Automatic Tuning of the PID Controller Based on the Artificial Gorilla Troops Optimizer

Abstract

Tuning a PID controller is a crucial task in control engineering to achieve optimal performance of the system. However, manual tuning of PID parameters can be inaccurate and difficult without extensive experience. One approach to tuning the PID controller is by utilizing heuristic algorithms. These algorithms are based on natural phenomena and can efficiently search for the optimal set of PID parameters. Therefore, utilizing met heuristic algorithms for tuning PID controllers can significantly improve the system's performance and reduce costs associated with manual tuning. In this paper, a new method for tuning parameters of the PID controller of DC motors using a hybrid adaptive PID_GTO predictive model based on the artificial gorilla troop optimizer algorithm (GTO) is proposed. The empirical results are compared based on four types of error indicator functions: integral time squared error (ITSE), integral time absolute error (ITAE), integral absolute error (IAE), and integral squared error (ISE), as well as with other previously published techniques in the literature, such as the Ziegler-Nichols and PSO Optimizer algorithms. The empirical results show that this method outperforms other techniques in improving steady-state error, stability, overshoot, rising time, and settling time of the DC motor.