Performance Analysis of a Predictive Controller Using Recursive and Non-Recursive Models Applied to a Speed System

In recent years, control techniques are in continuous improvement, in order to fulfill the performance requirements and guarantee the robustness and stability of the systems. Among the predictive controllers, the Generalized Predictive Control (GPC) controller stands out, which uses a process model to estimate the future response, aiming to determine the control signal. In this work, the goal was to verify the performance of this type of controller when applied in a speed control system, using both the Recursive Least Squares Method (RLSM) and Non-Recursive Least Squares Method (NRLSM) estimators to compute the coefficients of Controlled Auto-Regressive Integrated Moving Average model (CARIMA) used in GPC. Finally, this study analysed the performance of the GPC controller and compared to the classic Proportional Integral Derivative (PID) and all controllers had a satisfactory performance in relation to tracking references without overshoot, with emphasis on the GPC-RLSM, which had lower rise time than others.