Implementation of a Fuzzy PID Controller Using Neural Network on the Magnetic Levitation System

Abstract

This paper presents the fuzzy PID (FPID) controller using neural network (NN) for controlling the magnetic levitation system. Magnetic levitation systems are open loop unstable, uncertainly and inherently nonlinear systems. Consequently, controlling this kind of the system is very difficulty. The FPID controller is developed to provide nonlinear or linear control action that can improve performance of the controller in comparison with a conventional PID controller using only linear policy. Unfortunately, since FPID controller are nonlinear, it is more difficult to set the controller gains compared the linear PID controller. In this paper we propose a neural network to assist the FPID controller. The NN is added in parallel with FPID controller. The NN is used to compensate for inadequate FPID parameters and for stabilize the magnetic levitation system. The uniqueness our method is when the parameters of FPID are incorrect, then the NN takes over the controller, otherwise the NN does not operate. Online training and fast computing of the NN has been designed for that purposes. Finally, the experiment results showed the effectiveness of the proposed method.