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|Type:||Artigo de evento|
|Title:||Real-time Nonlinear Modeling Of A Twin Rotor Mimo System Using Evolving Neuro-fuzzy Network|
|Abstract:||This paper presents an evolving neuro-fuzzy network approach (eNFN) to model a twin rotor MIMO system (TRMS) with two degrees of freedom in real-time. The TRMS is a fast, nonlinear, open loop unstable time-varying dynamic system, with cross coupling between the rotors. Modeling and control of TRMS require high sampling rates, typically in the order of milliseconds. Actual laboratory implementation shows that eNFN is fast, effective, and accurately models the TRMS in real-time. The eNFN captures the TRMS system dynamics quickly, and develops precise low cost models from the point of view of time and space complexity. The results suggest eNFN as a potential candidate to model complex, fast time-varying dynamic systems in real-time.|
|Editor:||Institute of Electrical and Electronics Engineers Inc.|
|Citation:||Ieee Ssci 2014 - 2014 Ieee Symposium Series On Computational Intelligence - Cica 2014: 2014 Ieee Symposium On Computational Intelligence In Control And Automation, Proceedings. Institute Of Electrical And Electronics Engineers Inc., v. , n. , p. - , 2015.|
|Appears in Collections:||Unicamp - Artigos e Outros Documentos|
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