Reference system model predictive control (RS-MPC) aims to fill a void in predictive control technology that is caused mainly by the lack of intuitiveness of tuning of currently popular MPC schemes. It addresses the two main drawbacks of weight-based approaches: (1) the absence of clear easy-to-follow guidelines for the selection of such weights to obtain the desired closed-loop response; (2) the lack of a uniform closed-loop response over a wide operating range without the need to retune the controller. Because the determination of tuning parameter values is based on a specificiation of the desired closed-loop response, RS-MPC offers significant advantages, particularly in the context of nonlinear systems. Presented herein is the development of the RS-MPC algorithm as well as some representative example cases demonstrating its tuning advantages. It is shown herein that RS-MPC consistently delivers the asked performance without needing retuning at different operating conditions. This is in sharp constrast to weight-based techniques which we show often fail to provide a uniform closed-loop response as the underlying open-loop dynamics change due to the nonlinear character of a process.