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Increment PID controller based on Immunity particle swarm optimization algorithm

IMACS Multiconference on Computational Engineering in Systems Applications (CESA 2006)

作者： WeiZhng KunWang Shouzhi-Li Xian Univ Sch Arts & Sci Elect Power & Electron Device Xian Shanxi Province Peoples R China

ISBN: (纸本)9787302139225

Based on the astringency and practicability of particle swarm optimization algorithm (PSO) and T cell's promotions and B cell's restrainability of Immunity particle swarm optimization algorithm(IMPSO) and applied it to PID controllers. It is clear that IMPSO is suitable to Increment PID control according to the simulations and it made the tracking and anti-jamming of IM PID based on IMPSO, IMPSO more effective than those of PID based on PSO and those of IMPID based on Immunity algorithm.

关键词： particle swarm optimization algorithm increment PED control immunity algorithm

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optimization of Square-shaped Bolted Joints Based on Improved particle swarm optimization algorithm

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EKSPLOATACJA I NIEZAWODNOSC-MAINTENANCE AND RELIABILITY 2023年第3期25卷

作者： Chen, Kui Yang, Cheng Zhao, Yongsheng Niu, Peng Niu, NaNa Wu, Hongchao Beijing Univ Technol Fac Mat & Mfg Beijing Peoples R China Beijing Inst Aerosp Control Devices Beijing Peoples R China XCMG Construction Machinery Xuzhou Peoples R China

The bolted joint is widely used in heavy-duty CNC machine tools, which has huge influence on working precision and overall stiffness of CNC machine. The process parameters of group bolt assembly directly affect the stiffness of the connected parts. The dynamic model of bolted joints is established based on the fractal theory, and the overall stiffness of joint surface is calculated. In order to improve the total stiffness of bolted assembly, an improved particle swarm optimization algorithm with combination of time-varying weights and contraction factor is proposed. The input parameters are preloading of bolts, fractal dimension, roughness, and object thickness. The main goal is to maximize the global rigidity. The optimization results show that improved algorithm has better convergence, faster calculation speed, preferable results, and higher optimization performance than standard particle swarm optimization algorithm. Moreover, the global rigidity optimization is achieved.

关键词： particle swarm optimization algorithm bolt connection bolted joint fractal theory

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Residual Vibration Suppression of Piezoelectric Inkjet Printing Based on particle swarm optimization algorithm

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MICROMACHINES 2024年第10期15卷 1192-1192页

作者： Zhu, Huixuan Li, Song Zhu, Runyang Gao, Feiyang Yin, Zhenyu Liu, Lianqing Zheng, Xiongfei Chinese Acad Sci Shenyang Inst Automat State Key Lab Robot Shenyang 110016 Peoples R China Univ Chinese Acad Sci Beijing 100049 Peoples R China Chinese Acad Sci Shenyang Inst Comp Technol Shenyang 110168 Peoples R China Liaoning Key Lab Domest Ind Control Platform Techn Shenyang 110168 Peoples R China

Piezoelectric inkjet printing technology, known for its high precision and cost-effectiveness, has found extensive applications in various fields. However, the issue of residual vibration significantly limits its printing quality and efficiency. This paper presents a method for suppressing residual vibration based on the particle swarm optimization (PSO) algorithm. Initially, an improved PI model considering the nonlinear hysteresis characteristics of piezoelectric ceramics is established, and the model is identified through a strain gauge circuit to ensure its accuracy in describing the nonlinear hysteresis characteristics. Subsequently, a dynamic model of the piezoelectric inkjet printing system is constructed, with precise parameter identification achieved using the self-induction principle. This enables precise simulation of residual vibration. Finally, the driving waveform is optimized based on the PSO algorithm, with iterative calculations employed to find the optimal combination of driving waveform parameters, effectively suppressing residual vibration while ensuring sufficient injection energy. The results indicate that this method significantly reduces the amplitude of residual vibration, thereby effectively enhancing printing quality and stability. This research offers a novel solution for residual vibration suppression in piezoelectric inkjet printing technology, potentially advancing its applications in printing and biofabrication.

