Controllers based on linear matrix inequalities (LMI) and model predictive control (MPC) both use optimization methods; there are however significant differences between them. In case of LMI controllers, optimization is carried out during controller synthesis, because LMI’s are an optimization tool that requires a linear programming problem being solved. With MPC controllers, however, optimization methods are not used as much in controller synthesis as in controller algorithm operation, to determine optimal control signal values based on the found minimum of the criteria function. A square function is used with boundaries from above and below, which requires a square programming problem, with boundaries for decision variables, being solved. In this paper controller synthesis methods using LMI and MPC are shown, with a focus on the steps that need to be performed, and a comparison of both methods.