Nonlinear system-identification of the filling phase of a wet-clutch system

The work presented illustrates how the choice of input perturbation signal and experimental design improves the derived model of a nonlinear system, in particular the dynamics of a wet-clutch system. The relationship between the applied input current signal and resulting output pressure in the filling phase of the clutch is established based on bandlimited periodic signals applied at different current operating points and signals approximating the desired filling current signal. A polynomial nonlinear state space model is estimated and validated over a range of measurements and yields better fits over a linear model, while the performance of either model depends on the perturbation signal used for model estimation.

► The filling region of a wet-clutch is modelled via a nonlinear state space model.
► The polynomial nonlinear state space outperforms a linear model.
► Choice of input signal leads to an improved model.
► Model is versatile and robust to be used in iterative learning control.