真结果进行了对比分析。仿真结果表明,在机械臂3中,预期的运动轨迹是θ3 = 0.4 cos(2πt),初始条件θ(0)=[000]τ,控制参数K =诊断接头(40、40),40),扰动参数τ= 20 cos(πt),机器人手臂链接参数l1 = 0.62 m, l2 = 0.41 m, l3 = 0.34 m, m1 = 3.5平方米= 2.5公斤,m3 = 2公斤,g = 9.82米/ s2,在t = 2 s,机械臂的运动轨迹由外界干扰,采用自适应神经网络控制运动轨迹，跟踪误差小，输入转矩脉动小。结论。该机械手采用自适应神经网络控制方法，提高了运动轨迹的控制精度，减弱了机械手运动的抖动现象。
{"title":"Motion Trajectory Error of Robotic Arm Based on Neural Network Algorithm","authors":"B. Xu, Chen Sem-Lin","doi":"10.1155/2023/3958434","DOIUrl":"https://doi.org/10.1155/2023/3958434","url":null,"abstract":"In order to solve the problems of unstable motion and large trajectory tracking error of the manipulator when it is disturbed by the outside world, the author proposes an adaptive neural network manipulator motion trajectory error method. The author gives the dynamic equation of the manipulator and uses the positive feedback neural network to study the dynamic characteristics of the manipulator. An adaptive neural network control system is designed, and the stability and convergence of the closed-loop system are proved by the Lyapunov function. A schematic diagram of the manipulator model is established, and MATLAB/Simulink software is used to simulate the dynamic parameters of the manipulator. At the same time, it is compared and analyzed with the simulation results of the PID control system. Simulation results show that in robot arm 3, the expected motion trajectory is θ3 = 0.4cos(2πt), the initial condition θ(0) = [000]τ, the control parameter K = diag(40,40),40), the disturbance parameter τ’ = 20cos(πt), robot arm link parameters l1 = 0.62 m, l2 = 0.41 m, l3 = 0.34 m, m1 = 3.5, m2 = 2.5 kg, m3 = 2 kg, g = 9.82 m/s2, under t = 2s, the motion trajectory of the robotic arm is disturbed by the outside world, and the adaptive neural network is used to control the motion trajectory with a small tracking error, input torque ripple is small. Conclusion. The manipulator adopts the adaptive neural network control method, which can improve the control accuracy of the motion trajectory and weaken the jitter phenomenon of the manipulator motion.","PeriodicalId":46052,"journal":{"name":"Journal of Control Science and Engineering","volume":null,"pages":null},"PeriodicalIF":1.7,"publicationDate":"2023-04-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83001158","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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