| Titre : | Optimal parameter estimation for long-term prediction in the presence of model mismatch (2012) |
| Auteurs : | Ryan Sangjun Lee, Auteur ; Gregery T. Buzzard, Auteur ; Peter H. Meckl, Auteur |
| Type de document : | Article : texte imprimé |
| Dans : | Transactions of the ASME . Journal of dynamic systems, measurement, and control (Vol. 134 N° 4, Juillet 2012) |
| Article en page(s) : | 16 p. |
| Note générale : | Dynamic systems |
| Langues : | Anglais |
| Index. décimale : | 629.8 |
| Tags : | Nonlinear multi-input multi-output (MIMO) systems Least-squares minimization Long-term prediction error MIMO nonlinear systems. |
| 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=JDSMAA000134000004041010000001&idtype=cvips&gifs=Yes&ref=no |
