Parameter Estimation and Speed Control of Real DC Motor with Low Resolution Encoder

In this study, parameter estimation and speed control of a real brushed Direct Current (DC) motor are conducted. For parameter estimation, a popular iterative optimization algorithm, namely Particle Swarm Optimization (PSO), is used. The motor is assumed to have first-order dynamics, which are a reasonable assumption for most DC motors in practical use and two parameters representing the mathematical model of the model are estimated. The step response of the open-loop system is used as data for the parameter estimation algorithm. Using the estimated parameters simulations are conducted for closed loop Proportional-Integral (PI) control. Experiments on a real brushed DC motor are also conducted for closed-loop PI control of rotor speed. Speed of motor is measured using single channel low resolution optical encoder. Due to low resolution of speed sensor, precision of the whole system is limited and oscillations are observed in speed output measured. Controlling low-resolution sensor systems is important for two reasons. First, cost of the system is quite low compared with high resolution sensors. Second, for some real systems, especially with small physical dimensions, high resolution sensors may not be available at all. So, one of the important aspects of this study is to analyze low resolution sensor systems. As a closed loop controller, simple but popular PI controller is chosen to show the compatibility of experimental results with simulations. Oscillations around reference value are less than 10% in both simulations and experiment results. In real experiments, oscillations are slightly higher due to low resolution of encoder which is expected due to quantization error. Several parameters are used for controller and results are reported and discussed in corresponding sections.