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EnglishThis paper deals with a new practical approach to continuous-time parametric identification for dynamic models in Hammerstein form and its application in the context of estimating the dynamics of joint stiffness. The proposed methodology deals the static non-linearities as a linear combination of nonlinear basis functions, while the linear dynamic parts of the system are modeled by a parametric rational transfer function. The proposed identification method is enabled by the algebra of Volterra linear integral operators by a suitable design of kernels admitting finite-dimensional and internally stable statespace realizations. Simulation results show the effectiveness of the proposed continuous-time identification technique.
| Titolo: | Kernel-based Continuous-Time Identification of Hammerstein Models: Application to the case of Ankle Joint Stiffness Dynamics | |
| Autori: | ||
| Data di pubblicazione: | 2015 | |
| Abstract: | This paper deals with a new practical approach to continuous-time parametric identification for dynamic models in Hammerstein form and its application in the context of estimating the dynamics of joint stiffness. The proposed methodology deals the static non-linearities as a linear combination of nonlinear basis functions, while the linear dynamic parts of the system are modeled by a parametric rational transfer function. The proposed identification method is enabled by the algebra of Volterra linear integral operators by a suitable design of kernels admitting finite-dimensional and internally stable statespace realizations. Simulation results show the effectiveness of the proposed continuous-time identification technique. | |
| Handle: | http://hdl.handle.net/11368/2847828 | |
| URL: | http://ieeexplore.ieee.org/document/7330835/ | |
| Appare nelle tipologie: | 4.1 Contributo in Atti Convegno (Proceeding) |
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| Assalone_Pin_Parisini_ECC2015.pdf | pdf editoriale | Documento in Versione Editoriale | Digital Rights Management non definito | Administrator Richiedi una copia |
http://hdl.handle.net/11368/2847828
