Open AccessComparison between butterfly optimization algorithm and particle swarm optimization for tuning cascade PID control system of PMDC motor
Author(s) -
kareem Ghazi Abdulhussein,
Naseer M. Yasin,
Ihsan Jabbar Hasan
Publication year - 2021
Publication title -
international journal of power electronics and drive systems/international journal of electrical and computer engineering
Language(s) - English
Resource type - Journals
eISSN - 2722-2578
pISSN - 2722-256X
DOI - 10.11591/ijpeds.v12.i2.pp736-744
Subject(s) - particle swarm optimization , pid controller , overshoot (microwave communication) , control theory (sociology) , cascade , computer science , position (finance) , matlab , algorithm , engineering , control engineering , artificial intelligence , control (management) , temperature control , telecommunications , finance , chemical engineering , economics , operating system
In this paper, two optimization methods are used to adjust the gain values for the cascade PID controller. These algorithms are the butterfly optimization algorithm (BOA), which is a modern method based on tracking the movement of butterflies to the scent of a fragrance to reach the best position and the second method is particle swarm optimization (PSO). The PID controllers in this system are used to control the position, velocity, and current of a permanent magnet DC motor (PMDC) with an accurate tracking trajectory to reach the desired position. The simulation results using the Matlab environment showed that the butterfly optimization algorithm is better than the particle swarming optimization (PSO) in terms of performance and overshoot or any deviation in tracking the path to reach the desired position. While an overshoot of 2.557% was observed when using the PSO algorithm, and a position deviation of 7.82 degrees was observed from the reference position.