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2023 7th International Conference on Electronic Information Technology and Computer Engineering, EITCE 2023

作者： Xu, Xiao Peng, Qian School of Mechanical and Automotive Engineering Xiamen University of Technology Fujian Xiamen China

ISBN: (纸本)9798400708305

A PI (proportional integral) control parameter optimization method based on particle swarm optimization improved grey wolf algorithm is proposed to address the issues of insufficient parameter adjustment accuracy, slow response speed, and poor stability in traditional PI control for permanent magnet synchronous motors (PMSM). To improve the issue of the traditional grey wolf optimization algorithm easily falling into local optima and causing a decrease in accuracy, the position update strategy of the particle swarm algorithm is applied to the position update of the grey wolf algorithm, effectively enhancing the global optimization capacity of the grey wolf algorithm. By establishing a simulation model of the PMSM vector control system and conducting hardware-in-the-loop experimental tests, the motor system response of the improved grey wolf algorithm and traditional PI control is compared and analyzed. The results show that compared to optimizing PI control parameters using traditional methods, the improved grey wolf algorithm significantly enhances the response speed, anti-interference capability, and robustness of the PMSM control system. © 2023 ACM.

关键词： Improve the Grey Wolf algorithm particle swarm optimization algorithm Permanent magnet synchronous motor PI control paramet

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Power optimization of gas pipelines via an improved particle swarm optimization algorithm

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Petroleum Science 2012年第1期9卷 89-92页

作者： Zheng Zhiwei Wu Changchun Beijing Key Laboratory of Urban Oil and Gas Distribution Technology China University of PetroleumBeijing 102249China

In past decades dynamic programming, genetic algorithms, ant colony optimization algorithms and some gradient algorithms have been applied to power optimization of gas pipelines. In this paper a power optimization model for gas pipelines is developed and an improved particle swarm optimization algorithm is applied. Based on the testing of the parameters involved in the algorithm which need to be defined artificially, the values of these parameters have been recommended which can make the algorithm reach efficiently the approximate optimum solution with required accuracy. Some examples have shown that the relative error of the particle swarm optimization over ant colony optimization and dynamic programming is less than 1% and the computation time is much less than that of ant colony optimization and dynamic programming.

关键词： Gas pipeline operation optimization particle swarm optimization algorithm

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Two-dimensional variational mode decomposition for seismic record denoising

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JOURNAL OF GEOPHYSICS AND ENGINEERING 2022年第3期19卷 433-444页

作者： Zhang, Xingli Chen, Yan Jia, Ruisheng Lu, Xinming Shandong Univ Sci & Technol Coll Comp Sci & Engn Qianwan Port Rd Qingdao 266590 Peoples R China

Seismic signal denoising is the main task of seismic data processing. This study proposes a novel method for the denoising seismic record on the basis of a two-dimensional variational mode decomposition (2D-VMD) algorithm and permutation entropy (PE). 2D-VMD is a recently introduced adaptive signal decomposition method in which K and a are important decomposing parameters to determine the number of modes, and have a predictable effect on the nature of detected modes. We present a novel method to address the problems of selecting appropriate K and a values and apply these values to the proposed method. First, for a 2D seismic signal, the 2D-VMD method can decompose it into K modes with specific direction and vibration characteristics. Next, the PE value of each mode is calculated. Random noise components are eliminated according to the PE value. Finally, the signal components are reconstructed to acquire the denoised seismic signal. Experimental and simulation results indicate that the proposed method has remarkable denoising effect on synthetic and real seismic signals. We hope that this new method can inspire and help evaluate new ideas in this field.

关键词： seismic signal noise reduction two-dimensional variational mode decomposition particle swarm optimization algorithm permutation entropy

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PSO-Based System Identification on Cement Vertical Mill System Using Real-World Data

PSO-Based System Identification on Cement Vertical Mill Syst...

