Abstract
The feasibility of a neural network method was discussed in terms of a self-tuning proportional–integral–derivative (PID) controller. The proposed method was configured with two neural networks to dramatically reduce the number of tuning attempts with a practically achievable small amount of data acquisition. The first network identified the target system from response data, previous PID parameters, and response characteristics. The second network recommended PID parameters based on the results of the first network. The results showed that it could recommend PID parameters within 2 s of observing responses. When the number of trained data was as low as 1000, the performance efficiency of these methods was 92.9%, and the tuning was completed in an average of 2.94 attempts. Additionally, the robustness of these methods was determined by considering a system with noise or a situation when the target position was modified. These methods are also applicable for traditional PID controllers, thus enabling conservative industries to continue using PID controllers.
Highlights
The rapid development of process industries has resulted in an increase in the number of factories and machines, making it difficult for humans to monitor and operate all the machines during operation
The results indicated that the PID parameter tuning was completed in less than 1.6 attempts in every method, because these methods generated the acceptable PID parameters of the system as outputs after identifying the system from the response
According to the learning method classified by the type of network, number of sampling data, and whether response characteristics were included as inputs, the long short-term memory (LSTM)
Summary
The rapid development of process industries has resulted in an increase in the number of factories and machines, making it difficult for humans to monitor and operate all the machines during operation. 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 [1–3]. 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. If the system changes owing to deterioration or environmental changes, the PID parameters must be changed dynamically to obtain a good response
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