Optimal parameter estimation for long-term prediction in the presence of model mismatch / Ryan Sangjun Lee in Transactions of the ASME . Journal of dynamic systems, measurement, and control, Vol. 134 N° 4 (Juillet 2012)  

Public ISBD [article]  inTransactions of the ASME . Journal of dynamic systems, measurement, and control > Vol. 134 N° 4 (Juillet 2012) . - 16 p. Titre : Optimal parameter estimation for long-term prediction in the presence of model mismatch Type de document : texte imprimé Auteurs : Ryan Sangjun Lee, Auteur ; Gregery T. Buzzard, Auteur ; Peter H. Meckl, Auteur Année de publication : 2012 Article en page(s) : 16 p. Note générale : Dynamic systems Langues : Anglais (eng) Mots-clés : Nonlinear multi-input multi-output (MIMO) systems Least-squares minimization Long-term prediction error MIMO nonlinear systems. Index. décimale : 629.8 Résumé : For nonlinear multi-input multi-output (MIMO) systems such as multilink robotic manipulators, finding a correct, physically derived model structure is almost impossible, so that significant model mismatch is nearly inevitable. Moreover, in the presence of model mismatch, the use of least-squares minimization of the one-step-ahead prediction error (residual error) to estimate unknown parameters in a given model structure often leads to model predictions that are extremely inaccurate beyond a short time interval. In this paper, we develop a method for optimal parameter estimation for accurate long-term prediction models in the presence of significant model mismatch in practice. For many practical cases, where a correct model and the correct number of degrees of freedom for a given model structure are unknown, we combine the use of long-term prediction error with frequency-based regularization to produce more accurate long-term prediction models for actual MIMO nonlinear systems. DEWEY : 629.8 ISSN : 0022-0434 En ligne : http://asmedl.org/getabs/servlet/GetabsServlet?prog=normal&id=JDSMAA000134000004 [...] [article] Optimal parameter estimation for long-term prediction in the presence of model mismatch [texte imprimé] / Ryan Sangjun Lee, Auteur ; Gregery T. Buzzard, Auteur ; Peter H. Meckl, Auteur . - 2012 . - 16 p. Dynamic systems Langues : Anglais (eng) in Transactions of the ASME . Journal of dynamic systems, measurement, and control > Vol. 134 N° 4 (Juillet 2012) . - 16 p. Mots-clés : Nonlinear multi-input multi-output (MIMO) systems Least-squares minimization Long-term prediction error MIMO nonlinear systems. Index. décimale : 629.8 Résumé : For nonlinear multi-input multi-output (MIMO) systems such as multilink robotic manipulators, finding a correct, physically derived model structure is almost impossible, so that significant model mismatch is nearly inevitable. Moreover, in the presence of model mismatch, the use of least-squares minimization of the one-step-ahead prediction error (residual error) to estimate unknown parameters in a given model structure often leads to model predictions that are extremely inaccurate beyond a short time interval. In this paper, we develop a method for optimal parameter estimation for accurate long-term prediction models in the presence of significant model mismatch in practice. For many practical cases, where a correct model and the correct number of degrees of freedom for a given model structure are unknown, we combine the use of long-term prediction error with frequency-based regularization to produce more accurate long-term prediction models for actual MIMO nonlinear systems. DEWEY : 629.8 ISSN : 0022-0434 En ligne : http://asmedl.org/getabs/servlet/GetabsServlet?prog=normal&id=JDSMAA000134000004 [...]

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