In this paper we develop a new model reference control architecture for uncertain dynamical systems that can effectively suppress the system uncertainties and achieve a guaranteed system performance. The proposed approach neither resorts to nonlinear adaptive control laws nor relies on excessive modeling information as often done in traditional robust control frameworks. It only requires a parameterization of the system uncertainty given by unknown weights with known conservative bounds in order to stabilize the uncertain dynamical system. The proposed methodology is based on a recently developed command governor theory that minimizes the effect of system uncertainty and shapes the system's input through feedback in order to improve overall system performance. Specifically, we show the controlled uncertain dynamical system approximates a given ideal reference system by properly choosing the design parameter of the command governor. Unlike model reference adaptive control approaches, the proposed model reference controller preserves linearity of the controlled uncertain dynamical system since its control laws are linear, and hence, the closed-loop performance is predictable for different command spectrums. A numerical example is provided to illustrate the effectiveness of the proposed architecture.