Design A RBF Neural Network Auto-tuning Controller for Magnetic Levitation System with Kalman Filter

In this paper, a new controller structure called the radius basis function (RBF) neural network auto-tuning PID controller with Kalman filter is presented to manipulate a linearized magnetic levitation system. The proposed RBF neural network auto-tuning PID controller with Kalman filter makes use of Kalman filter to deal with the uncertainties and noises induced by the process of linearization of magnetic levitation system as well as the noise problems induced by the position feedback sensor device. To validate the proposed new design structure, the MATLAB simulations under different types of noise problems are presented. Furthermore, results confirm that the output transient response and steady-state error of magnetic levitation system by the proposed controller with Kalman filter can be improved and assured while the results of auto-tuning PID controller are inclined to be unstable.