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Identification of dynamic parameters in low-mobility mechanical systems: application to short long arm vehicle suspension

The identification of dynamic parameters in low-mobility mechanical systems is addressed and applied to short long arm (SLA) front car suspension. The main goal of the identification technique is to obtain, from experimental measurements, the values of those dynamic parameters (masses, location of the centre of masses, terms of the inertial matrix of the links, constant friction terms and elastic and viscous damping constant terms) that affect the dynamic behaviour of the system. Moreover, additional but important information that could be obtained from the procedure is related to the weight of those terms inside the dynamics of the system, so that simplified dynamic models based on relevant and well-identified parameters can be established. First, a systematic procedure will be presented for obtaining the equations of motion in a linear form with regard to the dynamic parameters to be identified. The main drawbacks related to the identification of parameters in low-mobility mechanisms will be pointed out, mainly the difficulty of determining a natural cutoff point into the singular values of the observation matrix which allows us to determine the true dimension of the set of base parameters and of obtaining an observation matrix well conditioned from the numerical point of view that allows an identification in the presence of measurement error. The procedure proposed for overcoming these problems will be based on the development of symbolic relationships among the physical parameters in order to determine the true rank of the observation matrix and on the consideration of a reduced subset of the base parameters set. These relevant parameters will be selected according to their influence on the dynamic behaviour of the mechanical system. A virtual benchmark will be used for testing purposes. The dynamic models based on relevant parameters show a better adjustment than the complete ones, mainly when the level of noise in the measurements used in the identification process increases.