Design of a Self-Tuning PID Control System by Neural Networks

Abstract

In Japan, about 84 percent of real plants adopts PID controllers. However, when we use the PID controllers, it generally needs much effort and time to tune PID gains. In this paper, we propose a method to tune the PID gains by using three-layered neural networks. Taking into consideration that the PID gains are non-negative real number, we select functions whose derivatives are sigmoid as output functions in the output layer. To find system's Jacobian, we identify the unknown plant by using a neural network as an emulator. Finally, numerical results are illustrated to show effectiveness of the present method through simulations.