Offline Auto-Tuning of a PID Controller Using Extended Classifier System (XCS) Algorithm

Proportional + Integral + Derivative (PID) controllers are widely used in engineering applications such that more than half of the industrial controllers are PID controllers. There are many methods for tuning the PID parameters in the literature. In this paper an intelligent technique based on eXtended Classifier System (XCS) is presented to tune the PID controller parameters. The PID controller with the gains obtained by the proposed method can robustly control nonlinear multiple-input–multiple-output (MIMO) plants in any form, such as robot dynamics and so on. The performance of this method is evaluated with Integral Squared Error (ISE) criteria which is one of the most popular optimizing methods for the PID controller parameters. Both methods are used to control the ball position in a magnetic levitation (MagLev) system and the performance of controllers are compared. Matlab Simulink has been used to test, analyze and compare the performance of the two optimization methods in simulations.

Keywords:

Artificial Intelligence , Extended classifier system , Magnetic levitation , PID controller tuning