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Kinematic parameters calibration of industrial robot based on RWS-PSO algorithm

PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART C-JOURNAL OF MECHANICAL ENGINEERING SCIENCE 2023年第14期237卷 3210-3220页

作者： Li, Hang Hu, Xiaobing Zhang, Xuejian Wei, Shangyun Luo, Qingyi Sichuan Univ Sch Mech Engn Chengdu Peoples R China Sichuan Univ Yibin R&D Pk Yibin Peoples R China Sichuan Univ Sch Mech Engn 24South Sect 1First Ring Rd Chengdu 610065 Peoples R China

The positioning accuracy of an industrial robot has a significant impact on its application in precision manufacturing, and it is necessary to calibrate robot kinematic parameters. Previous studies have established numerous nonlinear equations to solve the kinematic parameters, which are complicated and time consuming. A standard particle swarm optimization (PSO) algorithm is limited by long running time and low solution efficiency. Therefore, in this study, a dynamic particle swarm optimization algorithm based on roulette wheel selection (RWS-PSO) is proposed to realize the kinematic parameters calibration. First, a kinematics model is constructed using the standard Denavit-Hartenberg (D-H) method, and the theoretical and actual values of the spatial position of the robot endpoint are obtained via forward kinematics and a Laser Tracker, respectively. Next, the kinematic parameters calibration problem is transformed into a solution of a high-dimensional nonlinear equation using the proposed RWS-PSO algorithm. In the proposed RWS-PSO algorithm, the inertia factor is considered as linearly decreasing and the number of particles is selected by the roulette wheel selection (RWS) to improve its computational efficiency. The proposed RWS-PSO and standard PSO algorithms are compared based on various indices by simulation. The results reveal that the time cost of the RWS-PSO algorithm is much lower than that of the standard PSO algorithm on the basis of high precision and a reduced running time of approximately 53%. Finally, the kinematic parameter errors obtained by the two algorithms are compensated. According to the experimental results, the positioning accuracy of the robot in three (x-, y-, and z-) directions is improved by 78%, 46%, and 67%, respectively, compared to that of before compensation, which proves that the RWS-PSO algorithm is effective and practical for kinematic parameters calibration.

关键词： Industrial robot positioning accuracy kinematic parameters calibration intelligent optimization algorithm laser tracker

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Stochastic Computing and intelligent optimization Analysis for a Generalized Information Delivery System

Stochastic Computing and Intelligent Optimization Analysis f...

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2019 International Conference on Management, Education Technology and Economics(ICMETE 2019)

作者： Xiao Xiaonan Xiamen University Tan Kah Kee College

In this paper, we use the optimal nonlinear random filtering method and intelligent optimization algorithm to study the optimal control problem of a kind of incomplete data and continuous nonstationary stochastic information delivery system. We obtain the two optimal control mathematical models in these two situations;illustrate how to establish the optimal encoding and decoding of the nonstationary stochastic process;and provide an effective and reliable approach for the optimal control of such a process.

关键词： Generalized information Delivery system Stochastic analysis intelligent optimization algorithm Optimal control

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A Novel Hybrid Model Based on TVIW-PSO-GSA algorithm and Support Vector Machine for Classification Problems

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IEEE ACCESS 2019年 7卷 27789-27801页

作者： Xue, Hongxin Bai, Yanping Hu, Hongping Xu, Ting Liang, Haijian North Univ China Sch Informat & Commun Engn Taiyuan 030051 Shanxi Peoples R China North Univ China Sch Sci Dept Math Taiyuan 030051 Shanxi Peoples R China North Univ China Sch Informat & Commun Engn Natl Key Lab Elect Measurement Technol Key Lab Instrumentat Sci & Dynam MeasurementMini Taiyuan 030051 Shanxi Peoples R China

The increasingly serious haze problem in China has brought about a growing public awareness of air quality. Precise air quality index (AQI) forecasts play an important role in both controlling air pollution and promoting the sustainable development of human society. However, the randomness, non-stationarity, and irregularity of the AQI series make its classifications very difficult. This paper introduces a time-varying inertia weighting (TVIW) strategy based on a combination of gravitation search algorithm (GSA) and particle swarm optimization (PSO) called the TVIW-PSO-GSA. The TVIW-PSO-GSA is utilized to optimize the penalty parameter C and kernel function parameter gamma of a support vector machine (SVM) to create a hybrid TVIW-PSO-GSA-SVM algorithm. Twenty-three benchmark functions, five UCI datasets, and an AQI hierarchical classification example are tested to find that the convergence speed and performance of the TVI-PSO-GSA exceed those of other algorithms, and the TVIW-PSO-GSA-SVM algorithm also achieves higher classification accuracy and efficiency than the PSO-GSA-SVM, GSA-SVM, GA-SVM, or PSO-SVM, which indicates that the TVIW-PSO-GSA-SVM reliably and accurately classifies AQI and UCI datasets.

