DefinePK

Abstract

A new nonlinear self-tuning parameters algorithm for two types of the PID controllers is designed, the first type is traditional PID controller and the second is nonlinear PID controller, with intelligent algorithm for nonlinear magnetic levitation system (MagLev) is presented in this study. The proposed scheme of the on-line self-tuning control algorithm is based on neural network and PSO algorithm to make both controllers are an on-line adaptive PID controllers by calculating the optimal nonlinear values of the PID parameters in order to generate the best or near optimal value of the control action that will guarantee the output of the actual model accurately represents the desired position output of the magnetic ball. From numerical simulation results, the nonlinear adaptive PID controller is the best from the traditional adaptive PID controller with the proposed nonlinear self-tuning parameters algorithm in terms of fast on-line learning and tuning the nonlinear parameters of the controller with best voltage control action that generated to precisely track the motion of magnetic ball and reach to the desired position with convergence of the position error to zero value.