Performance Improvement of Discrete Time PID Controller using Neural Networks

抄録

This paper demonstrates that Neural Networks can be used effectively to compensate for incorrectly selecting gains of the PID controller. Selecting appropriate the gains of the PID controller is difficult and is time consuming. If the gains of the PID controller are not suitable, then the performance of the PID becomes poor. Therefore, selecting the PID gains is very important. In this paper, the Neural Network (NN) is used as a ""plug and play"" module for the PID controller. When the PID gains are incorrect, the NN take over the controller, otherwise the NN doesn't operate. A simple structure, fast computing, and on-line training NN has been designed for that purpose. We applied the proposed controller to the magnetic levitation test bed and the experimental results showed that using the proposed NN in the PID controller can improve the performance of the PID controller.