关键词： intelligent optimization algorithm GSA PSO time-varying inertia weighting strategy SVM

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A Spectral Convolutional Neural Network Model Based on Adaptive Fick’s Law for Hyperspectral Image Classification

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Computers, Materials & Continua 2024年第4期79卷 19-46页

作者： Tsu-Yang Wu Haonan Li Saru Kumari Chien-Ming Chen School of Artificial Intelligence(School of Future Technology) Nanjing University of Information Science&TechnologyNanjing210044China College of Computer Science and Engineering Shandong University of Science and TechnologyQingdao266590China Department of Mathematics Chaudhary Charan Singh UniversityMeerutUttar Pradesh250004India

Hyperspectral image classification stands as a pivotal task within the field of remote sensing,yet achieving highprecision classification remains a significant *** response to this challenge,a Spectral Convolutional Neural Network model based on Adaptive Fick’s Law algorithm(AFLA-SCNN)is *** Adaptive Fick’s Law algorithm(AFLA)constitutes a novel metaheuristic algorithm introduced herein,encompassing three new strategies:Adaptive weight factor,Gaussian mutation,and probability update *** adaptive weight factor,the algorithmcan adjust theweights according to the change in the number of iterations to improve the performance of the *** helps the algorithm avoid falling into local optimal solutions and improves the searchability of the *** probability update strategy helps to improve the exploitability and adaptability of the *** the AFLA-SCNN model,AFLA is employed to optimize two hyperparameters in the SCNN model,namely,“numEpochs”and“miniBatchSize”,to attain their optimal ***’s performance is initially validated across 28 functions in 10D,30D,and 50D for CEC2013 and 29 functions in 10D,30D,and 50D for *** results indicate AFLA’s marked performance superiority over nine other prominent optimization ***,the AFLA-SCNN model was compared with the Spectral Convolutional Neural Network model based on Fick’s Law algorithm(FLA-SCNN),Spectral Convolutional Neural Network model based on Harris Hawks optimization(HHO-SCNN),Spectral Convolutional Neural Network model based onDifferential Evolution(DE-SCNN),SpectralConvolutionalNeuralNetwork(SCNN)model,and SupportVector Machines(SVM)model using the Indian Pines dataset and PaviaUniversity *** experimental results show that the AFLA-SCNN model outperforms other models in terms of Accuracy,Precision,Recall,and F1-score on Indian Pines and Pavia *** them,the Accuracy of the AFLA-SCNN model on India

关键词： Adaptive Fick’s law algorithm spectral convolutional neural network metaheuristic algorithm intelligent optimization algorithm hyperspectral image classification

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Levy Flight Shuffle Frog Leaping algorithm Based on Differential Perturbation and Quasi-Newton Search

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IEEE ACCESS 2019年 7卷 116078-116093页

作者： Zhang, Xinming Fu, Zihao Chen, Haiyan Mao, Wentao Liu, Shangwang Liu, Guoqi Henan Normal Univ Coll Comp & Informat Engn Xinxiang 453007 Henan Peoples R China Hubei Canc Hosp Dept Gynecol Tumor Wuhan 430079 Hubei Peoples R China

Levy flight Shuffle Frog Leaping algorithm (LSFLA) is a SFLA variant and enhances the performance of SFLA largely, however, it still has some defects, such as poor convergence and low efficiency. So an improved LSFLA, namely, LSFLA based on Differential perturbation and Quasi-Newton search (DQLSFLA), is proposed in this paper. Firstly, the way of updating only one solution which is the worst one at every sub-iteration in LSFLA is replaced with an all-solution updating way in the subgroup to improve the probability of obtaining the best solution, to omit one sorting step at every sub-iteration and the subiteration number parameter setting, to reduce the computational complexity and to enhance the optimization efficiency. Secondly, a random differential perturbation approach and an improved Levy flight updating one are created and used in the subgroup updating to form DLSFLA, that is, the first half frogs in the subgroup use the random differential perturbation updating approach to improve the global search ability, and the last half adopt the improved Levy flight updating method to keep the advantage of the Levy flight updating method and overcome its defects. Finally, quasi-Newton local search is integrated into DLSFLA near the end of the algorithm to improve the convergence speed. Experimental results on the complex functions from CEC-2014 show that DQLSFLA's convergence quality and optimization efficiency are much better than LSFLA's and that compared with quite a few other state-of-the-art algorithms, DQLSFLA has better performance. The results on medical image enhancement and Quadratic Assignment Problem (QAP) also show that DQLSFLA can solve the real word optimization problems better than LSFLA can.