关键词： piezoelectric inkjet printing residual vibration suppression nonlinear hysteresis characteristics particle swarm optimization algorithm self-induction principle

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Development of an expression for the output voltage ripple of the DC-DC boost converter circuits by using particle swarm optimization algorithm

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MEASUREMENT 2020年 158卷 107694-000页

作者： Sabanci, Kadir Balci, Selami Karamanoglu Mehmetbey Univ Dept Elect & Elect Engn Fac Engn TR-70200 Karaman Turkey

In this study, a mathematical expression is derived to calculate the ripple value of the output voltage of 2-phase interleaved cascaded boost converter (ICBC) circuits. In this context, the total number of 432 data of ICBC circuit including four parameters (switching frequency, duty ratio, coupling coefficient and the output voltage ripple) are acquired using Ansys-Electronics software. The expression of the output voltage ripple enclosing ICBC circuit parameters related to optimization variables is developed. Afterwards, the coefficients of the expression are acquired by using an algorithm called particle swarm optimization (PSO). While the mean absolute error (MAE) of 420 data is obtained as 1.5055, the MAE of 12 test data not used in the optimization process is found to be 1.4608. These results show that, the output voltage ripple of ICBC can be easily calculated closing their actual values with the proposed basic mathematical expression instead of long-term and complex simulations. In addition, the accuracy of the developed mathematical expression is verified with the co-simulation of Maxwell 3D and Twin Builder interfaces within the Ansys-Electronics software. (C) 2020 Elsevier Ltd. All rights reserved.

关键词： Coupled inductors Boost converter Parametric FEA simulation Output voltage ripple particle swarm optimization algorithm

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Convergence and Performance Analysis of a particle swarm optimization algorithm for Optical Tuning of Gold Nanohole Arrays

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MATERIALS 2024年第4期17卷 807页

作者： Angelini, Margherita Zagaglia, Luca Marabelli, Franco Floris, Francesco Univ Pavia Dept Phys Via Bassi 6 I-27100 Pavia Italy

Gold nanohole arrays, hybrid metal/dielectric metasurfaces composed of periodically arranged air holes in a thick gold film, exhibit versatile support for both localized and propagating surface plasmons. Leveraging their capabilities, particularly in surface plasmon resonance-oriented applications, demands precise optical tuning. In this study, a customized particle swarm optimization algorithm, implemented in Ansys Lumerical FDTD, was employed to optically tune gold nanohole arrays treated as bidimensional gratings following the Bragg condition. Both square and triangular array dispositions were considered. Convergence and evolution of the particle swarm optimization algorithm were studied, and a mathematical model was developed to interpret its outcomes.

关键词： FDTD simulation particle swarm optimization algorithm gold nanohole arrays

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Image reconstruction for an electrical capacitance tomography system based on a least-squares support vector machine and a self-adaptive particle swarm optimization algorithm

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MEASUREMENT SCIENCE AND TECHNOLOGY 2011年第10期22卷

作者： Chen, Xia Hu, Hong-li Liu, Fei Gao, Xiang Xiang Xi An Jiao Tong Univ State Key Lab Power Equipment & Elect Insulat Xian 710049 Peoples R China

The task of image reconstruction for an electrical capacitance tomography (ECT) system is to determine the permittivity distribution and hence the phase distribution in a pipeline by measuring the electrical capacitances between sets of electrodes placed around its periphery. In view of the nonlinear relationship between the permittivity distribution and capacitances and the limited number of independent capacitance measurements, image reconstruction for ECT is a nonlinear and ill-posed inverse problem. To solve this problem, a new image reconstruction method for ECT based on a least-squares support vector machine (LS-SVM) combined with a self-adaptive particle swarm optimization (PSO) algorithm is presented. Regarded as a special small sample theory, the SVM avoids the issues appearing in artificial neural network methods such as difficult determination of a network structure, over-learning and under-learning. However, the SVM performs differently with different parameters. As a relatively new population-based evolutionary optimization technique, PSO is adopted to realize parameters' effective selection with the advantages of global optimization and rapid convergence. This paper builds up a 12-electrode ECT system and a pneumatic conveying platform to verify this image reconstruction algorithm. Experimental results indicate that the algorithm has good generalization ability and high-image reconstruction quality.

关键词： ECT image reconstruction support vector machine particle swarm optimization algorithm

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A Modified particle swarm optimization algorithm for the Design of a Double-Pad Aerostatic Bearing With a Pocketed Orifice-Type Restrictor

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JOURNAL OF TRIBOLOGY-TRANSACTIONS OF THE ASME 2014年第2期136卷 021701-021701页

作者： Chang, S. H. Jeng, Y. R. Natl Chung Cheng Univ Dept Mech Engn Adv Inst Mfg High Tech Innovat Chiayi 62102 Taiwan

The performance of an aerostatic bearing with a pocketed orifice-type restrictor is affected by the bearing size, pocket size, orifice design, supply pressure, and bearing load. This study proposes a modified particle swarm optimization (MPSO) algorithm to optimize a double-pad aerostatic bearing. In bearing optimization, the upper and lower bearing designs are independent and several design variables that affect bearing performance must be considered. This study also applies the concept of mutation from a genetic algorithm. The results show that the MPSO algorithm has a global search capability and high efficiency to optimize a problem with several design variables and that the mutation can provide an avenue for particles to escape from a local optimal value.

