Optimal experiment design for the black box identification of a semi-active damper

Abstract

This paper discusses the design of an optimal excitation for the experimental identification of a continuously variable, semi-active damper of a passenger car. The applied excitations are multisine signals of which the phases are optimized to obtain a uniform coverage of the achievable working range. Based on the obtained experimental data, a neural network based output error model of the damper is identified using a state of the art iterative procedure that includes the automated model structure selection and the parameter estimation. It will be shown that models identified using the data from the optimized experiments are considerably more accurate than those identified using conventional random phase multisine excitations.