{"title":"基于自组织特征选择模糊神经网络的不确定非线性系统终端滑模控制。","authors":"Yundi Chu, Cheng Zhou, Shixi Hou, Houzhi Chen, Juntao Fei","doi":"10.1016/j.isatra.2024.09.007","DOIUrl":null,"url":null,"abstract":"<div><div>A composite terminal sliding mode controller (CTSMC) for a kind of uncertain nonlinear system (UNS) is developed in this study. The primary aim of the design is to enhance the control performance of the CTSMC by learning its unknown parameters using a newly fuzzy neural network (FNN). Firstly, the stability and convergence of CTSMC for UNS with known parameters are demonstrated. Secondly, since some parameters of actual UNS are unmeasurable, a self-organizing feature selection fuzzy neural network (SOFSFNN) is intended to approach these unknown parts. Finally, the CTSMC using SOFSFNN is applied to UNS. The outcomes demonstrate that it has minimal tracking error, good robustness, and the ability to dynamically modify the network structure.</div></div>","PeriodicalId":14660,"journal":{"name":"ISA transactions","volume":"154 ","pages":"Pages 171-185"},"PeriodicalIF":6.3000,"publicationDate":"2024-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Self-organizing feature selection fuzzy neural network-based terminal sliding mode control for uncertain nonlinear systems\",\"authors\":\"Yundi Chu, Cheng Zhou, Shixi Hou, Houzhi Chen, Juntao Fei\",\"doi\":\"10.1016/j.isatra.2024.09.007\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><div>A composite terminal sliding mode controller (CTSMC) for a kind of uncertain nonlinear system (UNS) is developed in this study. The primary aim of the design is to enhance the control performance of the CTSMC by learning its unknown parameters using a newly fuzzy neural network (FNN). Firstly, the stability and convergence of CTSMC for UNS with known parameters are demonstrated. Secondly, since some parameters of actual UNS are unmeasurable, a self-organizing feature selection fuzzy neural network (SOFSFNN) is intended to approach these unknown parts. Finally, the CTSMC using SOFSFNN is applied to UNS. The outcomes demonstrate that it has minimal tracking error, good robustness, and the ability to dynamically modify the network structure.</div></div>\",\"PeriodicalId\":14660,\"journal\":{\"name\":\"ISA transactions\",\"volume\":\"154 \",\"pages\":\"Pages 171-185\"},\"PeriodicalIF\":6.3000,\"publicationDate\":\"2024-11-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"ISA transactions\",\"FirstCategoryId\":\"94\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S0019057824004373\",\"RegionNum\":2,\"RegionCategory\":\"计算机科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"AUTOMATION & CONTROL SYSTEMS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"ISA transactions","FirstCategoryId":"94","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0019057824004373","RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"AUTOMATION & CONTROL SYSTEMS","Score":null,"Total":0}
引用次数: 0
摘要
本研究针对一种不确定非线性系统(UNS)开发了一种复合终端滑模控制器(CTSMC)。设计的主要目的是利用新的模糊神经网络(FNN)学习 CTSMC 的未知参数,从而提高其控制性能。首先,证明了已知参数 UNS 的 CTSMC 的稳定性和收敛性。其次,由于实际 UNS 的某些参数是不可测量的,因此打算使用自组织特征选择模糊神经网络(SOFSFNN)来接近这些未知部分。最后,将使用 SOFSFNN 的 CTSMC 应用于 UNS。结果表明,它具有最小的跟踪误差、良好的鲁棒性和动态修改网络结构的能力。
Self-organizing feature selection fuzzy neural network-based terminal sliding mode control for uncertain nonlinear systems
A composite terminal sliding mode controller (CTSMC) for a kind of uncertain nonlinear system (UNS) is developed in this study. The primary aim of the design is to enhance the control performance of the CTSMC by learning its unknown parameters using a newly fuzzy neural network (FNN). Firstly, the stability and convergence of CTSMC for UNS with known parameters are demonstrated. Secondly, since some parameters of actual UNS are unmeasurable, a self-organizing feature selection fuzzy neural network (SOFSFNN) is intended to approach these unknown parts. Finally, the CTSMC using SOFSFNN is applied to UNS. The outcomes demonstrate that it has minimal tracking error, good robustness, and the ability to dynamically modify the network structure.
期刊介绍:
ISA Transactions serves as a platform for showcasing advancements in measurement and automation, catering to both industrial practitioners and applied researchers. It covers a wide array of topics within measurement, including sensors, signal processing, data analysis, and fault detection, supported by techniques such as artificial intelligence and communication systems. Automation topics encompass control strategies, modelling, system reliability, and maintenance, alongside optimization and human-machine interaction. The journal targets research and development professionals in control systems, process instrumentation, and automation from academia and industry.