{"title":"基于迭代学习的致动器/传感器故障估计,适用于一类具有时间延迟的重复非线性系统","authors":"Kenan Du, Li Feng, Yi Chai, Meng Deng","doi":"10.1002/rnc.7544","DOIUrl":null,"url":null,"abstract":"<p>To meet the demand for estimating actuator and sensor faults simultaneously in a class of repetitive nonlinear time-delay systems, this paper proposes a novel fault estimation strategy based on an iterative learning scheme. Firstly, an iterative-learning-based fault estimation law is designed to estimate actuator faults while system is free of sensor failures. Both the fixed initial shift and random one are taken into consideration. Secondly, a novel sensor fault observer is proposed based on an augmented state variable which consists of original system state and sensor fault signal; output compensation strategy is also provided to ensure the iterative-learning-based actuator fault estimation method is effective considering the existence of sensor failures. In addition, theorems based on <span></span><math>\n \n <semantics>\n <mrow>\n <mi>λ</mi>\n </mrow>\n <annotation>$$ \\lambda $$</annotation>\n </semantics></math>-norm and linear matrix inequality are provided to determine values or ranges of gain matrices and parameters in proposed sensor fault observer and iterative-learning-based actuator fault estimation law. Finally, two simulation examples are provided to illustrate the effectiveness of the proposed methods.</p>","PeriodicalId":50291,"journal":{"name":"International Journal of Robust and Nonlinear Control","volume":"34 16","pages":"10842-10866"},"PeriodicalIF":3.2000,"publicationDate":"2024-08-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Iterative-learning-based actuator/sensor fault estimation for a class of repetitive nonlinear systems with time-delay\",\"authors\":\"Kenan Du, Li Feng, Yi Chai, Meng Deng\",\"doi\":\"10.1002/rnc.7544\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p>To meet the demand for estimating actuator and sensor faults simultaneously in a class of repetitive nonlinear time-delay systems, this paper proposes a novel fault estimation strategy based on an iterative learning scheme. Firstly, an iterative-learning-based fault estimation law is designed to estimate actuator faults while system is free of sensor failures. Both the fixed initial shift and random one are taken into consideration. Secondly, a novel sensor fault observer is proposed based on an augmented state variable which consists of original system state and sensor fault signal; output compensation strategy is also provided to ensure the iterative-learning-based actuator fault estimation method is effective considering the existence of sensor failures. In addition, theorems based on <span></span><math>\\n \\n <semantics>\\n <mrow>\\n <mi>λ</mi>\\n </mrow>\\n <annotation>$$ \\\\lambda $$</annotation>\\n </semantics></math>-norm and linear matrix inequality are provided to determine values or ranges of gain matrices and parameters in proposed sensor fault observer and iterative-learning-based actuator fault estimation law. Finally, two simulation examples are provided to illustrate the effectiveness of the proposed methods.</p>\",\"PeriodicalId\":50291,\"journal\":{\"name\":\"International Journal of Robust and Nonlinear Control\",\"volume\":\"34 16\",\"pages\":\"10842-10866\"},\"PeriodicalIF\":3.2000,\"publicationDate\":\"2024-08-12\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"International Journal of Robust and Nonlinear Control\",\"FirstCategoryId\":\"94\",\"ListUrlMain\":\"https://onlinelibrary.wiley.com/doi/10.1002/rnc.7544\",\"RegionNum\":3,\"RegionCategory\":\"计算机科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"AUTOMATION & CONTROL SYSTEMS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"International Journal of Robust and Nonlinear Control","FirstCategoryId":"94","ListUrlMain":"https://onlinelibrary.wiley.com/doi/10.1002/rnc.7544","RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"AUTOMATION & CONTROL SYSTEMS","Score":null,"Total":0}
Iterative-learning-based actuator/sensor fault estimation for a class of repetitive nonlinear systems with time-delay
To meet the demand for estimating actuator and sensor faults simultaneously in a class of repetitive nonlinear time-delay systems, this paper proposes a novel fault estimation strategy based on an iterative learning scheme. Firstly, an iterative-learning-based fault estimation law is designed to estimate actuator faults while system is free of sensor failures. Both the fixed initial shift and random one are taken into consideration. Secondly, a novel sensor fault observer is proposed based on an augmented state variable which consists of original system state and sensor fault signal; output compensation strategy is also provided to ensure the iterative-learning-based actuator fault estimation method is effective considering the existence of sensor failures. In addition, theorems based on -norm and linear matrix inequality are provided to determine values or ranges of gain matrices and parameters in proposed sensor fault observer and iterative-learning-based actuator fault estimation law. Finally, two simulation examples are provided to illustrate the effectiveness of the proposed methods.
期刊介绍:
Papers that do not include an element of robust or nonlinear control and estimation theory will not be considered by the journal, and all papers will be expected to include significant novel content. The focus of the journal is on model based control design approaches rather than heuristic or rule based methods. Papers on neural networks will have to be of exceptional novelty to be considered for the journal.