{"title":"基于灰盒法的双转子MIMO系统动力学建模","authors":"A. Rahideh, M. Shaheed","doi":"10.1109/ISMA.2008.4648835","DOIUrl":null,"url":null,"abstract":"This investigation presents a grey box modelling approach for an experimental nonlinear aerodynamic test rig, a twin rotor multi-input-multi-output system (TRMS) using genetic algorithms (GA). The dynamic equations of the system in terms of 2 degrees of freedom (2DOF) are developed using Newtonian method. The measurable parameters of the system are measured and others are estimated using physical knowledge of the system. In order to improve the accuracy of the model the estimated parameters are returned using a GA optimization approach. Note that the estimated parameters of the system are utilized as the initial populations of the GA. The performances of the white and grey box models are compared with respect to each other to validate the improvement of the grey box approach. The developed model will be used in a nonlinear model predictive control approach.","PeriodicalId":350202,"journal":{"name":"2008 5th International Symposium on Mechatronics and Its Applications","volume":"102 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2008-05-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"19","resultStr":"{\"title\":\"Dynamic modelling of a twin rotor MIMO system using grey box approach\",\"authors\":\"A. Rahideh, M. Shaheed\",\"doi\":\"10.1109/ISMA.2008.4648835\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This investigation presents a grey box modelling approach for an experimental nonlinear aerodynamic test rig, a twin rotor multi-input-multi-output system (TRMS) using genetic algorithms (GA). The dynamic equations of the system in terms of 2 degrees of freedom (2DOF) are developed using Newtonian method. The measurable parameters of the system are measured and others are estimated using physical knowledge of the system. In order to improve the accuracy of the model the estimated parameters are returned using a GA optimization approach. Note that the estimated parameters of the system are utilized as the initial populations of the GA. The performances of the white and grey box models are compared with respect to each other to validate the improvement of the grey box approach. The developed model will be used in a nonlinear model predictive control approach.\",\"PeriodicalId\":350202,\"journal\":{\"name\":\"2008 5th International Symposium on Mechatronics and Its Applications\",\"volume\":\"102 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2008-05-27\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"19\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2008 5th International Symposium on Mechatronics and Its Applications\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ISMA.2008.4648835\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2008 5th International Symposium on Mechatronics and Its Applications","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ISMA.2008.4648835","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Dynamic modelling of a twin rotor MIMO system using grey box approach
This investigation presents a grey box modelling approach for an experimental nonlinear aerodynamic test rig, a twin rotor multi-input-multi-output system (TRMS) using genetic algorithms (GA). The dynamic equations of the system in terms of 2 degrees of freedom (2DOF) are developed using Newtonian method. The measurable parameters of the system are measured and others are estimated using physical knowledge of the system. In order to improve the accuracy of the model the estimated parameters are returned using a GA optimization approach. Note that the estimated parameters of the system are utilized as the initial populations of the GA. The performances of the white and grey box models are compared with respect to each other to validate the improvement of the grey box approach. The developed model will be used in a nonlinear model predictive control approach.