Adaptive Tuning of PID Controller using Crow Search Algorithm for DC motor

The DC motor has been commonly utilized in the industry although its maintenance is costly, more than the induction motor. Consequently, speed control of DC motor has attracted considerable researches and different algorithms have evolved. All the traditional algorithms for the Proportional Integral Derivative (PID) controller provide initial practical values for (kp, ki, and kd) PID parameters, which are manually tuned to achieving the desired performance. The manual tuning is inaccurate and a hard job, which requests comprehensive experience of the problem domain. in this paper we proposed a new method to adjustment of the PID parameters to improve tracking performance of DC motor, also provides optimal stability through creating a hybrid PID-CSA predictive model for tuning parameters of the PID controller of DC motors based on Crow search algorithm. The empirical results are compared with four type of proposed PID-CSA versions [ Integral Squared Error (ISE), Integral Absolute Error (IAE), Integral Time Absolute Error (ITAE), and Integral Time Squared Error (ITSE) ] that based on the type of error indicator functions and with other previously techniques including the Ziegler-Nichols method and PSO Optimization. The PID-CSA is more adapt in improving the steady-state error, the controller step response stability, overshoot, the rising time and settling time this give rise. The performance of the DC motor is not affected by these disturbances.