Modelling of an Optimal Fuzzy Logic BasedDCMotor Speed Controller Using Particle Swarm Optimization

This research presents an application of particle swarm optimization- fuzzy logic control (PSO-FLC) for direct current motor speed. The mathematical model that describes the dynamic behaviour of the DC motor was developed by using Simulink, fuzzy set Guassian membership function, Mamdani fuzzy inference system (FIS) and centroid method of defuzification to optimize the fuzzy membership functions. The effectiveness of the control strategy was validated on Matlab/Simuink environment. For a normal load of 5000N, FLC attained a speed of 1432rpm with a percentage error of 0.49 while that of PSO-FLC 1434 had rpm with a percentage error of 0.14. As incremental load of 10%, 20% and 30% respectively was applied in succession, and the maximum speed attained by FLC kept on decreasing with a high percentage error while that of PSO was able to track the speed with minimal error. The results demonstrated that the designed PSO-FLC speed controller is more effective for good dynamic behavior monitoring of the DC motor by providing better tracking and performance than fuzzy logic controller.