Meijie Liu;Ping Luo;Changhua Hu;Rui Guo;Xiaoxiang Hu
{"title":"T-S模糊高超声速飞行器模型执行器故障检测:基于T-S模糊的H∞滑模观测器方法","authors":"Meijie Liu;Ping Luo;Changhua Hu;Rui Guo;Xiaoxiang Hu","doi":"10.1109/JMASS.2023.3278654","DOIUrl":null,"url":null,"abstract":"A T–S fuzzy-based \n<inline-formula> <tex-math>$H\\infty $ </tex-math></inline-formula>\n sliding mode observer (SMO)-based fault detection scheme is conducted to realize the actuator fault detection issue, including stuck fault detection and partial loss of effectiveness (PLOE) fault detection in our work. First, a T–S fuzzy attitude control model with an uncertainty term is derived from the original nonlinear hypersonic flight vehicle (HSV) model by combining local linear models at four equilibrium points. Second, the actuator fault model is introduced to further establish a T–S fuzzy HSV model with actuator faults. Then, a T–S fuzzy-based \n<inline-formula> <tex-math>$H\\infty $ </tex-math></inline-formula>\n SMO is designed for fault detection based on matrix coordinate transformation. Finally, the SMO observer simulation is conducted to the T–S fuzzy HSV model for single-input single-style actuator fault detection. The simulation results show that stuck faults can be timely and accurately detected at the fault time and the state change amplitude is approximately in direct-ratio relation with the amplitude of stuck faults, which is caused by the implicit relationship between the states and the flap. Unfortunately, the detection of PLOE faults encounters some difficulties for acceptable reasons and needs further attention and investigation.","PeriodicalId":100624,"journal":{"name":"IEEE Journal on Miniaturization for Air and Space Systems","volume":"4 3","pages":"274-282"},"PeriodicalIF":0.0000,"publicationDate":"2023-08-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Actuator Fault Detection of T–S Fuzzy Hypersonic Flight Vehicle Model: A T–S Fuzzy-Based H∞ Sliding Mode Observer Approach\",\"authors\":\"Meijie Liu;Ping Luo;Changhua Hu;Rui Guo;Xiaoxiang Hu\",\"doi\":\"10.1109/JMASS.2023.3278654\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"A T–S fuzzy-based \\n<inline-formula> <tex-math>$H\\\\infty $ </tex-math></inline-formula>\\n sliding mode observer (SMO)-based fault detection scheme is conducted to realize the actuator fault detection issue, including stuck fault detection and partial loss of effectiveness (PLOE) fault detection in our work. First, a T–S fuzzy attitude control model with an uncertainty term is derived from the original nonlinear hypersonic flight vehicle (HSV) model by combining local linear models at four equilibrium points. Second, the actuator fault model is introduced to further establish a T–S fuzzy HSV model with actuator faults. Then, a T–S fuzzy-based \\n<inline-formula> <tex-math>$H\\\\infty $ </tex-math></inline-formula>\\n SMO is designed for fault detection based on matrix coordinate transformation. Finally, the SMO observer simulation is conducted to the T–S fuzzy HSV model for single-input single-style actuator fault detection. The simulation results show that stuck faults can be timely and accurately detected at the fault time and the state change amplitude is approximately in direct-ratio relation with the amplitude of stuck faults, which is caused by the implicit relationship between the states and the flap. Unfortunately, the detection of PLOE faults encounters some difficulties for acceptable reasons and needs further attention and investigation.\",\"PeriodicalId\":100624,\"journal\":{\"name\":\"IEEE Journal on Miniaturization for Air and Space Systems\",\"volume\":\"4 3\",\"pages\":\"274-282\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2023-08-22\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"IEEE Journal on Miniaturization for Air and Space Systems\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://ieeexplore.ieee.org/document/10226530/\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE Journal on Miniaturization for Air and Space Systems","FirstCategoryId":"1085","ListUrlMain":"https://ieeexplore.ieee.org/document/10226530/","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Actuator Fault Detection of T–S Fuzzy Hypersonic Flight Vehicle Model: A T–S Fuzzy-Based H∞ Sliding Mode Observer Approach
A T–S fuzzy-based
$H\infty $
sliding mode observer (SMO)-based fault detection scheme is conducted to realize the actuator fault detection issue, including stuck fault detection and partial loss of effectiveness (PLOE) fault detection in our work. First, a T–S fuzzy attitude control model with an uncertainty term is derived from the original nonlinear hypersonic flight vehicle (HSV) model by combining local linear models at four equilibrium points. Second, the actuator fault model is introduced to further establish a T–S fuzzy HSV model with actuator faults. Then, a T–S fuzzy-based
$H\infty $
SMO is designed for fault detection based on matrix coordinate transformation. Finally, the SMO observer simulation is conducted to the T–S fuzzy HSV model for single-input single-style actuator fault detection. The simulation results show that stuck faults can be timely and accurately detected at the fault time and the state change amplitude is approximately in direct-ratio relation with the amplitude of stuck faults, which is caused by the implicit relationship between the states and the flap. Unfortunately, the detection of PLOE faults encounters some difficulties for acceptable reasons and needs further attention and investigation.