Model Predictive Control and Genetic Algorithm PID for DC Motor position

Direct current (DC) drivers have been widely implemented in a variety of systems due to their simple and affordable configuration and capability for variable speed control. DC drivers position control are modeled as third order systems, which demand considerable effort in terms of control action. More recent attention has focused on the development of controllers based on artificial intelligence. However, the computational resource is significant. To determine the effectiveness of a specific controller, it is necessary to examine the parameters of rising time (tr), settling time (ts), overshoot percentage (M p %) and steady state error (E SS ). Thus, the main contribution in this paper is to provide a comprehensive study of two strategies, Model Predictive Control (MPC) and Genetic Algorithm (GA). MPC applies a set of rules to the model in order to forecast the upcoming behavior of a system across a defined horizon. The latter is a solver that imitates the natural evolution of Darwins law to tune PID controller. MPC provides a suitable response in terms of toque load disturbance and overshoot percentage.