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, general-purpose 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.
Vehicle Suspension Identification Via Algorithmic Computation of State and Design Sensitivities
Contributed by the Design Automation Committee of ASME for publication in the JOURNAL OF MECHANICAL DESIGN. Manuscript received July 6, 2014; final manuscript received October 20, 2014; published online December 8, 2014. Assoc. Editor: Ettore Pennestri.
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Callejo, A., and de Jalón, J. G. (February 1, 2015). "Vehicle Suspension Identification Via Algorithmic Computation of State and Design Sensitivities." ASME. J. Mech. Des. February 2015; 137(2): 021403. https://doi.org/10.1115/1.4029027
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