Auto Tuning of PID Controller using Particle Swarm Optimization Method for DC Motor Speed Control

Abstract

The proportional-integral-derivative (PID) controllers are the most popular controllers used in industry because of their remarkable effectiveness, simplicity of implementation and broad applicability. However, tuning of these controllers is time consuming, not easy and generally lead to poor performance especially with non-linear systems. This research presents an artificial intelligence (AI) method of particle swarm optimization (PSO) algorithm for tuning the optimal (PID) controller parameters for DC motor speed control (DCMSC) system to achieve the mean objective which is the tracking between the reference speed and the output speed. This approach has superior features, including easy implementation, stable convergence characteristic and good computational efficiency over the conventional methods. The PID conventional controller had been applied and results were compared with the automatic tuning PSO-PID for (DCMSC) using Simulink of MATLAB. The DC Motor Scheduling PID-PSO controller is modeled in MATLAB environment. Simulation results for the proposed method give optimum input/output tracking and the error approximately equal zero without using the conventional solutions for DC motor speed control