Optimized PID-Like Neural Network Controller for Single-Objective Systems

Abstract

The utilization of intelligent controllers becomes more prevalent as the hype of Industry 4.0 arises. Artificial neural network (ANN) exhibits the mapping ability and can estimate the output by means of either interpolation or extrapolation. These properties are sought to supersede the classical controllers. In this study, the ANN establishment was initiated by collecting dataset from the input and output of a well-known PID controller. The dataset was trained using a set of control factor combinations, including the number of neurons, the number of hidden layers, activation functions, and learning rates. Two kinds of ANN controllers were investigated, including one-input and three-input ANN. The testing was conducted under normal and uncertain conditions. These uncertainties include external disturbances, plant variations, and setpoint variations. The integral absolute error (IAE) was selected as the single objective to assess. The simulation results show that the response of three-input ANN controllers could yield smaller IAE at their best combinations under most kinds of conditions. Besides, the three-input ANN outperforms the one-input ANN both qualitatively and quantitatively. These facts might lead to a broader utilization of ANN as controllers.