Genetic algorithm and fuzzy self-tuning PID for DC motor position controllers

Abstract

The development of several strategies to control Direct Current (DC) motors have been facilitated by the improvement of the technology. To decide an optimal controller for position control in DC motors, it is fundamental to study the behavior of the system by looking at some parameters such as steady-state error, overshoot percentage, torque load rejection, rise and settling time. Proportional, integrative and derivative controller (PID) provides an easy tuning of its gains and offers a stable output. Fuzzy Logic Controller (FLC) imitates the human knowledge by replacing mathematical calculation for linguistic ideology. This controller provides suitable responses, but they are not ideal due to each of them suffer from slow response or torque load increment. On one hand, Self-tuning controller has been developed by scientist as a system that links PID and FLC. Self-tuning controller works by setting the FLC as a master meanwhile the PID gains can be set applying a linguistic interpretation of the behavior of the variables. On the other hand, Genetic Algorithm (GA) is a robust optimizer that emulates the natural selection and is applied for tuning PID controller coefficients to guarantee optimal performance. This research provides a comparative examination between GA PID and Fuzzy self-tuning controllers by looking at the above-mentioned variables to present the benefits and drawbacks of each controllers.