ILC-PIV Design for Improved Trajectory Tracking of Magnetic Levitation System

Abstract

This paper puts forward the hybrid control algorithm, which integrates the iterative learning control (ILC) scheme with proportional integral velocity (PIV) control, for improved trajectory tracking of magnetic levitation system. ILC is a type of model-free controller, which is used for systems that perform repetitive tasks. Adjusting the control inputs based on the error information obtained during previous iterations, ILC tries to enhance the transient response of the closed-loop system. One of the striking features of ILC is that even without the full dynamic model of the plant, it can yield perfect trajectory tracking by learning the plant dynamics through iterations. Adopting this learning control feature of ILC, this paper aims to synthesize ILC with PIV for both improved tracking and better robustness compared to conventional PIV. The efficacy of the proposed ILC-PIV controller framework is assessed through a simulation study on the magnetic levitation plant for reference following application.