Tuning of PID Controller Coefficient by Genetic Algorithm Using Runge-Kutta Method

This paper introduces an innovative approach for adjusting the coefficients of a Proportional-Integral-Derivative (PID) controller within a second-order control system. Although PID controllers are extensively utilized in engineering and industrial settings, conventional tuning techniques frequently face challenges in attaining the optimal balance between stability, rapid response, and error reduction. To address these challenges, a genetic algorithm (GA) was utilized to enhance the proportional, integral, and derivative gains by minimizing the total output error of the system. The system was represented in state space form and examined using the fourth-order Runge-Kutta numerical method, which guarantees precise dynamic assessment. Throughout the optimization phase, the state variable matrices were modified to keep the Mean Absolute Error (MAE) below 1%. The refined controller underwent testing with a unit step input, and the system’s performance was evaluated based on settling time, steady state error, and transient response. Simulation findings show that the PID controller optimized by GA markedly improves system performance in comparison to traditional tuning methods. The suggested approach results in decreased settling time, minimized error, and enhanced stability, showcasing its strength and flexibility for second-order systems. These findings validate that evolutionary algorithms present an effective and dependable strategy for sophisticated PID tuning, delivering superior control performance for real-world applications.