关键词： intelligent optimization algorithm shuffled frog leaping algorithm (SFLA) Levy flight quasi-Newton search image enhancement quadratic assignment problem

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Energy-Aware Hybrid Cooperative Relaying with Asymmetric Traffic

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ETRI JOURNAL 2015年第4期37卷 717-726页

作者： Chen, Jian Lv, Lu Geng, Wenjin Kuo, Yonghong Xidian Univ Sch Telecommun Engn Xian Peoples R China

In this paper, we study an asymmetric two-way relaying network where. two source nodes intend to exchange information with the help of multiple relay nodes. A hybrid time-division broadcast relaying scheme with joint relay selection (RS) and power allocation (PA) is proposed to realize energy-efficient transmission. Our scheme is based on the asymmetric level of the two source nodes' target signal-to-noise ratio indexes to minimize the total power consumed by the relay nodes. An optimization model with joint RS and PA is studied here to guarantee hybrid relaying transmissions. Next, with the aid of our proposed intelligent optimization algorithm, which combines a genetic algorithm and a simulated annealing algorithm, the formulated optimization model can be effectively solved. Theoretical analyses and numerical results verify that our proposed hybrid relaying scheme can substantially reduce the total power consumption of relays under a traffic asymmetric scenario;meanwhile, the proposed intelligent optimization algorithm can eventually converge to a better solution.

关键词： Hybrid relaying strategy asymmetric traffic energy consumption relay selection power allocation intelligent optimization algorithm PA RS

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A simultaneous optimization of a flexible heat exchanger network under uncertain conditions

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APPLIED THERMAL ENGINEERING 2021年第Part2期183卷 116230-116230页

作者： Wang, Shihao Tian, Yitong Li, Shaojun East China Univ Sci & Technol Key Lab Adv Control & Optimizat Chem Proc Minist Educ Shanghai 200237 Peoples R China

Heat exchanger networks are important for energy recovery and cost reduction. Flexible networks can handle environmental disturbances, and is more practical for industrial processes than the conventional HEN design with fixed parameters. Previous research on flexible HEN synthesis has mainly applied sequential synthesis, in which the structure determination and area optimization are conducted separately, or other multi-period methods in which only a few extreme operating conditions are selected;however, these methods are not sufficiently accurate for solving nonconvex problems. In this study, a bi-level simultaneous optimization strategy is proposed for the synthesis of flexible HENs under the nominal and all the extreme operating conditions. A binary particle swarm optimization is used in the outer layer to optimize the structural variables, and an Alopex-based evolutionary algorithm is used in the inner layer to determine the heat exchanger areas. Via this method, the result of a convex problem can be directly obtained. Furthermore, to deal with nonconvex problems, a flexibility analysis is conducted to adjust the heat exchanger areas on each critical point. The results of two cases with lower total annual costs than those reported previously are included to demonstrate the efficiency of the proposed method.

关键词： Flexible heat exchanger network Simultaneous synthesis Uncertain fluctuations intelligent optimization algorithm

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In-field calibration of triaxial accelerometer based on PE-ANGO

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TM-TECHNISCHES MESSEN 2024年第2期91卷 86-101页

作者： Qiao, Meiying Yao, Wenhao Gao, Kefei Du, Heng Zhao, Kaidong Henan Polytech Univ Sch Elect Engn & Automat Jiaozuo 454003 Peoples R China

This paper presents a new method for in-field calibration of accelerometers to address the problems of low efficiency and high cost associated with traditional calibration methods. A nonlinear mathematical model of the accelerometer is established, and the cost function is analysed and deduced the cost function. Then, an adaptive Northern Goshawk Optimisation (NGO) algorithm based on prior knowledge enhancement is introduced. A method of collecting multi-position data with a hand-held accelerometer is introduced, and the proposed algorithm is used to in-field calibrate nine parameters of the accelerometer's nonlinear error model. In addition, simulation is used to compare the results of calibrating the accelerometer using the proposed algorithm and the original algorithm, demonstrating the superiority of the proposed algorithm. Finally, experimental results confirm that the proposed method can rapidly calibrate accelerometer error parameters without relying on complex equipment and with greater accuracy than traditional methods.

