Artificial Intelligence Techniques based PID Controller for Speed Control of DC Motor

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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.