关键词： aerostatic bearing particle swarm optimization algorithm orifice-type restrictor

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Multi-Objective and Parallel particle swarm optimization algorithm for Container-Based Microservice Scheduling

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SENSORS 2021年第18期21卷 6212-6212页

作者： Chen, Xinying Xiao, Siyi Dalian Jiaotong Univ Sch Software Dalian 116000 Peoples R China

An application based on a microservice architecture with a set of independent, fine-grained modular services is desirable, due to its low management cost, simple deployment, and high portability. This type of container technology has been widely used in cloud computing. Several methods have been applied to container-based microservice scheduling, but they come with significant disadvantages, such as high network transmission overhead, ineffective load balancing, and low service reliability. In order to overcome these disadvantages, in this study, we present a multi-objective optimization problem for container-based microservice scheduling. Our approach is based on the particle swarm optimization algorithm, combined parallel computing, and Pareto-optimal theory. The particle swarm optimization algorithm has fast convergence speed, fewer parameters, and many other advantages. First, we detail the various resources of the physical nodes, cluster, local load balancing, failure rate, and other aspects. Then, we discuss our improvement with respect to the relevant parameters. Second, we create a multi-objective optimization model and use a multi-objective optimization parallel particle swarm optimization algorithm for container-based microservice scheduling (MOPPSO-CMS). This algorithm is based on user needs and can effectively balance the performance of the cluster. After comparative experiments, we found that the algorithm can achieve good results, in terms of load balancing, network transmission overhead, and optimization speed.

关键词： multi-objective optimization container-based microservice scheduling particle swarm optimization algorithm cloud computing

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Kinematic Parameter Identification for a Parallel Robot with an Improved particle swarm optimization algorithm

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APPLIED SCIENCES-BASEL 2024年第15期14卷 6557页

作者： Yu, Dayong Univ Shanghai Sci & Technol Sch Mech Engn Shanghai 200093 Peoples R China

The spacecraft docking motion simulation system for on-orbit docking plays a very important role in some theoretical research and engineering application fields. The parallel robot utilized in the spacecraft docking simulation system requires high positioning and orientation accuracy to achieve better simulation results. A novel kinematic parameter identification method with an improved particle swarm optimization (PSO) algorithm is proposed to enhance positioning and orientation accuracy of the parallel robot. A fitness function is established using these residuals between the measured and computed poses by a coordinate measuring machine and forward kinematics. The kinematic parameter identification problem is turned into a high-dimensional nonlinear optimization in which the unknown kinematic parameter errors are regarded as optimal variables. The optimal variables are solved by the proposed improved PSO algorithm. The mean values of the positioning and orientation errors are reduced from 4.3268 mm and 0.2221 deg to 0.7692 mm and 0.0674 deg, respectively. The proposed kinematic parameter identification method increases the positioning accuracy mean by 22.26% and the orientation accuracy mean by 32.80% compared with the least squares method. The kinematic parameter identification method with the improved PSO algorithm can effectively enhance positioning and orientation accuracy of the parallel robot for docking motion simulation.

关键词： parallel robot parameter identification particle swarm optimization algorithm pose accuracy nonlinear least squares

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Quantitative study of the defects of ferromagnetic materials using the magnetic charge model and particle swarm optimization algorithm

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JOURNAL OF MAGNETISM AND MAGNETIC MATERIALS 2022年第Part1期564卷

作者： Su, Sanqing Li, Junting Wang, Wei Liu, Xinwei Zuo, Fuliang Deng, Ruize Xian Univ Architecture & Technol Sch Civil Engn Xian 710055 Shaanxi Peoples R China

Local defects in components are an important factor responsible for causing damage to steel structures. Hence, metal magnetic memory (MMM) has been used to investigate this issue in recent years. MMM can detect defects based on variations in the self-magnetic leakage of ferromagnetic materials. However, there have been problems of difficulty in quantifying the theoretical parameters of the magnetic charge model and the single character-ization of magnetic feature parameters, when using MMM for quantitative analysis of defects. Therefore, the particle swarm optimization (PSO) algorithm was introduced to quicky and accurately quantify the parameters of the magnetic charge model. Theoretical values were compared with experimental data to verify the accuracy of the proposed method. Then, the defect information (defect width and defect depth) was changed and the variation patterns of the magnetic signal and characteristic parameters were analyzed. Finally, the weight of each characteristic parameter was calculated using the entropy value method. A theoretical formula to comprehen-sively describe defects using multi-feature parameters was proposed. The results show that the theoretical values calculated based on the parameter identification of the PSO algorithm well agreed with the experimental data. Moreover, the proposed formula well described the relationship between the defect width and characteristic parameters. This study is expected to provide a basis for improving the quantitative analysis of MMM.

关键词： Metal magnetic memory Magnetic charge model particle swarm optimization algorithm Parameter identification Local defect Characteristic parameter

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