关键词： calibration Inertial Measurement Unit three-axis accelerometer intelligent optimization algorithm Northern Goshawk optimization algorithm

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Multi-variable intelligent matching pursuit algorithm using prior knowledge for image reconstruction by l₀ minimization

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NEUROCOMPUTING 2016年 207卷 548-559页

作者： Li, Dan Wang, Qiang Shen, Yi Harbin Inst Technol Control Sci & Engn 92 West Da Zhi St Harbin 150001 Peoples R China

Image reconstruction by l(0) minimization is an NP-hard problem that requires exhaustively listing all possibilities of the original signal with a very high computational complexity, which is difficult to be achieved by traditional algorithms. Although greedy algorithm aims at solving l(0) minimization, it is more likely to fall into a suboptimal solution. In this paper, we propose a multi:variable intelligent matching pursuit algorithm (MIMP), which can solve l(0) minimization problem essentially by taking the advantage of the intelligent optimization algorithm in solving combinatorial optimization problems and searching for the global optimal solution to improve the performance of image reconstruction. The updating mechanism of MIMP is designed by introducing the matching strategies of greedy algorithm to accelerate the reconstruction speed. Also, the multi-variable scheme is utilized to sample images and then the joint reconstruction is implemented to the measurements, which can not only improve the reconstruction accuracy but also reduce the computational complexity. Moreover, the edge saliency can be obtained as the prior knowledge to guide the compressive sensing reconstruction, which contributes a lot to reduce the computational complexity and accelerate the reconstruction speed. As the sparsity level of image is hard to be estimated, anew optimization function is proposed to solve this problem without knowing the sparsity level as a prior. Compared with other state-of-the-art algorithms, the proposed method MIMP can achieve a better reconstruction accuracy by solving l(0) minimization essentially with intelligent optimization algorithms. Also, MIMP has a reasonable relatively faster reconstruction speed by introducing the matching strategies of greedy algorithm and using the edge saliency as a prior knowledge. Numerical experiments on several images demonstrate that the proposed method MIMP significantly outperforms the state-of-the-art algorithms and the st

关键词： Compressive sensing l(0) minimization intelligent optimization algorithm Prior knowledge Multi -variable scheme

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An intelligent CPSOGSA-based mixed H2/H∞ robust controller for the multi-hydro-turbine governing system with sharing common penstock

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RENEWABLE ENERGY 2023年第1期206卷 481-497页

作者： Dao, Fang Zou, Yidong Zeng, Yun Qian, Jing Li, Xiang Kunming Univ Sci & Technol Faulty Met & Energy Engn Kunming 650093 Peoples R China Wuhan Univ Sch Power & Mech Engn Wuhan 430072 Peoples R China Kunming Univ Sci & Technol Kunming 650093 Peoples R China

In order to improve the stability of the multi-hydro-turbine governing system with sharing one penstock (MGSSP) during operation, an intelligent CPSOGSA-based mixed H2/H infinity robust controller with an integrator is proposed. The excellent control performance of the H2/H infinity robust controller is used to stabilize the system. Firstly, the mathematical model of the MGSSP is derived from the transfer function. Secondly, the robust controller is designed based on the linear matrix inequality (LMI), considered the optimal and robust performance. The modified hybrid PSOGSA with chaotic maps algorithm (CPSOGSA) is also introduced in the design process to ensure the controller is optimal. The designed robust controller is simple in structure, low in order, and easy to implement. Simulation experiments show that the CPSOGSA can complete the optimization of the controller with less than ten iterations. The designed robust controller can stabilize the system in less than 10 s for different operating conditions. The robust controller enables the controlled object to exhibit better dynamic performance than the conventional controller. The research results can provide a reference for the control application of the extended operation of the hydro-turbine governing system (HTGS).

关键词： Multi-hydro-turbine governing system with sharing one penstock (MGSSP) H2 H? robust controller intelligent optimization algorithm Linear matrix inequality (LMI)

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