Application of Ant Colony Optimization Algorithm in Determining PID Parameters in AC Motor Control

Abstrak

Application of Ant Colony Optimization (ACO) Algorithm in determining PID (Proportional-Integral-Derivative) parameters to optimize AC motor control through simulation using MATLAB. AC motors are a critical component in a wide range of industrial applications requiring efficient control to ensure optimal stability and response. This research focuses on optimizing the motor's RPM control by fine-tuning PID parameters using the ACO algorithm. Precise RPM control is crucial for maintaining performance in dynamic industrial environments. The ACO algorithm is used to optimize the PID parameter by referring to the objective function of Integral Time Absolute Error (ITAE). The optimization results show that this algorithm can achieve optimal convergence in the 33rd iteration with a fitness value of 6269. The optimal PID parameters obtained were Kp of 164.98, Ki of 23.47, and Kd of 10.51. The simulation of the AC motor control system shows a significant improvement in performance compared to the Trial-and-Error method. The simulation results demonstrate that ACO reduces steady-state errors by up to 9%, while Trial-and-Error reaches 25%. The settling time is also faster with ACO, which is 0.7 seconds, compared to the Trial-and-Error method which takes longer. The use of the ACO method in PID tuning has been proven to be more efficient and accurate than conventional approaches, thus improving the RPM stability and response of the AC motor control system. This study concludes that the integration between ACO and PID can be the optimal solution in automated control applications in industries that require responsive and stable motor RPM control.