Iterative Learning Control Based Fractional Order PIDController for Magnetic Levitation System

Abstract

Maglev (Magnetic Levitation) systems are an interesting class of systems since they work without anyphysical contact and are hence frictionless. Due to this attractive property, such systems have thepotential for wide range of applications such as maglev trains. Maglev is non-linear due to magnetic fieldand unstable that suggest the need of stabilizing controller. An appropriate controller is required tolevitate the object at desired position. FOPID (Fractional Order Proportional Integral Derivative) controllerand ILC (Iterative learning Control) based FOPID controller are designed in this paper for the levitationof metallic ball with desired reference at minimum transient errors. Since maglev is unstable and ILC isused only for stable systems, FOPID controller is used to stabilize the plant. Non-linear interior pointoptimization method is used to obtain the parameters of FOPID controller. An ILC is used as a feedforwardcontroller in order to improve the response iteratively. P, PD and PID-ILC control laws are used to updatethe new control input in ILC based FOPID controller. The overall control scheme is therefore a hybridcombination of ILC and FOPID. The effectiveness of proposed technique is analyzed based on performanceindices via simulation. ISE (Integral Square Error) and IAE (Integral Absolute Error) is lesser in case ofhybrid PID-ILC as compared to simple FOPID controller.