DC Motor Position Control with Model Predictive Control

Abstract

In this study, model predictive controller design that is one of the modern control methods for the position control of permanent magnet brushed direct current motor system, was carried out and the proposed controller design was verified in real-time with different tests. The system model was obtained by using the black box model approach, one of the system identification methods. The model predictive controller design has been developed based on the system model obtained with the experimental data sets. The controller was implemented in real-time using the Matlab-supported Waijung block set and the STM32F4 development kit. The controller performance has been tested under different reference inputs and parameter variations. The system output has successfully followed all reference inputs and maintained its success despite changing system parameters, confirming the proposed controller design. In addition, the results were compared with the PID controller, which is one of the classical control approaches. Model predictive controller advantages over PID controller are demonstrated by experimental results.