Torque estimation in closed-loop position control with DC motors

Design and control with efficiency and performance are one of the most goals to achieve in the robotics field, cranes or aerospace automation. The stability of the system is essential and the non-linear effects create by motor reduce stability, like maintain speed or position at set points. The position control of DC motor allows it to move to a precise position and remain there even if an external force tries to move it. Different techniques have been developed to perform the position control of DC Motor, the most used are Proportional-Integral-Derivative(PID), Fuzzy Logical Model(FLM) and Artificial Neural Network(ANN). This thesis discusses the implementation and tuning steps of the Proportional Integral Derivative (PID) controller for the position control of Brushed DC Motor. By analyzing three principal feedback (Rotation, Speed and Current), using STMNucleo- G431RB microcontroller and monitoring the data in a GUI-Matlab, specially designed for this purpose based on UART communication protocol. Since the system is stable, the performance of the system is analyzed and validation is done in terms of robustness, time response and percentage of error. In conclusion, Torque estimation has been carried out through a load cell, in order to realize a function Torque-Current and monitoring how much Torque DC Motor is producing at any given moment.