Observatorio de I+D+i UPM

Descripción
It is common in mechanical simulation to not know the value of key system parameters. When the simulation is very sensitive to those design parameters and practical or budget limitations prevent the user from measuring the real values, parameter identification methods become essential. Kalman filter methods and optimization methods are the most widespread approaches for the identification of unknown parameters in multibody systems. A novel gradient-based optimization method, based on sensitivity analyses for the computation of machine-precision gradients, is presented in this paper. The direct differentiation approach, together with the algorithmic differentiation of derivative terms, is employed to compute state and design sensitivities. This results in an automated, generalpurpose and robust method for the identification of parameters. The method is applied to the identification of a real-life vehicle suspension system (namely of five stiffness coefficients) where both smooth and noisy reference responses are considered. The identified values are very close to the reference ones, and everything is carried out with limited user intervention and no manual computation of derivatives.
Internacional	Si
JCR del ISI	Si
Título de la revista	Journal of Mechanical Design
ISSN	1050-0472
Factor de impacto JCR	1,247
Información de impacto	Datos JCR del año 2012
Volumen	137
DOI	10.1115/1.4029027
Número de revista
Desde la página	021403
Hasta la página	021411
Mes	SIN MES
Ranking

Esta actividad pertenece a memorias de investigación

• Autor: Alfonso Callejo Goena (UPM)
• Autor: Francisco Javier Garcia de Jalon De la Fuente (UPM)

• Creador: Grupo de Investigación: Grupo de Inv. en Seguridad e Impacto Medioambiental de Vehículos y Transportes (GIVET)
• Centro o Instituto I+D+i: Instituto Universitario de Investigación del Automóvil (INSIA)
• Departamento: Matemáticas del Área Industrial