For a fast and precise control of mechatronic systems usually non-linear PID controllers are used. The more nonlinearities a system contains, the more complex the control task becomes. A more advanced control concept is Model Predictive Control (MPC), which resolves a number of disadvantages of the classic control concepts. These include the explicit adherence to state and input constraints and the intuitive controller design. The MPC also involves solving an optimal control problem that is computationally expensive. Since the solving must be done between two sampling steps, processes with high dynamics can push the process to its limits. One solution is Model Predictive Trajectory Set Control (MPTSC), which approximates MPC. This approximation reduces the computational effort significantly, without losing the model predictive character. Essentially, MPTSC is based on the discretization of the input domain and can thereby forego gradient-based optimization algorithms in particular. Therefore, any cost terms and prediction models can be used. Adaptation of the discretization ensures a high approximation quality of the classical MPC.