In this study, predictive controllers of the Generalized Predictive Controller (GPC) and MPCSS (Model Predictive Controller State Space). The main objective is to provide a comparative study of performance and dynamic stability between these control structures, when submitted to processes with distinct dynamic characteristics and in the presence of load disturbance at plant output and noise. The study presents the design of unconstrained GPC and MPCSS controllers, applied to monovariable systems, using the process model and the cost function incrementally. For the GPC controller design, a predictive model is used to predict the output of the process over a time horizon with the output being composed of measured signals of the past inputs-outputs and the future control signal. However, the MPCSS project is developed in the state space representation domain, with feedback of estimated states using a state observer using the Kalman Filter equations. The MPCSS controller has its inherited structure from the project model, where variables of states with physical behavior, enter into obtaining a law of control by feedback of estimated states. Numerical simulations are applied to evaluate the designs of the presented controllers.
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