{"title":"Neuro controller design for aircraft auto-landing under actuator failure and severe wind condition","authors":"Zhifeng Wang, Zhongjian Li","doi":"10.1109/ICCIAUTOM.2011.6183991","DOIUrl":null,"url":null,"abstract":"In this paper, a robust Neural Network (neuro) aided H2 control scheme is developed for a nonlinear F16 aircraft in auto-landing phase under actuator failure and severe wind conditions. In this scheme, a dynamic Radial Basis Function network called Minimal Resource Allocating Network (MRAN) that incorporates a growing and pruning strategy, is utilized to aid H2 controller using a feedback-error-learning mechanism. Specifically, the performance of this neuro-H2 controller for an aircraft auto-landing is studied and compared with H2 control schemes. Simulation studies show that the performance obtained by the neuro-H2 controller scheme is better than H2 controller.","PeriodicalId":177039,"journal":{"name":"2011 2nd International Conference on Control, Instrumentation and Automation (ICCIA)","volume":"492 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2011-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2011 2nd International Conference on Control, Instrumentation and Automation (ICCIA)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICCIAUTOM.2011.6183991","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
In this paper, a robust Neural Network (neuro) aided H2 control scheme is developed for a nonlinear F16 aircraft in auto-landing phase under actuator failure and severe wind conditions. In this scheme, a dynamic Radial Basis Function network called Minimal Resource Allocating Network (MRAN) that incorporates a growing and pruning strategy, is utilized to aid H2 controller using a feedback-error-learning mechanism. Specifically, the performance of this neuro-H2 controller for an aircraft auto-landing is studied and compared with H2 control schemes. Simulation studies show that the performance obtained by the neuro-H2 controller scheme is better than H2 controller.