Model Predictive Speed Control of DC-DC Buck Converter Driven DC-motor with Various Load Torque Values

Abstract

This paper presents a speed control of a buck converter driven a direct current (DC) motor. The speed control is designed and programmed to compute a constrained model predictive controller (MPC) algorithm that regulates a reliably constant and accurate speed in the presence of different value of load torque. The challenge of regulating speed under variable load torques is solved by using the predicted discrete averaged state space model and the predicted constrained input control signal. The proposed strategy uses the output feedback signal to the MPC to first predict the state vector and the input control signal that are fed to the DC-DC buck converter to adjust the required output voltage and current, then fed to the permanent magnet DC-motor. Since the DC-motor output speed signal varies, a constrained input control signal applied to the DC-DC converter to keep the control signal limited between boundaries of 0-1. This process is fulfilled by computing the quadratic programming (QP) optimization algorithm at each prediction horizon interval. MATLAB and Simulink were used to obtain the simulated results and output figures.