Identification of a nonlinear spring and damper characteristics of a motorcycle suspension using test ride data

During test rides of motorcycles modifications are made to the suspension. In order to quantify those changes, the nonlinear spring and damper characteristics must be determined. This is usually done on a test bench. However, measurements on a test bench are closely related to high costs and high time exposure. Hence, a parameter identification after a test run, formulated as an optimization task, seems to be an auspicious approach. For this purpose a cost function is defined, which is minimized by considering the dynamics of the system. The strength of the contribution is the efficient gradient computation using the adjoint variable approach. In order to approximate the nonlinear spring and damper characteristics cubic splines are used. The values of the spline functions at specified grid points (knots) are adjusted such that the deviation between simulation and measurement is minimal.