## 1 Introduction  
The rapid development of process industries has resulted in an increase in the number of factories and machines, thus making it difficult for humans to monitor and operate all the machines during operation. Therefore, it is essential to develop technologies that automatically control the values of parameters such as pressure, velocity, temperature, and flow. The most widely used controller is the proportional-integral-derivative (PID) controller because it is effective despite its simplicity. It controls the output by calculating the error between the response and the target value [1, 2, 3]. In PID control, the response characteristics of the system vary according to the magnitude of the proportional, integral, and derivative terms, called PID parameters. When optimal values of PID parameters for the control object are set, the system shows a response close to the target value. However, if the system changes owing to deterioration or environmental changes, the PID parameters must also be changed dynamically to obtain a good response. In general, to set an appropriate PID parameter, an expert familiar with the system adjusts the parameter using a trial-and-error method and simultaneously checks the response of the system to select the optimal value of the parameter. Experts commonly use methods such as the Ziegler-Nichols step response method, Chien-Hrones-Reswick method, ITAE, and robust PID tuning [4, 5, 6, 7, 8, 9, 10, 11]. However, a major drawback of these methods is that the new PID parameters must be tuned manually whenever the characteristics of the system change; hence, complete automation of the tuning process is a challenge. Despite the development of relevant software and hardware to tune the parameters, the process is time consuming [12, 13, 14, 15, 16]. Therefore, for complete automation, it is necessary to set the PID parameters quickly even without a human. Methods have been developed for auto-tuning, such as a fuzzy logic controller. The fuzzy PID controller is a combination of classical PID control and fuzzy logic based on human knowledge and expertise [17]. It has been successfully applied in many nonlinear systems [18; 19; 20]. However, problems with fuzzy logic include the fact that the accuracy of responses depends on the knowledge and expertise of human beings, fuzzy rules must be updated with time, and there is no standard procedure for the design of the fuzzy controller. To compensate for the fuzzy PID controller, an adaptive neuro-fuzzy inference system (ANFIS) has been proposed. ANFIS is a combination of a neural network and a fuzzy logic controller [21; 22; 23]. ANFIS can successfully control a system by finding the values for fuzzy logic through a neural network without expert knowledge. Active disturbance rejection control (ADRC) is also a robust controller with the cancellation of disturbances [24; 25; 26]. However, ANFIS and ADRC cannot be applied to conservative industries that prefer the traditional PID controller with a PID parameter for safety because ANFIS control systems operate without PID parameters. Artificial intelligence (AI) is the technology used to automate PID control. The research on PID control using AI has been conducted in two ways. First, to replace the PID controller with AI [27; 28; 29] and second, to automate the PID parameter setting [30; 31; 32; 33; 34]. The advantage of replacing the PID controller with AI is that the tuning of PID parameters is automated. However, certain industries still prefer the traditional approach to tune the PID controller, especially in conservative systems where safety is essential. Therefore, the automation of PID parameter settings is indispensable for complete system automation.