Neural Adaptive Controller for Magnetic levitation System

In this study a Neural Adaptive method is used for position control and identification of a Magnetic levitation system. This controller consists of three parts: PID controller, radial basis function (RBF) network controller and radial basis function (RBF) network identifier. The combination of controllers produces a stable system which adapts to optimize performance. It is shown that this technique can be successfully used to stabilize any chosen operating point of the system. All derived results are validated by computer simulation of a nonlinear mathematical model of the system.