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2024 International Conference on Guidance, Navigation and Control

作者： Xu, Yanxin Xu, Yang Min, Cheng Zhu, Jing Nanjing Univ Aeronaut & Astronaut Coll Automat Engn Nanjing 211106 Peoples R China

ISBN: (纸本)9789819622504;9789819622481;9789819622474

Despite the rapid development of cement industry, the production control of cement vertical mills continues to heavily rely on traditional expert experience-based methods. In order to enhance production efficiency, this paper meticulously analyzes the dynamic structure and production processes inherent in cement vertical mill systems. It divides the process from the furnace to the mill outlet into three distinct subsystems and carefully selects mathematical process models to construct a comprehensive system model. Field operation data from the Tangshan Plant of Tianjin Cement Research Institute serve as the basis for identifying model parameters. The paper utilizes the particle swarm optimization algorithm to optimize and determine the most appropriate model parameters for the vertical mill system, ultimately achieving an impressive fitting degree of approximately 90% for the optimized model.

关键词： System Identification particle swarm optimization algorithm Data Preprocessing

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Test Case Generation for Modified Code using a Variant of particle swarm optimization (PSO) algorithm

Test Case Generation for Modified Code using a Variant of Pa...

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10th International Conference on Information Technology - New Generations (ITNG)

作者： Tiwari, Shailesh Mishra, K. K. Misra, A. K. MNNIT Allahabad CSED Allahabad Uttar Pradesh India

ISBN: (纸本)9780769549675

In this paper, a variant of particle swarm optimization (PSO) algorithm using modified time varying acceleration coefficients (PSO-TVAC) has been proposed and applied in creation of new test cases for modified code in regression testing. The performance of the proposed algorithm is compared with other existing PSO algorithms on five well known benchmark test functions. The experiments prove that the proposed algorithm has better performance. The test cases generated by the proposed PSO-TVAC algorithm have greater code coverage capability over the initial test cases.

关键词： regression testing particle swarm optimization algorithm test case generation

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Improved PSO-BPNN Multi-Parameter Identification Method and Its Application in Battery Thermal Network Model 2

Improved PSO-BPNN Multi-Parameter Identification Method and ...

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2nd IEEE International Power Electronics and Application Symposium, PEAS 2023

作者： Jiang, Tingzhi Ma, Mingyao Hu, Qiong Song, Qiwei Hefei University of Technology National and Local Joint Engineering Laboratory for Renewable Energy Access to Grid Technology Hefei China Sungrow Power Supply Co. Ltd Power Generation Sys Hefei China

ISBN: (纸本)9798350313765

Traditional battery thermal models either lack accuracy or have high computational costs, making it difficult for battery management systems to monitor battery temperature online. In this study, a three-dimensional thermal network model was developed specifically for a 120Ah square lithium-ion battery, significantly reducing the computational burden while ensuring relatively accurate outputs at multiple temperature points. Firstly, the heat generation rate of the entire battery during charging and discharging processes was obtained using an accelerated adiabatic calorimeter (ARC). Then, based on the positions of the electrode tabs and temperature measurement points, the battery was divided, and a three-dimensional thermal network model architecture was constructed. Special particle swarm algorithms and neural networks were used to fit the thermal parameters of the thermal network model. Finally, discharge temperature experiments were conducted to validate the feasibility of the thermal network model. © 2023 IEEE.

关键词： BP neural network particle swarm optimization algorithm Square lithium-ion battery Three-dimensional battery thermal network model

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Optimal scheduling of wind-photovoltaic power-generation system based on a copula-based conditional value-at-risk model

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Clean Energy 2022年第4期6卷 550-556页

作者： Xin Ju Xiaomin Liu Shangke Liu Yangli Xiao State Grid Ningxia Economic and Technological Research Institute YinchuanNingxiaChina

Increasing the application of renewable energy in the power system is an effective way to achieve the goal of‘Dual Carbon’.At the same time,the high proportion of renewable energy connected to the grid endangers the safe operation of the power *** solve this problem,this paper proposes the application of a copula function to describe the correlation between wind power and photovoltaic power,and reduce the uncertainty of power-system operation with a high proportion of renewable *** order to increase the robustness of the model,this paper proposes the application of the conditional value-at-risk theory to construct the objective function of the model and effectively control the tail risk of power-system operation *** case analysis,it is found that the model proposed in this paper has strong practicality and economy.

