DC motor demand is rising in the industrial sector due to its efficiency and
in contrast to AC motors, a DC motor's momentum can be easily adjusted. For
industrial uses, making a highly regulated motor is essential. DC motors need to have
excellent speed tracing and load regulation in order to operate satisfactorily. The speed
of a DC motor was controlled in this work using proportional integral derivative (PID)
controllers. This study used MATLAB to determine how a Proportional-IntegralDerivative (PID) controller affected the performance of a DC motor of the industrial
type by selection of PID controller parameters using Zeigler’s Nichols (ZN), Genetic
Algorithm (GA), and Fuzzy Inference System. Nonlinearities and model uncertainties
must be included in the control design in order to provide effective and efficient
control. The higher-order systems could use the suggested strategies. The PID
controller's primary function is to regulate motor speed based on incoming system data
and auto-tuning. The findings of the simulation also demonstrate improved motor
performance, which decreases rise time, steady state error, and overshoot, and increases
system stability.
Keywords: DC motor, Fuzzy inference system, Genetic algorithm, PID, ZN method.
