{"title":"非线性MIMO系统辨识中的智能混合主动控制","authors":"T. Mohamed, K. A. K. Ishak, H. Ramli, M. S. Meon","doi":"10.1109/SCORED.2012.6518622","DOIUrl":null,"url":null,"abstract":"This paper presents Active Force Control (AFC) based scheme embedded with neural network and fuzzy logic in scheming the twin rotor multi-input multi-output (MIMO) system (TRMS). This architecture is proposed due to limitations of classic PID lead to difficulty in compensate the disturbances and internal changes appertain by angular momentum and reaction turning between two axes. The proposed architecture is employed in both pitch and yaw control scheme to optimize the responses. The results shown a very significant achievement as the proposed candidate give reasonably good performance and capable of compensating the internal and external disturbances. The integration of Neural Network and fuzzy logic is proven to be a potential hybrid as it provides an advanced optimization in accelerates the performance of TRMS.","PeriodicalId":299947,"journal":{"name":"2012 IEEE Student Conference on Research and Development (SCOReD)","volume":"1 4","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2012-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"Intelligent hybrid Active Force Control in identification of a nonlinear MIMO system\",\"authors\":\"T. Mohamed, K. A. K. Ishak, H. Ramli, M. S. Meon\",\"doi\":\"10.1109/SCORED.2012.6518622\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This paper presents Active Force Control (AFC) based scheme embedded with neural network and fuzzy logic in scheming the twin rotor multi-input multi-output (MIMO) system (TRMS). This architecture is proposed due to limitations of classic PID lead to difficulty in compensate the disturbances and internal changes appertain by angular momentum and reaction turning between two axes. The proposed architecture is employed in both pitch and yaw control scheme to optimize the responses. The results shown a very significant achievement as the proposed candidate give reasonably good performance and capable of compensating the internal and external disturbances. The integration of Neural Network and fuzzy logic is proven to be a potential hybrid as it provides an advanced optimization in accelerates the performance of TRMS.\",\"PeriodicalId\":299947,\"journal\":{\"name\":\"2012 IEEE Student Conference on Research and Development (SCOReD)\",\"volume\":\"1 4\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2012-12-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2012 IEEE Student Conference on Research and Development (SCOReD)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/SCORED.2012.6518622\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2012 IEEE Student Conference on Research and Development (SCOReD)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/SCORED.2012.6518622","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Intelligent hybrid Active Force Control in identification of a nonlinear MIMO system
This paper presents Active Force Control (AFC) based scheme embedded with neural network and fuzzy logic in scheming the twin rotor multi-input multi-output (MIMO) system (TRMS). This architecture is proposed due to limitations of classic PID lead to difficulty in compensate the disturbances and internal changes appertain by angular momentum and reaction turning between two axes. The proposed architecture is employed in both pitch and yaw control scheme to optimize the responses. The results shown a very significant achievement as the proposed candidate give reasonably good performance and capable of compensating the internal and external disturbances. The integration of Neural Network and fuzzy logic is proven to be a potential hybrid as it provides an advanced optimization in accelerates the performance of TRMS.