  The present study proposes a simple grey-box identification approach to model
a real DC-DC buck converter operating in continuous conduction mode. The
problem associated with the information void in the observed dynamical data,
which is often obtained over a relatively narrow input range, is alleviated by
exploiting the known static behavior of buck converter as a priori knowledge. A
simple method is developed based on the concept of term clusters to determine
the static response of the candidate models. The error in the static behavior
is then directly embedded into the multi-objective framework for structure
selection. In essence, the proposed approach casts grey-box identification
problem into a multi-objective framework to balance bias-variance dilemma of
model building while explicitly integrating a priori knowledge into the
structure selection process. The results of the investigation, considering the
case of practical buck converter, demonstrate that it is possible to identify
parsimonious models which can capture both the dynamic and static behavior of
the system over a wide input range.
