Particle Swarm Optimization and Genetic Algorithm PID for DC motor position controllers

Due to the increasing number of applications, researchers have developed several methodologies to control Direct Current (DC) motor. To decide for an optimal position control, it is fundamental to analyse the performance of the system in terms of rise time (t
_r
), settling time (t
_s
), overshoot percentage (M
_p
%), torque load rejection, and steady state error (E
_ss
). Proportional-Integral-Derivative (PID) controller offers a stable response by tuning their coefficients u nder a s pecific me thodology. In this paper, we propose a comparative study of PID controller tuned by Genetic Algorithm (GA) and Particle Swarm Optimization (PSO). The former is an optimizer that simulates the natural selection of species and is used to tune the PID gains. The latter is an algorithm that follows the social behavior of bird flocking or fish and is implemented to tune the PID coefficients. The simulations suggest that the use of PSO results in a low overshoot percentage and better system response for torque load disturbance.