Bat optimization of hybrid neural network-FOPID controllers for robust robot manipulator control.

IF 2.9 Q2 ROBOTICS
Frontiers in Robotics and AI Pub Date : 2025-05-02 eCollection Date: 2025-01-01 DOI:10.3389/frobt.2025.1487844
Bashra Kadhim Oleiwi, Mohamed Jasim, Ahmad Taher Azar, Saim Ahmed, Ahmed Redha Mahlous
{"title":"Bat optimization of hybrid neural network-FOPID controllers for robust robot manipulator control.","authors":"Bashra Kadhim Oleiwi, Mohamed Jasim, Ahmad Taher Azar, Saim Ahmed, Ahmed Redha Mahlous","doi":"10.3389/frobt.2025.1487844","DOIUrl":null,"url":null,"abstract":"<p><p>The position and trajectory tracking control of rigid-link robot manipulators suffers from problems such as poor accuracy, unstable performance, and response caused by unidentified loads and outside disturbances. In this paper, three control structures have been proposed to control a multi-input, multi-output coupled nonlinear three-link rigid robot manipulator (3-LRRM) system and effectively solve the signal chattering in the control signal. To overcome these problems, three hybrid control structures based on combinations between the benefits of fractional order proportional-integral-derivative operations (FOPID) and the benefits of neural networks are proposed for a 3-LRRM. The first hybrid control scheme is a neural network- (NN) like fractional order proportional-integral plus an NN-like fractional order proportional derivative controller (NN-FOPIPD) and the second control scheme is an NN plus FOPID controller (NN + FOPID). In contrast, the third control scheme is the Elman NN-like FOPID controller (ELNN-FOPID). The bat optimization algorithm (BOA) is applied to find the best parameter values of the proposed control scheme by minimizing the performance index of the integral time square error (ITSE). MATLAB software is used to carry out the simulation results. Using the simulation tests, the performance of the suggested controllers is compared without retraining the controller parameters. The robustness of the designed control schemes' performance is assessed utilizing uncertainties in system parameters, outside disturbances, and initial position changes. The results show that the NN-FOPIPD structure demonstrated the best performance among the suggested controllers.</p>","PeriodicalId":47597,"journal":{"name":"Frontiers in Robotics and AI","volume":"12 ","pages":"1487844"},"PeriodicalIF":2.9000,"publicationDate":"2025-05-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12082718/pdf/","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Frontiers in Robotics and AI","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.3389/frobt.2025.1487844","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2025/1/1 0:00:00","PubModel":"eCollection","JCR":"Q2","JCRName":"ROBOTICS","Score":null,"Total":0}
引用次数: 0

Abstract

The position and trajectory tracking control of rigid-link robot manipulators suffers from problems such as poor accuracy, unstable performance, and response caused by unidentified loads and outside disturbances. In this paper, three control structures have been proposed to control a multi-input, multi-output coupled nonlinear three-link rigid robot manipulator (3-LRRM) system and effectively solve the signal chattering in the control signal. To overcome these problems, three hybrid control structures based on combinations between the benefits of fractional order proportional-integral-derivative operations (FOPID) and the benefits of neural networks are proposed for a 3-LRRM. The first hybrid control scheme is a neural network- (NN) like fractional order proportional-integral plus an NN-like fractional order proportional derivative controller (NN-FOPIPD) and the second control scheme is an NN plus FOPID controller (NN + FOPID). In contrast, the third control scheme is the Elman NN-like FOPID controller (ELNN-FOPID). The bat optimization algorithm (BOA) is applied to find the best parameter values of the proposed control scheme by minimizing the performance index of the integral time square error (ITSE). MATLAB software is used to carry out the simulation results. Using the simulation tests, the performance of the suggested controllers is compared without retraining the controller parameters. The robustness of the designed control schemes' performance is assessed utilizing uncertainties in system parameters, outside disturbances, and initial position changes. The results show that the NN-FOPIPD structure demonstrated the best performance among the suggested controllers.

混合神经网络fopid控制器的蝙蝠优化鲁棒机器人操纵臂控制。
刚性连杆机器人的位置和轨迹跟踪控制存在精度差、性能不稳定、响应受未知载荷和外界干扰等问题。针对多输入、多输出耦合非线性三连杆刚性机器人(3-LRRM)系统,提出了三种控制结构,有效地解决了控制信号中的抖振问题。为了克服这些问题,针对3-LRRM,提出了3种基于分数阶比例-积分-导数运算(FOPID)和神经网络优势相结合的混合控制结构。第一种混合控制方案是类神经网络(NN)分数阶比例积分加类神经网络分数阶比例导数控制器(NN- fopipd),第二种混合控制方案是类神经网络(NN)加FOPID控制器(NN + FOPID)。相比之下,第三种控制方案是Elman NN-like FOPID控制器(ELNN-FOPID)。采用蝙蝠优化算法(BOA),通过最小化积分时方误差(ITSE)的性能指标来找到所提出的控制方案的最佳参数值。利用MATLAB软件对仿真结果进行了分析。通过仿真测试,在不重新训练控制器参数的情况下,比较了所建议控制器的性能。利用系统参数、外界干扰和初始位置变化的不确定性来评估所设计控制方案的鲁棒性。结果表明,NN-FOPIPD结构在所有控制器中表现出最好的性能。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 求助全文
来源期刊
CiteScore
6.50
自引率
5.90%
发文量
355
审稿时长
14 weeks
期刊介绍: Frontiers in Robotics and AI publishes rigorously peer-reviewed research covering all theory and applications of robotics, technology, and artificial intelligence, from biomedical to space robotics.
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信