Use of a priori information in the identification of global nonlinear models-a case study using a buck converter

This paper investigates some issues related to grey-box identification. In particular it is discussed how a priori information about the system static nonlinearity can be incorporated into the model. Also, it is shown that in some applications involving nonlinear systems, there must be a tradeoff between the accuracy of the estimated model static nonlinearity and the quality of forecasts. These concepts are applied in the identification of a real DC-DC buck converter operating in continuous mode. Improvement in model global stability was achieved by using simple a priori information that takes into account the steady-state voltage relation of the converter. Models that are valid over a wide operating range are included. Such models are compact and can be estimated directly from data obtained from the converter