关键词： renewable energy ‘Dual Carbon’targets copula-CVaR particle swarm optimization algorithm optimized scheduling

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Nonlinear System Identification Using Adaptive Volterra Model Optimized with Sine Cosine algorithm

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ARABIAN JOURNAL FOR SCIENCE AND ENGINEERING 2022年第11期47卷 14411-14422页

作者： Singh, Sandeep Rawat, Tarun Kumar Ashok, Alaknanda Uttarakhand Tech Univ Dehra Dun Uttarakhand India Maharaja Surajmal Inst Technol New Delhi India Netaji Subhas Univ Technol New Delhi India GB Pant Univ Agr & Technol Pantnagar Uttarakhand India

This paper presents the handling of nonlinear system identification problem based on Volterra model. Gradient-based algorithms are generally applied to solve system identification problems. However, these algorithms have the limitation of getting trapped in the local minimum. In the presented work, a novel population-based optimization algorithm popularly known as sine cosine algorithm (SCA) is being utilized for the identification of nonlinear discrete-time system. The SCA uses mathematical sine and cosine functions for the purpose of optimization. SCA is responsible for the creation of multiple random solutions and moving them towards best solution while maintaining proper balance between the exploitation and exploration phases of optimization. The performance evaluation of the applied SCA is carried out in terms of coefficient evaluation, mean square error and convergence profile. Two different examples for nonlinear system are presented in this work so as to demonstrate the validity of the employed algorithm. Performance analysis of the proposed approach with the existing state-of-the-art algorithms proves that the SCA outperforms the other algorithms.

关键词： Nonlinear system Volterra model Sine cosine algorithm Gravitational search algorithm particle swarm optimization algorithm Cuckoo search algorithm

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Research on motion trajectory planning of the robotic arm of a robot

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ARTIFICIAL LIFE AND ROBOTICS 2022年第3期27卷 561-567页

作者： Miao, Xinghua Fu, Huansen Song, Xiangqian Taizhou Univ Sch Shipping & Mechatron Engn 93 Jichuan East Rd Taizhou 225300 Jiangsu Peoples R China

The emergence of robots has replaced repetitive manual labor, and good robotic arm route planning can effectively improve work efficiency. This paper briefly introduced the motion model and trajectory planning method of robotic arms. The motion trajectory of robot arms was optimized by the genetic algorithm-improved particle swarm optimization (PSO) algorithm, and simulation experiments were carried out. The results showed that the improved PSO algorithm converged faster and had the lowest fitness after stable convergence;the arm had continuous and smooth changes in angle, angular velocity and angular acceleration and consumed the shortest time while moving on the route planned by the improved particle swarm algorithm, and the improved PSO algorithm took the shortest time to compute the route.

关键词： Cross variation particle swarm optimization algorithm Robotic arm Trajectory planning

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Digital Twin System for Home Service Robot Based on Motion Simulation

Digital Twin System for Home Service Robot Based on Motion S...

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2023 IEEE Symposium Series on Computational Intelligence, SSCI 2023

作者： Jiang, Zhengsong Tian, Guohui Cui, Yongcheng Liu, Tiantian Gu, Yu Wang, Yifei School of Control Science and Engineering Shandong University Jinan China School of Information University of California Berkeley United States

ISBN: (纸本)9781665430654

In order to improve the task execution capability of home service robot, and to cope with the problem that purely physical robot platforms cannot sense the environment and make decisions online, a method for building digital twin system for home service robot based on motion simulation is proposed. A reliable mapping of the home service robot and its working environment from physical space to digital space is achieved in three dimensions: geometric, physical and functional. In this system, a digital space-oriented URDF file parser is designed and implemented for the automatic construction of the robot geometric model. Next, the physical model is constructed from the kinematic equations of the robot and an improved particle swarm optimization algorithm is proposed for the inverse kinematic solution. In addition, to adapt to the home environment, functional attributes are used to describe household objects, thus improving the semantic description of the digital space for the real home environment. Finally, through geometric model consistency verification, physical model validity verification and virtual-reality consistency verification, it shows that the digital twin system designed in this paper can construct the robot geometric model accurately and completely, complete the operation of household objects successfully, and the digital twin system is effective and practical. © 2023 IEEE.

关键词： Digital Twin Motion Simulation particle swarm optimization algorithm Service Robot

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