In practice, model predictive control is commonly based on a dynamic black-box model. For linear systems, the model is frequently based on either a process system＇s impulse response or step response. For nonlinear cases, many works have used an artificial neural network (ANN). The quality of the data set used to construct the ANN model is a critical issue. In this work, we present a systematic approach for designing the data set based on information theory. Information entropy is derived to identify the mutual positions among data points in all feasible regions. In addition, information enthalpy is derived to obtain a system＇s dynamic nonlinearity. Hence, the placements of the new data are designed on the basis of a compromise between the information entropy and the information enthalpy-the information free energy. Also included herein are realistic examples such as pH control. Simulation results demonstrate that the proposed approach is highly promising in terms of obtaining a reliable black-box model, such as ANN, for model predictive control.