Augmented Fuzzy-ILC for Trajectory Tracking Control of Magnetic Levitation System

Magnetic levitation, which is also known as maglev, is a technology that uses a magnetic field to levitate an object and keep it suspended in mid-air. Maglev systems are unstable and nonlinear in an open loop, a controller is required to tackle the non-linearity present in the system. There are several controllers like PID (Proportional-Integral-Derivative), and PIV (Proportional plus Integral plus Velocity) have been proposed. ILC (Iterative Learning control) is a scheme that works to enhance the outcomes through information-based dynamics learning, whereas at every iteration, the PID controller generates the same tracking error and adjusts control signal according to the time index. ILC augmented with PID Controller gives better tracking control. However, the tracking performance can be improved further by combining Fuzzy with ILC. Improved robustness and trajectory tracking of the maglev system can be attained by augmenting Fuzzy with ILC.